This document provides an overview of the key topics and activities covered in Week 11 of the FINM4100 course. It discusses evaluating ethical considerations regarding fintech and analytics in finance. Case studies and potential solutions to ethical issues are investigated. Applications of analytics in areas like risk management, fraud detection, and personalized services are reviewed. The document also explores governance and accountability, bias in AI systems, privacy concerns, and how to make technologies like open banking more ethical.
Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
This document provides an introduction to an information technology course. It begins with defining key terms like information technology and information systems. Next, it explains why the course is important by discussing how IT affects companies and the growing number of IT jobs. The course syllabus is then outlined, covering topics like e-business, infrastructure, data storage, and project management. Finally, there is a discussion of lecturing, including using Alibaba as an example and discussing core elements of the information age like data, technology, and people. Strategies for gaining competitive advantage through initiatives like business process reengineering, customer relationship management, and enterprise resource planning are also summarized. The document concludes with discussing how to measure the success of strategic initiatives using metrics
The document discusses ethical and social issues related to information systems in business. It covers topics like ethics in information system design and use, identifying and addressing ethical issues, examples of organizations violating ethics, and the relationship between society, information systems, and business. The document also provides examples of how businesses can address ethical concerns through codes of conduct, clear policies, transparency, and decision-making frameworks. Additionally, it discusses social issues such as privacy, responsibility, isolation, and how businesses can contribute to sustainability and social causes through responsible use of information systems.
Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
This document provides an introduction to an information technology course. It begins with defining key terms like information technology and information systems. Next, it explains why the course is important by discussing how IT affects companies and the growing number of IT jobs. The course syllabus is then outlined, covering topics like e-business, infrastructure, data storage, and project management. Finally, there is a discussion of lecturing, including using Alibaba as an example and discussing core elements of the information age like data, technology, and people. Strategies for gaining competitive advantage through initiatives like business process reengineering, customer relationship management, and enterprise resource planning are also summarized. The document concludes with discussing how to measure the success of strategic initiatives using metrics
The document discusses ethical and social issues related to information systems in business. It covers topics like ethics in information system design and use, identifying and addressing ethical issues, examples of organizations violating ethics, and the relationship between society, information systems, and business. The document also provides examples of how businesses can address ethical concerns through codes of conduct, clear policies, transparency, and decision-making frameworks. Additionally, it discusses social issues such as privacy, responsibility, isolation, and how businesses can contribute to sustainability and social causes through responsible use of information systems.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
The document provides an overview of understanding big data and data analytics, including business intelligence, analytics, and visualization. It discusses the evolution of business intelligence and analytics, from descriptive analytics describing what has occurred to predictive analytics predicting what will occur and prescriptive analytics determining what should occur. It also covers topics like data mining, market basket analysis, cluster analysis, and the importance of visualization for extracting insights from data.
From time-to-time internal auditors are faced with situations which call for them to make an ethical decision. In addition, they may, in the middle of auditing, come across circumstances which themselves appear to be violations of a corporate
code-of-conduct.
Several laws now specifically state that internal auditors, in terms of the act, will be bound by the IIA Code of Ethics.
This webinar explores the IIA Code of Ethics as it applies to everyday situations the auditor may encounter.
The module is designed to provide the participants with an in-depth knowledge of:
Ethics theory
The IIA Code of Ethics
Applicable areas within Internal Audit
Reporting of material facts
Corporate Codes of Conduct
Auditing Corporate Ethics
Webinar contents will include:
Classes of Ethics
The role of business
Employee ethics
Honesty, Objectivity and diligence
Conflicts of Interest
Reporting of Material Facts
Corporate Codes of Conduct
Corporate Social Responsibility
This document discusses data ethics and provides 5 key principles of data ethics for business professionals:
1) Ownership - individuals own their personal data and must provide consent for it to be collected
2) Transparency - individuals have a right to know how their data will be collected, stored, and used
3) Privacy - personal data must be securely stored and protected from unauthorized access
4) Intention - the intention behind collecting data must be considered to avoid potential harm
5) Outcomes - while intentions may be good, data analysis could inadvertently cause disparate impacts
Upholding data ethics helps businesses earn customer trust, which is essential to their success. Failure to do so can damage reputations and result
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1. A marketing information system consists of people, equipment, and procedures to gather, sort, analyze, evaluate, and distribute timely and accurate information to marketing decision makers.
2. Some benefits of a marketing information system include enabling information sharing, collaborating with customers, and predicting consumer trends through sales analysis.
3. Applications of a marketing information system to marketing research include digital surveys, databases, decision support systems, and competitive intelligence to collect and analyze information.
This document summarizes slides from a presentation on ethics and codes of conduct. It discusses the motivation for professional codes of ethics, including establishing status and self-regulation. It outlines the ACM Code of Ethics, including general moral imperatives and professional responsibilities. Two ethical cases are presented involving issues of intellectual property and privacy. The ACM Code of Ethics is applied to analyze the ethical considerations in each case.
An introduction to the ethics of AI in educationJisc
Presentation slides from Jisc's "an introduction to the ethics of AI in education" event held on 7 December 2021.
This presentation aims:
- To introduce the ethical issues associated with using AI in education
- To explain how ethical issues can be avoided, managed, mitigated and/or overcome
- To introduce you to the Ethical Framework for AI in Education and the Pathway to Ethical AI
Ch3 Gathering Information and Scanning EnvironmentNess Cabahug
This document summarizes the top 10 concepts for gathering marketing information and scanning the business environment from Chapter 3. It discusses the components of a marketing information system, how internal records and marketing intelligence can help identify opportunities and problems. It also outlines the key factors to consider in the demographic, economic, socio-cultural, natural, technological, and political-legal environments and how they impact business needs, trends and intelligence gathering. Examples are provided for each concept relating to a bank, Chinabank.
Data mining provides advantages for marketing, advertising, education, image recognition, and bioinformatics. However, it also poses disadvantages like misuse of information, violation of user privacy, and security issues. Data mining techniques can analyze user behavior and habits but lack adequate security and privacy protections. While data mining benefits many industries and applications, its potential disadvantages regarding privacy, security, and misuse of data must be addressed.
Ethics and Responsible AI Deployment
Abstract: As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. By taking a multidisciplinary approach, the research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines. The study concludes that these algorithms effectively enhance privacy protection while balancing the utility of AI with the need to protect personal data. The article emphasises the importance of a comprehensive approach that combines technological innovation with ethical and regulatory strategies to harness the power of AI in a way that respects and protects individual privacy.
Artificial intelligence (AI) has the potential to significantly impact employment, social equity, and economic systems in ways that require careful ethical analysis and aggressive legislative measures to mitigate negative consequences. This means that the implications of AI in different industries, such as healthcare, finance, and transportation, must be carefully considered.
Due to the global nature of AI technology, global collaboration must be fostered to establish standards and regulatory frameworks that transcend national boundaries. This includes the establishment of ethical guidelines that AI researchers and developers worldwide should follow.
To address emergent ethical concerns with AI, future research must focus on several recommendations. Firstly, ethical considerations must be integrated into the design phase of AI systems and not treated as an afterthought. This is known as "Ethics by Design" and involves incorporating ethical standards during the development phase of AI systems to ensure that the technology aligns with ethical principles.
Secondly, interdisciplinary research that combines AI, ethics, law, social science, and other relevant domains should be promoted to produce well-rounded solutions to ethical dilemmas. This requires the participation of experts from different fields to identify and address ethical issues.
Thirdly, regulatory frameworks must be dynamic and adaptive to keep pace with the rapid evolution of AI technologies. This means that regulatory frameworks must be flexible enough to accommodate changes in AI technology while ensuring ethical standards are maintained.
Fourthly, empirical research should be conducted to understand the real-world implications of AI systems on individuals and society, which can then inform ethical principles and policies. This means that empirical data must be collected to understand how AI affects people in different contexts.
Finally, risk assessment procedures should be improved to better analyse the ethical hazards associated with AI applications.
This document provides guidance on operationalizing AI ethics. It recommends identifying existing infrastructure to support an AI ethics program, creating a tailored risk framework for one's industry, and changing perceptions of ethics. Key steps include optimizing tools for product managers, building organizational awareness, inspiring employees to identify risks, and monitoring impacts and stakeholder engagement. The document warns against purely academic or engineering-led approaches and outlines specific tactics like establishing governance, developing quality assurance programs, and increasing trainings.
Legal Research Proposal on corporate governance on directors' training.final ...Siti Azhar
This document outlines a research proposal on corporate governance implementation for private companies in Malaysia. It discusses selecting the research area of corporate governance and identifying the problem of whether directors' training should be compulsory or voluntary. The proposal covers reviewing literature, developing a theoretical framework, selecting a methodology, and outlines chapters for the research. It provides details on the research problem formulation process, including defining the theme, dissecting it into sub-areas, raising research questions, and formulating objectives. The goal is to analyze if making directors' corporate governance training compulsory can help increase awareness and proper implementation in private companies.
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The document provides details about a survey being conducted by the Australian Information Industry Association (AIIA) Data & Analytics Special Interest Group. The survey aims to assess organizational maturity levels in effectively utilizing data and analytics. It will involve distributing a survey to members of partner organizations, analyzing the results, and conducting executive briefings. The timeline outlines plans to develop the survey, analyze responses in August-September, and hold briefings in October. Background information establishes the business issues motivating the survey and hypotheses about success factors related to data usage that will be tested. An overview of McKinsey and Gartner maturity models is also provided, as well as draft survey questions.
This document discusses ethics and improving business ethics. It begins by defining ethics as a set of beliefs about right and wrong behavior. It emphasizes the importance of integrity and operating consistently according to a moral code. It then discusses why fostering good business ethics is important for gaining community goodwill, operating consistently, producing good business outcomes, protecting the organization from legal issues, and avoiding unfavorable publicity. The document provides several approaches that corporations can take to improve ethics, such as appointing an ethics officer, establishing an ethics code, conducting ethics training, and including ethics in performance reviews.
The document discusses how data analytics and financial technology (fintech) companies are revolutionizing the banking industry. It provides definitions of key terms like fintech and describes how fintech companies exploit inherent risks in banking through data-driven lending. Several use cases of data analytics in banking are outlined, along with some of the risks to traditional banks from these new competitors. Techniques of data science that can be applied in banking are listed. The document aims to outline how data analytics is transforming financial services.
In today's interconnected digital world, understanding the intricate web of cyber law and professional ethics is crucial for individuals and organizations alike. These comprehensive notes serve as an invaluable guide, offering a deep dive into the multifaceted realm of cyber law and the ethical considerations that accompany it.
Covering a broad spectrum of topics, the notes provide a systematic exploration of the legal frameworks governing cyberspace, including regulations pertaining to data privacy, intellectual property rights, cybercrime, and digital transactions. Readers will gain insight into landmark legislation such as the General Data Protection Regulation (GDPR), the Digital Millennium Copyright Act (DMCA), and the Computer Fraud and Abuse Act (CFAA), among others.
From time-to-time internal auditors are faced with situations which call for them to make an ethical decision. In addition, they may, in the middle of auditing, come across circumstances which themselves appear to be violations of a corporate
code-of-conduct.
Several laws now specifically state that internal auditors, in terms of the act, will be bound by the IIA Code of Ethics.
This webinar explores the IIA Code of Ethics as it applies to everyday situations the auditor may encounter.
The module is designed to provide the participants with an in-depth knowledge of:
Ethics theory
The IIA Code of Ethics
Applicable areas within Internal Audit
Reporting of material facts
Corporate Codes of Conduct
Auditing Corporate Ethics
Webinar contents will include:
Classes of Ethics
The role of business
Employee ethics
Honesty, Objectivity and diligence
Conflicts of Interest
Reporting of Material Facts
Corporate Codes of Conduct
Corporate Social Responsibility
You are assisting Dr. Jones with a procedure that has been classifie.docxShainaBoling829
You are assisting Dr. Jones with a procedure that has been classified as sterile. However, you later learn the patient acquired an iatrogenic infection. Who is ultimately responsible for this event? How would you determine responsibility? What information would be required to make this determination? Please support your answer with at least one reference.
Why is it important to know what type of infection a patient has? An infection is an infection, is an infection. Does it matter where it comes from, why or why not, please explain?
.
You are an intelligence analyst for the Federal Bureau of Investigat.docxShainaBoling829
You are an intelligence analyst for the FBI's Counterintelligence Division tasked with researching and producing a case study on a major espionage case from the past to help identify anomalies that could indicate future espionage. You have been assigned to write a 750-word case study on Robert Hanssen, Aldrich Ames, Ana Montes, or John Walker and address who was involved, when and where the espionage took place, what information was compromised, how it was obtained and shared, why the spy acted, lessons learned, and the case's impact.
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From time-to-time internal auditors are faced with situations which call for them to make an ethical decision. In addition, they may, in the middle of auditing, come across circumstances which themselves appear to be violations of a corporate
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This document discusses data ethics and provides 5 key principles of data ethics for business professionals:
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1. A marketing information system consists of people, equipment, and procedures to gather, sort, analyze, evaluate, and distribute timely and accurate information to marketing decision makers.
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This document summarizes slides from a presentation on ethics and codes of conduct. It discusses the motivation for professional codes of ethics, including establishing status and self-regulation. It outlines the ACM Code of Ethics, including general moral imperatives and professional responsibilities. Two ethical cases are presented involving issues of intellectual property and privacy. The ACM Code of Ethics is applied to analyze the ethical considerations in each case.
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This document summarizes the top 10 concepts for gathering marketing information and scanning the business environment from Chapter 3. It discusses the components of a marketing information system, how internal records and marketing intelligence can help identify opportunities and problems. It also outlines the key factors to consider in the demographic, economic, socio-cultural, natural, technological, and political-legal environments and how they impact business needs, trends and intelligence gathering. Examples are provided for each concept relating to a bank, Chinabank.
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Artificial intelligence (AI) has the potential to significantly impact employment, social equity, and economic systems in ways that require careful ethical analysis and aggressive legislative measures to mitigate negative consequences. This means that the implications of AI in different industries, such as healthcare, finance, and transportation, must be carefully considered.
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To address emergent ethical concerns with AI, future research must focus on several recommendations. Firstly, ethical considerations must be integrated into the design phase of AI systems and not treated as an afterthought. This is known as "Ethics by Design" and involves incorporating ethical standards during the development phase of AI systems to ensure that the technology aligns with ethical principles.
Secondly, interdisciplinary research that combines AI, ethics, law, social science, and other relevant domains should be promoted to produce well-rounded solutions to ethical dilemmas. This requires the participation of experts from different fields to identify and address ethical issues.
Thirdly, regulatory frameworks must be dynamic and adaptive to keep pace with the rapid evolution of AI technologies. This means that regulatory frameworks must be flexible enough to accommodate changes in AI technology while ensuring ethical standards are maintained.
Fourthly, empirical research should be conducted to understand the real-world implications of AI systems on individuals and society, which can then inform ethical principles and policies. This means that empirical data must be collected to understand how AI affects people in different contexts.
Finally, risk assessment procedures should be improved to better analyse the ethical hazards associated with AI applications.
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This document outlines a research proposal on corporate governance implementation for private companies in Malaysia. It discusses selecting the research area of corporate governance and identifying the problem of whether directors' training should be compulsory or voluntary. The proposal covers reviewing literature, developing a theoretical framework, selecting a methodology, and outlines chapters for the research. It provides details on the research problem formulation process, including defining the theme, dissecting it into sub-areas, raising research questions, and formulating objectives. The goal is to analyze if making directors' corporate governance training compulsory can help increase awareness and proper implementation in private companies.
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The memo that you receive from your Drug Enforcement Administration (DEA) task force supervisor explains the situation:
MEMO
Re: Charging Decisions
You are the primary investigator in the cases against Jones, Smith, and Thompson. As I review your reports, it appears that each of these cases has strengths and weaknesses that we should evaluate before we determine whether to file charges in the U.S. District Court, the Sedgwick County District Court for the State of Kansas, or the Wichita Municipal Court. I will summarize those strengths and weaknesses here to make sure I am reading your reports correctly. I need you to give me advice on where you think these charges should be brought.
Jones has been working for you as a confidential informant because you have evidence against him for a February 6, 2005 third possession of cocaine after convictions in 1993 and 1994. He appears to have followed the terms of his deal with you to introduce our undercover agents to his dealer. We have promised not to prosecute for any drug offenses he may commit in the presence of our undercover agent while playing the role of our informant. His assistance has enabled us to get sufficient evidence on Smith and Thompson to obtain convictions. Based on Jones’ two prior convictions for possession of cocaine, we would normally want him to go to federal court, where the maximum sentences are available. However, because of his cooperation, we could file the case in the Sedgwick County, Kansas, and district court under state law. We could even change the charge to a drug paraphernalia offense and send his case to the city of Wichita.
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Assignment Guidelines
Address the following in 900–1,200 words:
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If you arrest the other individuals for the crimes not associated with the reasons for the wiretap, what happens to any future evidence that might be obtained from the wiretap? Why?
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A Workplace
Create
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.
You and your team have completed your fieldwork and have a handful o.docxShainaBoling829
You and your audit team have completed your fieldwork for an audit. As a senior staff member who may be promoted to manager, you need to instruct other staff on reviewing for contingent liabilities, letters from client lawyers, and subsequent events before issuing the audit report. This is to ensure nothing significant has occurred between completing fieldwork and reporting.
xxxx, great post. I agree that as technology has grown so has the .docxShainaBoling829
The document discusses how technology and social media have increased media influence over what information the public receives and how they perceive events. It also notes that social media portrays false images that influence dress choices and cause some people to build their lives around social media celebrities rather than being themselves. The document advocates that people should not feel they need to follow the masses or media portrayals and instead be comfortable being themselves.
Yes Richard I agree with you. The American Red Cross has been workin.docxShainaBoling829
Yes Richard I agree with you. The American Red Cross has been working alongside families and communities in Haiti for more than 10 years. When a 7.0 earthquake struck the country in 2010, Americans’ generosity has made this critical work possible. Thanks to donations from across the United States, American Red Cross continue to help Haitians recover from these disasters. They still provide food, water, medical care, sanitation and emergency shelter to families in need. Over the past seven years, they have funded more than 50 hospitals and clinics in Haiti and so much more.They continue working alongside the Haitian Red Cross to ensure that recovery is long-lasting and that families are prepared for future disasters that may come their way
I NEED YOU TO COMMENT FROM THIS POST, NO MORE THAN 150 WORDS NEEDED AND A REFERNCE PLEASE
.
Yet society has in every possible way created the impression that on.docxShainaBoling829
Wink argues that society creates the impression that some people are favored by God while others are rejected, based on attributes like appearance, wealth, gender, etc. There are benefits to going along with this system of unequal social hierarchies, but rejecting it challenges the entire structure. If God does not favor some over others based on accidents of birth, then the social order is a human construct established against God's nature of equality and justice for all.
xxxxx comment 1xxxxx, I believe America only sees leftright, li.docxShainaBoling829
xxxxx comment 1
xxxxx, I believe America only sees left/right, liberal/conservative, one's race/others' race, one's religion/others' religion, etc. To be fair, there are important issues that we do face but the media has pulled both further from the center. This is done to keep us preoccupied in conflict so we ignore what is being done in front of our faces, which is politicians/media/wealthy elites are controlling the government/financial system/media to mold the public's views and what they buy. By them focusing on these secondary issues and differences, we are missing the root problem: money in politics. These legal bribes guarantee that we are not represented in legislation unless enough people oppose the current law.
Comment 2
Nicely said, it is amazing how money can be used to basically buy anything in the world, even our politicians. The Presidency, our Senators and Congressman, Governors, Mayor's and more. This allows for things like the rich getting richer and the poor or course getting poorer. It almost seems like there is no middle class anymore. Money plays a huge role in everyday life. Don't get me wrong, money and politicians has definitely been used in some cases for the good or doing the right thing. We cannot base everything evil or not perfect on money. We just have to be more responsible.
.
WWTC Active Directory DesignWWTC office at New York is largely a.docxShainaBoling829
WWTC Active Directory Design
WWTC office at New York is largely autonomous and few IT personnel to take care of day-to-day IT support activities such as password resets troubleshoot virus problems. You are concerned about sensitive data store in this location. You want to deploy a highly developed OU structure to implement security policies uniformly through GPO automatically at all domains, OU, and workstations.
At this location Windows Server 2012 R2 is required providing the following
10 AD features
:
1.
Use BitLocker encryption technology for devices (server and Work station) disc space and volume.
2.
Enables a BitLocker system on a wire
d network to automatically unlock the system volume during boot (on capable Windows Server 2012 R2 networks), reducing internal help desk call volumes for lost PINs.
3.
Create group policies settings to enforce that either Used Disk Space Only or Full Encryption is used when BitLocker is enabled on a drive.
4.
Enable BranchCache in Windows Server 2012 for substantial performance, manageability, scalability, and availability improvements
5.
Implement Cache Encryption to store encrypted data by default.
This allows you to ensure data security without using drive encryption technologies.
6.
Implement Failover cluster services
7.
Implement File classification infrastructure feature to provide automatic classification process.
8.
IP Address Management (IPAM) is an entirely new feature in Windows Server 2012 that provides highly customizable administrative and monitoring capabilities for the IP address infrastructure on a corporate network.
9.
Smart cards and their associated personal identification numbers (PINs) are an increasingly popular, reliable, and cost-effective form of two-factor authentication. With the right controls in place, a user must have the smart card and know the PIN to gain access to network resources.
10.
Implement Windows Deployment Services to enables you to remotely deploy Windows operating systems. You can use it to set up new computers by using a network-based installation.
Other AD Deliverables
:
Create Active directory infrastructure to include recommended features
Create OU level for users and devices in their respective OU
Create Global, Universal, Local group. Each global group will contain all users in the corresponding department. Membership in the universal group is restrictive and membership can be assigned on the basis of least privileged principle. (For design purpose, you can assume that WTC as a Single Forest with multiple domains).
Create appropriate GPO and GPO policies and determine where they will be applied
.
Wrongful Convictions and the Utilization of Eyewitness Accounts Wr.docxShainaBoling829
Wrongful Convictions and the Utilization of Eyewitness Accounts
Write a 2 to 3 page paper responding to the following: APA FORMAT
Identify the ethical issues within the field of criminal investigation as applied to wrongful conviction based upon tainted or faulty line-ups.
In recent years we have seen many criminal convictions overturned for various reasons. One such reason is the “Eyewitness Account.”
Address the ethical responsibilities of law enforcement in their requirements for fairness, and responsibility to ensure there are no wrongful convictions based upon false identification.
Identify the processes utilized by law enforcement in the identification of suspects.
Consider individuals making identifications, do so in error at times, others intentionally, or are led by law enforcement through improper actions i.e., prejudicial line-ups or photo arrays.
.
Written Report on Documentary Enron The Smartest Guys in the Roo.docxShainaBoling829
Written Report on Documentary:
Enron: The Smartest Guys in the Room
For this assignment view the video,
ENRON:
The Smartest Guys in the Room,
[1 hr. & 50 min].
Write a critique of the film in 4-5 page double-spaced paper.
Answer each of the following questions in your essay.
The written assessment of
Enron
is due according to Syllabus.
Submit a paper copy in class and also post it on BB website SafeAssign.
2.
Describe the dominant culture of ENRON and the subculture of Enron’s trading group.
3.
Do you believe that Enron’ failure is a result of the behavior of “a few bad men”, or a demonstration of the “dark shadow of the American dream”?
Explain.
4.
What did Skilling say is the only thing that motivates people?
Do you agree or disagree?
5.
Describe the PRC (performance review committee).
Why was it referred to as “rank and yank”?
What was its effect?
What is your opinion of the ethics of the practice?
6.
Describe Enron’s initiative on broadband technology.
7.
What was Arthur Andersen’s conflict of interest in regards to Enron?
What could have been done to prevent this conflict of interest?
8.
How did Skilling treat Fortune author Bethany McLean when she started asking questions about Enron’s financials?
Do you think this was a tactic, and if so, what did he hope to achieve by it?
9.
What are three important “takeaway” messages you learned from this documentary?
.
Written assignment,. please follow instruction..Legislative Prof.docxShainaBoling829
The document provides instructions for a written legislative profile assignment requiring the respondent to research and provide information about various elected officials representing their state and district. This includes identifying a US Senator and House Representative, as well as state-level Senators and House members. For each official, the respondent must provide biographical details, committee assignments, political views supported by quotes, and summaries of speeches found online. The instructions emphasize completing all questions, citing sources, avoiding plagiarism, and ensuring correct grammar, spelling and completeness.
Written Assignment Choose a Part 121 air carrier(such as Am.docxShainaBoling829
Written Assignment:
Choose
a Part 121 air carrier
(such as American, Delta, Southwest, etc.) and provide data that shows how that enterprise has successfully employed competitive advantage obtained through the utilization of information technology to win and keep loyal customers or operate more efficiently in the reservations, maintenance, or operations departments. You may provide a historical example that would be found going back several decades. Learning from the past is a great way to succeed in the future.
.
WRITTEN ASSIGNMENT for Unit 11 is to write a eulogy, no longer than .docxShainaBoling829
This document provides instructions for a written assignment to write a 2-3 minute eulogy for a deceased or living person. Students are asked to write a manuscript as if transcribing what they would say at the eulogy, including an creative introduction, supporting details, and conclusion with transitions. They should not record a speech or provide an outline, but instead copy and paste the written word-for-word manuscript.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
FINM4100Analytics in Accounting, Finance and Economics
1. FINM4100
Analytics in Accounting,
Finance and Economics
Ethical considerations and more
applications of business analytics and
technology in accounting, finance and
economics
Week 11
Lesson Learning Outcomes
1 Evaluate ethical considerations regarding FinTech
and the use of analytics in Accounting, finance and
economics
2 Investigate case studies
3 Find potential solutions to ethical, privacy and legal
issues related to the finance sector and its use of data
2. 4 More applications of analytics in finance
Glossary1: Data Ethics
• Data Ethics relates to
- Responsible use of data
- The value placed on data by competing parties
- The purpose and interests of data processing
• It is about the right to keep your personal data protected
• It is about transparency & accountability
https://dataethics.eu/data-ethics-principles/
One implication is that Individual humans should have control
of their data.
T
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4. https://dataethics.eu/data-ethics-principles/
http://paul-barford.blogspot.com/2010/06/ss-holds-out.html
https://creativecommons.org/licenses/by-nd/3.0/
Where we are at…
• More and more accounting and finance organisations are
adopting AI and analytics
• There’s already an 80 - 90% reduction in time taken to do
usual tasks
• The roles of professionals in this area are changing as
repetitive tasks are automated
• Technology is changing the way we deal with compliance
• Ethical questions are arising daily
https://bernardmarr.com/artificial-intelligence-in-accounting-
and-finance/
This Photo by Unknown Author is licensed under CC BY-SA-
NC
https://technofaq.org/posts/2019/09/cyber-security-trends-to-
watch-out-for-organizations-to-stay-ahead/
https://creativecommons.org/licenses/by-nc-sa/3.0/
This Photo by Unknown Author is licensed under CC BY-SA
Where we are heading..
• Near real-time insights
5. • Algorithms will transform ideas around compliance and
reduce fraud costs and lead to….
• More flexible work arrangements and different roles
• Possible need to hire an ethics expert
•
• The redefining of ethical conduct in business
https://www.thebluediamondgallery.com/tablet/b/business-
ethics.html
https://creativecommons.org/licenses/by-sa/3.0/
Case Study: Google a bank?
• It hasn’t been easy for all financial institutions to keep up
with new
technology and demand for convenient services
• Consequently…. Amazon, Apple and Google have started to
offer services
normally offered by big banks
• Example: Google Pay
• The issue: Google is an advertising company with ads
representing 71% of
its revenue sources in 2019.
• Given Google’s history of collecting Terrabytes of data from
your location,
6. emails, shopping and song preferences
• Q: Do we really trust Google as a bank?
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8. • Exchange traded fund (ETF) is a kind of “pooled
investment security” (or basket of them) which can be
traded like a single stock
• They track a particular index, sector or commodity, e.g SPY
tracks the S&P 500 index
https://www.investopedia.com/terms/e/etf.asp
https://www.marketmovers.it/2019/01/pimco-euro-high-yield-
IE00BD8D5H32.html
https://creativecommons.org/licenses/by-nc/3.0/
Glossary 3: What is
Superannuation?
• “Superannuation (or ‘super’) is money set aside while
you’re working to support your financial needs in
retirement. Your super is invested in a range of assets to
help grow your balance so you can have the best
possible retirement outcome.”
This Photo by Unknown Author is licensed under CC BY-ND
http://theconversation.com/you-may-be-quietly-lining-up-to-
lose-on-your-superannuation-7612
9. https://creativecommons.org/licenses/by-nd/3.0/
Glossary 4: What is a robo-advisor
• “Robo-advisors are digital platforms that provide
automated, algorithm-driven financial planning services
with little to no human supervision. A typical robo-advisor
asks questions about your financial situation and future
goals through an online survey; it then uses the data to
offer advice and automatically invest for you.”
https://www.investopedia.com/terms/r/roboadvisor-
roboadviser.asp
This Photo by Unknown Author is licensed under CC BY-NC
https://navesinkinternational.com/where-robo-advisors-are-
better-than-financial-advisors/
https://creativecommons.org/licenses/by-nc/3.0/
Case study: Ethics and investing of
your money by others
• Are ETFs, superannuation funds and robo-advisors ethical?
• ETFs and superannuation are everchanging “holding
structures”.
10. • For example, an Australian shares ETF or choosing Australian
Shares
as an option in a super fund. In both cases you are buying
shares but
not directly.
• Robo Advisors also invest for you, so you are not directly
buying the
items yourself, just buying based on non-human advice.
• How do you know that they are ethical?
• Ways to find out if they are ethical or not:
– to understand how your money is invested
– To ask them for their environmental, social and governance
policy
Activity 1: Is technology neutral?
• Form small groups
• Watch the video at
https://www.youtube.com/watch?v=q_AwceyM68k
• Discuss the following:
Q1. You can’t see ethical value in technology by just looking at
it, so where
do we have to look to find it and how can we apply moral
judgement
11. regarding a particular technology?
Q2.What is so special about technology?
https://www.youtube.com/watch?v=q_AwceyM68k
•
Solution
s to potential ethical,
privacy and legal issues
This Photo by Unknown Author is licensed under CC BY-NC-
ND
https://www.gbcnv.edu/admissions/privacy.html
https://creativecommons.org/licenses/by-nc-nd/3.0/
Holding financial institutions and staff
accountable
• Australian Securities and Investments Commission (ASIC) is
Australia’s
12. corporate, markets and financial services regulator
• The Australian Prudential Regulation Authority (APRA)
establishes
frameworks of standards and practises in the financial sector
• Australian Competition and Consumer Commission (ACCC)
helps
protect consumers https://www.accc.gov.au/
• The Office of the Australian Information Commissioner
(OAIC) has a
great deal of documentation on Australia’s data privacy laws.
https://www.accc.gov.au/
Examining ethical conduct at an
individual level
13. • Given that the ethical implications of AI are such
a large concern, Who will examine the ethical
dilemas at an individual level?
• If you don’t have an ethics officer, it may be your
organisation’s management account or
compliance officer.
• They will
– Practice ethical standards
– Create an culture of ethical nature
– Use diagnostic analytics in cases where AI caused
ethical issues
https://sfmagazine.com/post-entry/january-2021-ethics-maps-
for-ai-analytics/
Ethics Mapping
• What is an ethics mapping?
14. • An ethics map is a map of the range of concerns you might
have
in the context of the type of service your staff/AI is suppose to
provide
• An overview of certain behaviours, e.g. what may be
considered
as acting in the accounting or finance space with
– no ethics
– indifference (or a relative view of ethics)
– value-based ethics
– (see examples next slide)
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.
7022&rep=rep
1&type=pdf#:~:text=Page%201,Ethics%20and%20the%20Publi c
%20Service'
This Photo by Unknown Author is licensed
16. bias or does not see a
problem
Codes with fairness and
non-discrimination in
mind regarding the
approval of loans
Collects data and sells it
on without permission
Collects data they may
not need and does not
see an issue with
sharing it
17. Collects data for a
specific purpose and
does not share it
https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.
7022&rep=rep
1&type=pdf#:~:text=Page%201,Ethics%20and%20the%20Public
%20Service'
Activity 2: Ethics Mapping
• Form small groups,
• Suppose that you work in either accounting, finance or
economics
• Create an ethics mapping table with four rows
• Each row should provide an example as in the previous slide
18. No values Relative Values or
Indifference
Value – based ethics
Example 1……
Example 2……
Example 3……
Example 4……
Reviewing the banking code
of practice
• “Australia's banks may face rules on ethical use of
tech, data”
• The banking code of practice was reviewed in 2021
19. • The code consists of a “set of enforceable standards” that
customers
and small businesses can expect from Australian banks, i.e. a
set of
rules setting out the rights of customers.
• The safe and secure handling of bank customer’s data was
questioned
in the review,
– especially in the context of financial and elder abuse, as well
as domestic violence
• What issues do you think they are talking about?
https://www.itnews.com.au/news/australias-banks-may-face-
rules-on-ethical-use-of-tech-data-566937
Case Study: Digital banking and privacy
• “Banks say they should not be treated like Big Tech by online
privacy bill”
• Along similar lines to the review of the banking code of
20. practice
• A new online privacy bill aimed at tech companies may affect
banks, insurers, superannuation funds, etc., because it is so
broad
in its definition of “online platforms”
• Examples of obligations:
• 26KC(4)(a) “respond to a request to not use…personal
information within a
reasonable period.”
• 26KC(2) “Notify an individual…of the purposes for which the
organisation
collects, uses and discloses personal information.”
https://www.itnews.com.au/news/banks-say-they-should-not-be-
treated-like-big-tech-by-online-privacy-
bill-575701This Photo by Unknown Author is licensed under
CC BY-SA-NC
https://technofaq.org/posts/2017/03/everything-you-should-
know-about-successful-online-employee-training/
21. https://creativecommons.org/licenses/by-nc-sa/3.0/
Activity 3: Why not to use a robo-
advisor
• Watch the video about robo-advisors at
• https://www.youtube.com/watch?v=wjB3hp1RUKQ
Q1. What reasons are given for not using the robo-advisors
Q2. Given that this guy is advertising his own business,
what do you think?
https://www.youtube.com/watch?v=wjB3hp1RUKQ
Reduce bias in AI-based financial
services
• Bias is often present in input data in finance
22. • Ways around this are:
– avoid gender, racial or ideological biases
– use complete and representative data
– have diversity in development teams
– monitor situations where AI systems self-improve, acquire
new behaviours and have unintended results
This Photo by Unknown Author is licensed under CC BY
https://disasteravoidanceexperts.com/how-to-evaluate-
unconscious-bias-caused-by-cognitive-biases-at-work/
https://creativecommons.org/licenses/by/3.0/
Consider ethical, privacy and legal
issues on a case by case basis, e.g.
• Many platform owners currently have the option to use
customer data for commercial/other purposes (in the fine
23. print)
• Suggestions to make things more ethical:
– Allow customers to disable use of some personal data
– Inform users of how exactly their data is being used
– Allow customers to choose how they want to share their data,
what type of data and for what purpose
https://fintechweekly.com/magazine/articles/what-about-the-
ethics-of-
fintech#:~:text=Ethical%20considerations%20for%20FinTech,-
First%2C%20many%20online&text=Customers%20should%20b
e%20able
%20to,vis%2D%C3%A0%2Dvis%20customers.
24. Glossary 5: What is Open Banking?
• Open banking is about sharing your banking data with third
parties.
• In Australia, the third parties must accredited by the ACCC
https://www.ausbanking.org.au/priorities/open-banking/
This Photo by Unknown Author is licensed under CC BY
Data that can be shared
• Personal information
• Account balances
• Bank product information
• Transaction amounts
http://www.midiatismo.com.br/open-banking-sera-que-vamos-
ter-acesso-isso-algum-dia
https://creativecommons.org/licenses/by/3.0/
25. Activity 4: Think-group-share
• Form small groups and
brainstorm
1. Potential ethical and
privacy issues in relation
to open banking
2. ways in which to make
open banking ethical, safe
and private where
necessary
This Photo by Unknown Author is licensed under CC BY-SA-
NC
https://www.getmespark.com/five-ways-not-to-brainstorm/
https://creativecommons.org/licenses/by-nc-sa/3.0/
Applications of analytics in finance
In prep for next week we will start revising some methods
and applications in Finance
26. Broad application areas are
• 1. Risk Analytics
• 2. Real-Time Analytics
• 3. Consumer Analytics
• 4. Customer Data Management
• 5. Personalized Services
• 6. Financial Fraud Detection
• 7. Algorithmic Trading
https://www.upgrad.com/blog/data-science-use-cases-finance-
industry/
This Photo by Unknown Author is licensed under CC BY-ND
https://www.upgrad.com/blog/data-science-use-cases-finance-
industry/#1_Risk_Analytics
https://www.upgrad.com/blog/data-science-use-cases-finance-
28. recommendations for
savings, investments and
loans
• Targeted offers based on
spending patterns
• AI-based money
management programs
https://personetics.com/
https://themonetaryfuture.blogspot.com/2013/11/banking-
innovation-depends-on-bitcoin.html
https://creativecommons.org/licenses/by/3.0/
Last minute questions?
This Photo by Unknown Author is licensed under CC BY
29. https://leadershipfreak.wordpress.com/2010/03/05/10-best-
questions-ever
https://creativecommons.org/licenses/by/3.0/
Victim Advocate Worksheet
Job Profile
Directions: Research the position of victim advocate and answer
the following questions.
What responsibilities does a victim advocate have in a case?
When does a victim advocate become involved in a criminal
case? When does the involvement end?
What skills would be important for a victim advocate to
possess? Why?
Based on what you have learned about this position, would you
be interested in becoming a victim advocate? Why or why not?
1
30. Walden University - MSCRJS CRJS6203
Type a caption for your photo
The highest rates of victims in Washington, D.C. include:
Include 5-10 types of victims and statistics for each type
Crime Victims' Bill of Rights
Insert information
Phone: [Telephone]
Email: [Email address]
Web: [Web address]
Victims’ Rights and Services
Above the title, insert an appropriate and engaging graphic. In
this text box, Insert a few important statistics.
Crime Victims’ Compensation Program
Contact Us
Insert information
Types of Victims
Note:
This brochure is designed to be printed. You should test print
on regular paper to ensure proper positioning before printing on
card stock.
You may need to uncheck Scale to Fit Paper in the Print dialog
(in the Full Page Slides dropdown).
31. Check your printer instructions to print double-sided pages.
To change images on this slide, select a picture and delete it.
Then click the Insert Picture icon
in the placeholder to insert your own image.
To change the logo to your own, right-click the picture
“replace with LOGO” and choose Change Picture.
Header
Community Resources
This spot would be perfect for a mission statement. You might
use the right side of the page to summarize how you stand out
from the crowd and use the center for a brief success story.
(And be sure to pick photos that show off what your company
does best. Pictures should always dress to impress.)
Think a document that looks this good has to be difficult to
format?
Think again! The placeholders in this brochure are formatted for
you. Enter your own text with just a click.
“insert powerful quote about rights and/or services.”
Get the exact results you want
To easily customize the look of this brochure, on the Design tab
of the ribbon, check out the Themes, Colors, and Fonts
galleries.
Have company-branded colors or fonts?
32. No problem! The Themes, Colors, and Fonts galleries give you
the option to add your own.
Use a photo depicting victim resources
Don’t forget to include some specifics about what you offer,
and how you differ from the competition.
Want to help us create change? Volunteer with us!
Insert volunteer information
Use a photo depicting volunteers
Note:
This brochure is designed to be printed. You should test print
on regular paper to ensure proper positioning before printing on
card stock.
You may need to uncheck Scale to Fit Paper in the Print dialog
(in the Full Page Slides dropdown).
Check your printer instructions to print double-sided pages.
To change images on this slide, select a picture and delete it.
Then click the Insert Picture icon
in the placeholder to insert your own image.
To change the logo to your own, right-click the picture
“replace with LOGO” and choose Change Picture.
33. Economic Applications of
Big Data & Predictive Analytics
FINM4100
Analytics in Accounting,
Finance and Economics
Week 9
Lesson Learning Outcomes
1 Define and review ideas around micro- and
macroeconomics
2 Review the concept of correlation
34. 3 Analyse Macroeconomic data
Why Build Models?
“Just because you
have more data
doesn’t mean that
you’re going to make
better decisions.”
Models encapsulate
patterns that exist in
data, helping us make
sense of them.Christina Zhu
Assistant Professor of Accounting
Wharton School of the University of Pennsylvania
35. SELTS
• Student feedback is usually done in week 9
• You may be asked to fill in a survey
This Photo by Unknown Author is licensed under CC BY-SA
http://exzuberant.blogspot.co.uk/2011/02/putting-student-voice-
into-practice.html
https://creativecommons.org/licenses/by-sa/3.0/
Software for today
1. Google Colab
• Either
A. watch the teacher demonstrate analytics and accounting in
python
OR
36. B. you can run the python scripts yourself in Google Colab
• If you want to run the code provided, make sure you have
access
(signed in) to Google Colab https://colab.research.google.com
2. Exploratory
A. watch the teacher demonstrate analytics and accounting in
Exploratory OR
B. run each step yourself online (access is explained on the next
slide)
https://colab.research.google.com/
Dataset
• Data: countries of the world.csv (1970 to 2017)
• Business Problem: How do we determine factors affecting a
country's GDP per capita and make a model using the data of
many countries?
37. • We have data from 227 countries and variables (factors) such
as GDP, population, literacy, crops (%), birthrate, and others.
• We will explore correlations between each factor and GDP
across various countries in python
• Make charts (try multiple linear regression in Exploratory)
This Photo by Unknown Author is licensed under CC BY-SA
http://superuser.com/questions/49642/where-can-i-find-google-
maps-with-a-geopolitical-overlay-as-in-colored-countrie
https://creativecommons.org/licenses/by-sa/3.0/
What is Economics?
• Economics is the study of how society allocates scarce
resources to satisfy unlimited wants
• We can consider two branches of economics:
▪ Microeconomics is the study of how single economic
38. units of society make economic decisions
▪ Macroeconomics is the study of how an aggregated
economy makes economic decisions
What is Economics?
Is the study of how society allocates scarce resources
to satisfy unlimited wants
Economics
Production,
distribution
and
consumption
39. Scarcity,
choice and
decision
making
Microeconomics
Focus:
• How individual consumers and companies make decisions
• How they respond to changes in price
• Why different goods have different prices
• How humans may trade in an optimal way
Typical topics in this area are:
• Demand and supply
40. • Costs of producing goods (production, revenue and costs)
• Market structure, e.g. perfect competition
This Photo by Unknown Author is licensed under CC BY-ND
https://mru.org/courses/principles-economics-
microeconomics/subsidies-definition-subsidy-wedge
https://creativecommons.org/licenses/by-nd/3.0/
Macroeconomics
Focus:
The overall economy of a region, e.g. country, using aggregated
data
Typical topics in this area are:
• Economic cycles
• Economic growth
41. • Fiscal and monetary policy
• Unemployment rates
• Gross Domestic Product (GDP) which is a broad measure of a
country’s economic performance
T
h
is
P
h
o
to
b
y
U
n
k
n
o
43. B
Y
We will be analysing GDP data today
https://courses.lumenlearning.com/ivytech-
introbusiness/chapter/reading-stages-of-the-economy/
https://creativecommons.org/licenses/by/3.0/
Why is Economic Growth important?
• It is an indicator of a healthy economy
• One theory says increasing GDP leads to more employment in
some
sectors
• It leads to a better standard of living
• Key components of economic growth are thought to be
– Natural resources
– Infrastructure
44. – Population/labour
– Human capital
– Technology
– Law
This Photo by Unknown Author is licensed under CC BY-SA-
NC
https://ourworld.unu.edu/en/does-economic-growth-make-us-
happy
https://creativecommons.org/licenses/by-nc-sa/3.0/
GDP per capita 2021
How are we doing?
Activity 1: Think – pair – share
45. Economics
• Watch the video below which compares micro- and
macro- economics
• https://www.youtube.com/watch?v=nJbWj_kHCJQ
• Form pairs
• Person 1 will explain macroeconomics to person 2, then
person 2 will explain microeconomics to person 1
• Report back to class with comments and questions
https://www.youtube.com/watch?v=nJbWj_kHCJQ
Review of concepts
• Before analysing today’s data, we need to
review the idea of
– Covariance and correlation
– correlation heatmaps
46. This Photo by Unknown Author is licensed under CC BY
https://courses.lumenlearning.com/precalcone/chapter/distinguis
h-between-linear-and-nonlinear-relations/
https://creativecommons.org/licenses/by/3.0/
Two Measures of Association
▪ Covariance (is there any pattern to the way two variables
move together?)
a. Only concerned with the direction of the relationship
b. No causal effect is implied
c. Is affected by units of measurement
▪ Correlation coefficient which incorporates part of the
covariance formula (how strong is the linear relationship
between two variables?)
47. Correlation coefficient
Also called Standardised Covariance and is between –1 and 1
• The closer to –1, the stronger the negative linear relationship
• The closer to 1, the stronger the positive linear relationship
• The closer to 0, the weaker the linear relationship
This Photo by Unknown Author
is licensed under CC BY-NC-ND
http://communitymedicine4asses.wordpress.com/2013/12/27/cor
relation
https://creativecommons.org/licenses/by-nc-nd/3.0/
Visualising correlation coefficient
• Method 1: Correlation heatmap
48. This Photo by Unknown Author is licensed under CC BY-SA
http://stackoverflow.com/questions/6189327/correlation-heat-
map-for-windows-presentation-foundation
https://creativecommons.org/licenses/by-sa/3.0/
Visualising correlation coefficient
Y
X
Y
X
Y
X
r = -1.0 r = 0r = +0.3
Method 2: Plots of pairs of variables
49. Formulae for Covariance and
Correlation
Measures the relative strength of the linear relationship
between two variables
Sample covariance
and correlation coefficient
where
� =
σ�=1
� ��� − ҧ� (�� − ത�
σ�=1
� �� − ҧ�
2 σ�=1
� �� − ത�
50. 2
COV(x, y� =
σ�=1
� ��� − ҧ� (�� − ത�
� − 1
ҧ� is the mean of the x’s
ത� is the mean of the y’s
countries of the world.csv data
• In today’s data some of the variables are obvious while others
are
not
• It also has commas instead of dots (which we will deal with
later)
• Variables
– Agriculture
51. – Industry
– Service
• These three represent labour force by sector, so if agriculture
in
Liberia is 0,769. It is really 0.769 and means that 76.9% of the
work
force in Liberia work in the agricultural sector. Similarly for
Industry
and Service.
• Climate measure is a classification between 1 (drier) and 4
(milder)
Activity Open the script and run
or watch the demo
• Download the data countries of the world.csv to a directory of
your
choice
52. • Open the script below
https://colab.research.google.com/drive/15LsR6QoH858T4e2U4
LHFtlzWSL
EJrWMG?usp=sharing
• You will be prompted in the second block of code to choose
the data file
• Click in the box and find your countries of the world.csv to be
uploaded
• Run the rest of the script and analyse the output as it is
generated, e.g.
correlation heatmap, countries with the highest GDP, etc.
https://colab.research.google.com/drive/15LsR6QoH858T4e2U4
LHFtlzWSLEJrWMG?usp=sharing
Sample Output
Sample Output
53. Sample Output
Data Modification
• Make a copy of the data file in your folder
• Open the data in Microsoft Excel
• We would normally use a dot to indicate accuracy to one or
more decimal places, however a comma has been used here
• Highlight the data columns with commas
• Go to the “Editing menu”
• Click on Find & Select and scroll down to “replace”
• Replace commas , for dots . (Enter symbols as below) and
click
54. on Replace all
• Save your file
Data Modification
• Create a new column heading in column U called “GDP
Low_High”
• Type =IF(I2<3000, 0,1) in cell U2 and enter
• Click on the corner of that cell (you should see a cross), hold
and drag it down
the column to repeat the formula in rows down to cell U228
• You should see a zero if GDP < $3000 per capita and a one
otherwise
• Save your file
55. Exploratory
• Access Exploratory
• Start a new project called GDP analysis
• Use Data Frames + to find and import the modified data file
• Change variable GDP Low_High from numeric to logical
before clicking on save
• Select Analytics
• We are going to go through a simple guided Decision tree
model then you can
experiment and try to interpret your own
• Instructions for the model type and variables are on the next
slide
Exploratory analytics model
56. • Select Decision Tree as the type
• GDP Low_High as the Target variable
• Phones, birthrate and Agriculture as the predictor variables
• Leave sample size as is an run
• You will see a tree which is to be read from the top
• We will start to interpret this (first see next slide)
Simple Decision Tree
• The model makes its own
thresholds if you don’t make
all variables binary
• Positive of each condition is
to the right and negative to
57. the left
• If you add the percentages
from the bottom of the tree,
they sum at each level, e.g.
• 7% + 4% make up the 11%,
• 11% + 25% make up the
36%
Simple Decision Tree
The model makes its own thresholds if you don’t make all
variables binary
Positive of each condition is to the right and negative to the left
• Rule 1: “< 75 phones per 1000
58. persons”
• In the case “no” = “>=75 phones
per 1000 persons”
• 64% of the countries have >=75
phones per 1000 persons (dark
blue)
• This gives them a (0.92) 92%
chance of having a GDP >=$3000
per capitaOf the countries with < 75 phones
per 1000 persons (36%), only a
0.15 (15%) have a GDP >=$3000
per capita
59. Simple Decision Tree
• Rule 2: “Agricultural workforce >=20%”
• If we split the group with >75 phones per 1000
persons up further into those with an Agricultural
workforce >=20% or not
• We find that 59% of countries have >75 phones
per 1000 persons and an Agricultural workforce
>=20%
• This raises the chance of the country having a
GDP >=$3000 per capita to 0.96, i.e. 96%, given
the two other conditions
60. Simple Decision Tree
• Rule 3: “Birthrate >=29 (thought to be
roughly 29 births per 1000 capita)
• 11% of countries have <75 phones per
1000 capita and a birth rate < 29 both
per 1000 capita
• These would give the countries a 43%
chance of having a GDP >=$3000 per
capita
• 4% of the countries have <75 phones
per 1000 capita and a birth rate < 29
both per 1000 capita and an Agricultural
workforce < 16%. 62% in this category
61. have a GDP >=$3000 per capita
If you look at the “Importance” menu (green) , the order
of importance is phones, birth rate, agriculture
Decision Tree Exploration
• Try some different combinations of predictor variables
and attempt to interpret the results
• You will find that the thresholds change a lot
• Report back to class as needed
This Photo by Unknown Author is licensed under CC BY
http://www.sapelli.org/building-a-simple-decision-tree-with-
sapelli-xml/
https://creativecommons.org/licenses/by/3.0/
62. Vis poverty with satellite data
• If time (or in your own time) look at the report
at
• https://www.kaggle.com/reubencpereira/visua
lizing-poverty-w-satellite-data/report
• and interact with the maps on Kaggle
• You may have to sign in
https://www.kaggle.com/reubencpereira/visualizing-poverty-w-
satellite-data/report
Finance applications of big data and
predictive analytics: risk & return
FINM4100
Analytics in Accounting,
63. Finance and Economics
Week 10
Lesson Learning Outcomes
1 Define risk and return
2 Explore different ways of measuring risk and return
3 Investigate factors influencing risk and return
4 Performing portfolio analytics and optimisation
Why Build Models?
“Just because you
have more data
doesn’t mean that
64. you’re going to make
better decisions.”
Models encapsulate
patterns that exist in
data, helping us make
sense of them.Christina Zhu
Assistant Professor of Accounting
Wharton School of the University of Pennsylvania
Software for today
1. Google Colab
• Either
A. watch the teacher demonstrate analytics and accounting in
python
65. OR
B. you can run the python scripts yourself in Google Colab
• If you want to run the code provided, make sure you have
access
(signed in) to Google Colab https://colab.research.google.com
2. Exploratory
A. watch the teacher demonstrate analytics and accounting in
Exploratory OR
B. run each step yourself online (access is explained on the next
slide)
https://colab.research.google.com/
The risk return relationship is one of
the most fundamental relationships in
all of finance
66. • Return is a measure of the amount
earned by owning an asset
• Risk is a measure of the variability of
that return
To earn more return, an asset owner
must be prepared to accept more risk
The Risk Return Relationship
Photo by Parker Johnson on Unsplash
https://unsplash.com/@pkripperprivate?utm_source=unsplash&u
tm_medium=referral&utm_content=creditCopyText
https://unsplash.com/s/photos/pattern?utm_source=unsplash&ut
m_medium=referral&utm_content=creditCopyText
All investments carry risk, some more than others.
Risk & Return
67. Cash is generally low
risk. Suitable for investors
who have a short-term
investment outlook or low
tolerance for risk.
Shares are the most
volatile asset class, but
historically over long
periods of time have
achieved on average the
highest returns.
68. Risk and return in Australia
Risk and Return for Australian Shares & Bonds from 1974 to
2009
High return, high risk
Medium return, medium risk
Low return, low risk
Average
return
Std
14.34% 21.89%
10.14% 7.66%
9.73% 4.33%
69. How do we measure risk and return?
Return is a
measure of the
earnings made on
an asset
Risk is a measure
of the variability in
earnings made on
an asset
Dollar terms ($)
Percentage terms
(%)
Standard deviation
70. Coefficient of
variation
Beta
Dollar terms ($)
Percentage terms
(%)
• Let’s review the measures of standard deviation and
coefficient of variation
• We saw Beta in week 8
Glossary 1: Variance and Standard
deviation as measures of variability
71. • Measures the squared difference
of a data set relative to its mean.
Variance
• Measures the spread of a data
set relative to its mean.
Standard deviation
Recall from STAM4000 that
Hence, standard deviation is used a
measure of financial risk
Formulas for the variance &
standard deviation
N = population size
n = sample size
72. � = population mean (average)
ҧ� = sample mean (average)
Population Sample
Variance �2=
σ x−� 2
�
�2=
σ x− ҧ� 2
(n−1)
Standard
deviation σ = �2 s = �2
11
Use �2 and s, respectively, as we
73. have a sample.
First, we need ҧ� =
σ �
�
=
6.9−4.8+2.3+2.2+0.6
6
= 1.68%
�2=
σ �− ҧ� 2
(�−1)
so we have
Example of STDEV of returns for the
S&P 500
Month Return
74. October 2021 6.9%
September 2021 -4.8%
August 2021 2.9%
July 2021 2.3%
June 2021 2.2%
May 2021 0.6%
Returns for S&P 500, May 2021-October 2021
�2=
6.9−1.68 2+ −4.8 −1.68 2+ 2.9−1.68 2+ 2.3−1.68 2+ 2.2−1.68
2+ 0.6−1.68 2
(6 −1)
=14.5
Standard deviation, s = 14.5 = 3.8%
https://www.businessinsider.com.au/what-is-standard-deviation
75. Standard deviation measures the variability of possible
outcomes and therefore quantifies uncertainty and risk
%150
Melbourne
investment
Sydney investment
Which investment is riskier – Melbourne or
Sydney?
Quantifying uncertainty and risk
76. • To measure the relationship between average return and
(risk) volatility simultaneously, we use the Coefficient of
Variation (CV):
CV =
�
�
=
Standard Deviation
Annualised Return
• Thus, CV can be used as a measure of asset quality.
• Note that single measures rarely provide the entire picture
but this is a start.
Glossary 2: Coefficient of variation
77. Activity 1: Can you identify the
least/most risky assets?
Investment Risk & Return
RISK
RETURN
Other risk factors and return
Interest Dividend
Capital
Gains
Housing
79. • From the video and previous slides, answer the
following
Q1. Return and risk are measures of what ?
Q2. What is standard deviation used to measure ?
Q3. Are bonds riskier than shares or visa versa?
Q4. What measure maximises return for the same risk?
https://www.youtube.com/watch?v=4KGvoy_Ke9Y
What is a Portfolio?
• A portfolio is a collection of materials, e.g.
career related materials, investments, art
work
• In assessment 3 you will create a portfolio of
analytics methods
• In a risk return context, a portfolio contains
financial investments
80. https://clarke.edu/academics/careers-internships/student-
checklist/resume-writing-and-portfolios/what-is-a-
portfolio/
This Photo by Unknown Author is licensed
under CC BY-NC-ND
This Photo by Unknown Author is licensed under CC BY-
SA-NC
This Photo by Unknown Author is licensed under CC BY-NC-
ND
http://ezdesigns.deviantart.com/art/Portfolio-design-190112229
https://creativecommons.org/licenses/by-nc-nd/3.0/
https://www.peoplematters.in/article/hr-analytics/7-
fundamentals-scale-hr-analytics-capabilities-12634
https://creativecommons.org/licenses/by-nc-sa/3.0/
http://dollarsandsense.sg/a-simple-strategy-to-create-an-easy-to-
manage-investment-portfolio/
https://creativecommons.org/licenses/by-nc-nd/3.0/
81. Risk and diversification for an
investment portfolio
In the same way that particular measures apply
to single stocks, they can also be applied to a
portfolio
• Standard deviation captures uncertainty
• Coefficient of variation standardises risk
• Beta measures systematic risk
Diversification refers to correlation reducing
portfolio standard deviation. Hence we seek to
have some uncorrelated (or imperfectly
correlated) investments.Photo by Michel Porro on Unsplash
83. • A ratio of 3.0 or higher is considered excellent
• A ratio under 1.0 is considered sub-optimal
• Sharpe ratio can be compared with Coeff. of Var. to make an
assessment on asset quality and performance.
Glossary 3: Sharpe Ratio
Activity 3: Quick Quiz
Q1. What mathematical methods are commonly used to measure
risk ?
Q2. Consider
Investment A and Investment B
• Portfolio return: 20% Portfolio return: 30%
• Risk free rate: 10% Risk free rate: 10%
84. • Standard Deviation: 5 Standard Deviation: 40
If the Sharpe ratios are (A) 2.0 and (B) 5.0, Confirm this from
the formula and
interpret these outcomes.
Q3. Is diversification useful in a portfolio or do you just need
more
investments?
Glossary 4: Skewness and Kurtosis
• Skewness and Kurtosis which you may have encountered in
STAM4000
are also measures of risk for investments
“Skewness is a measure of symmetry, or the lack of it.
T
h
is
87. are heavy-tailed or light-tailed relative to a
normal distribution. ”
https://en.wikipedia.org/wiki/Skewness
https://creativecommons.org/licenses/by-sa/3.0/
http://stats.stackexchange.com/questions/84158/how -is-the-
kurtosis-of-a-distribution-related-to-the-geometry-of-the-
density-fun
https://creativecommons.org/licenses/by-sa/3.0/
Activity 4: Portfolio calculations
• Make sure you are signed up with Google Colab or watch the
demo
• We start with a portfolio of four stocks (Google, Amazon,
MacDonalds,
The Walt Disney Company) and then start adding Australian
stocks to
see how the measures of risk change.
• Expected return, volatility, Sharpe ratio, skewness and
kurtosis are
88. calculated each time.
• The script is here
https://colab.research.google.com/drive/1T7sS1KLo_WcwyLKn
KBmZtaso6
cBsSzSQ?usp=sharing
• All you need to do is run each block of code and attempt to
interpret the
results with your teacher
https://colab.research.google.com/drive/1T7sS1KLo_WcwyLKn
KBmZtaso6cBsSzSQ?usp=sharing
Glossary 5: Annualised return
• The annualized return equates to what you would earn if the
annual
return was compounded over a period of time.
• It is the geometric average of an investment’s earnings in a
year
89. This Photo by Unknown Author is licensed under
CC BY-SA-NC
http://www.xaktly.com/ProbStat_Averages.html
https://creativecommons.org/licenses/by-nc-sa/3.0/
• There are various analytics methods for portfolio optimisation
• In broad terms, we seek to find the minimum (volatility)
variance
portfolio for a given selection of investments, i.e. perform
mean-
variance optimisation.
• Requirements and conditions for mean-variance optimisation:
Portfolio optimisation
Minimise
Portfolio
90. Covariance
Define Acceptable
Portfolio Return
Fully Allocate
Budgeted Capital
Set Capital
Allocation
Constraints
For example, consider a four security portfolio.
• BHP Billiton, QBE Insurance, Telstra and Westpac Banking
Corporation
Question: In what proportions should these investments be held
91. such
that the risk (volatility), measured using standard deviation, is
minimised
for a given level of return?
That is, how do we make a minimum variance portfolio?
Portfolio Optimisation contd…
Portfolio 1: Equal allocation…
Mean = 23.96% | Standard Deviation = 16.24%
Portfolio 2: Financials heavy…
Mean = 12.49% | Standard Deviation = 21.76%
Portfolio 3: Me heavy…
Mean = 11.73% | Standard Deviation = 19.67%
92. Attempts to create a min var
portfolio
Portfolio Efficient Frontier
• Efficient Frontier method: An optimisation method which
takes into
account volatility and Sharpe ratio
• The idea of an efficient frontier comes from Modern Portfolio
theory
• The frontier is a curve representing a set of portfolios which
provide the
greatest returns for each level of risk
This Photo by Unknown Author
is licensed under CC BY-SA-
93. NC
https://bogleheads.es/foro/viewtopic.php?f=4&t=673
https://creativecommons.org/licenses/by-nc-sa/3.0/
• Using the Efficient Frontier, the portfolio can be optimised for
– minimum volatility
– maximum Sharpe ratio
– minimum volatility for a given target return
– maximum Sharpe ratio for a given target volatility
• We have found a python script which uses the Efficient
Frontier method
• This allows us to compute and visualise optimised portfolios
Portfolio Efficient Frontier
This Photo by Unknown Author is
94. licensed under CC BY-ND
https://www.quoteinspector.com/images/investing/pie-area-
chart/
https://creativecommons.org/licenses/by-nd/3.0/
Activity 5: Efficient Frontier
• Make sure you are signed up with Google Colab or watch the
demo
• The script is here
https://colab.research.google.com/drive/1FiwNZKvvVLLWEpH
RX1plnLjS
zam7kwmb?usp=sharing
• Discuss the results of the different optimisation criteria with
your
teacher
• Example output next page
95. https://finquant.readthedocs.io/en/latest/examples.html
This Photo by Unknown Author is
licensed under CC BY
https://colab.research.google.com/drive/1FiwNZKvvVLLWEpH
RX1plnLjSzam7kwmb?usp=sharing
https://www.scirp.org/journal/PaperInformation.aspx?PaperID=
80120
https://creativecommons.org/licenses/by/3.0/
Of the portfolios that comprise the efficient frontier, there is
one portfolio
that had the lowest level of risk…
Risk & Return
�
�
They called it, the Minimum Variance Portfolio
96. Efficient Frontier Output
FINM4100
Analytics in Accounting,
Finance and Economics
Week 8
Data analytics techniques and applications in
accounting, finance and economics
Lesson Learning Outcomes
1 Explore and apply some of the widely used data
97. analytics techniques which are used to extract
insights in accounting, finance and economics, e.g.
• Association rule learning
• Classification tree analysis
• Genetic algorithms
• Machine learning
• Regression analysis
Software for today
1. Google Colab
• Either
A. watch the teacher demonstrate analytics and accounting in
python
98. OR
B. you can run the python scripts yourself in Google Colab
• If you want to run the code provided, make sure you have
access
(signed in) to Google Colab https://colab.research.google.com
2. Exploratory
A. watch the teacher demonstrate analytics and accounting in
Exploratory OR
B. run each step yourself
https://colab.research.google.com/
Data for today
1. GroceryStoreDataSet.csv
2. Churn_Modelling.csv
3. Salary_Data.csv
99. This Photo by Unknown Author is licensed under CC BY-SA-
NC
https://www.peoplematters.in/blog/recruitment/how-data-
analytics-is-revolutionizing-recruitment-28683
https://creativecommons.org/licenses/by-nc-sa/3.0/
A Vital Commodity
“It is a capital mistake to
theorize before one has
data.”
Sir Arthur Conan Doyle
Author
Sherlock Holmes
100. The Big Data Environment
216,000TB
Amount of new information
generated per person per year
90%
Proportion of the world’s total
big data created in the past 3
years.
$65 million
Boost in net income for every
Fortune 1000 company (if
data access is boosted 10%)
83%
Proportion of surveyed
businesses (Accenture)
101. investing in Big Data
initiatives.
Inevitable Transition
Force multiplier - Big data analytics and analytics
infrastructure is the means by which institutions apply force to
achieve geo-economic advantage.
Commercial activities will increasing relay on sophisticated
network-based logistics, communications systems and a big
data ecology to recommend products, retain customers and
mitigate churn.
The goal is to turn data into information, and information into
102. insight.
Techniques
There are a number of widely used analysis techniques to
extract valuable insights from data.
• Association rule learning
• Classification tree analysis
• Genetic algorithms
• Machine learning
• Regression analysis
This Photo by Unknown Author is licensed under CC BY-SA-
NC
https://ocw.tudelft.nl/courses/big-data-strategies-transform-
business/
103. https://creativecommons.org/licenses/by-nc-sa/3.0/
Association Rule Learning
Association rule learning is a method for discovering interesting
correlations between variables in large databases. It was first
used by
major supermarket chains to discover interesting relations
between
products, using data from supermarket point-of-sale (POS)
systems.
“Are people who purchase tea more or less
likely to purchase carbonated drinks?”
Association Rule Learning
Association rule learning is used to:
104. • place (correlated) products in better proximity to each other
in order to increase sales
• Determine data quality in accounting
• Help in investment planning
• monitor system logs to detect intruders and malicious
activity
• provide insight in revenue analysis
T
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This Photo by Unknown Author is licensed under CC BY-NC-
ND
https://researchoutreach.org/articles/value-added-data-systems-
architecture-end-user-informed-data-preparation/
https://creativecommons.org/licenses/by/3.0/
http://www.flickr.com/photos/hmtreasury/4723319199/
https://creativecommons.org/licenses/by-nc-nd/3.0/
Association coding concepts
“The Apriori Algorithm, used for the first phase of the
Association Rules, is the most
popular and classical algorithm in the frequent old parts. These
107. algorithm properties and
data are evaluated with Boolean Association Rules. In this
algorithm, there are product
clusters that pass frequently, and then strong relationships
between these products and
other products are sought.
Three main parameters that are used to identify the strength of
the algorithm are
Activity 2: Python in Colab
• Make sure you have access (signed in) to Colab
https://colab.research.google.com
• Click on the ‘File’ menu and select ‘New notebook’
https://colab.research.google.com/
Activity 2: Python in Colab
We have grocery store data for you to analyse
108. • The code is given below. All you have to do is click on the
arrows and run the
code
• NOTE: you don’t need to run the interpretation text at the end
it is just to help
you interpret the results
•
https://colab.research.google.com/drive/1Qg0qokW_oDUI6xU8
gvmZeV6AiMo
6bhxu?usp=sharing
• We start by getting you to choose to upload the
GroceryStoreDataSet.csv file
on MyKBS
(You will be prompted to Choose (find) the data file from where
it is
stored on your device)
109. https://colab.research.google.com/drive/1Qg0qokW_oDUI6xU8
gvmZeV6AiMo6bhxu?usp=sharing
Activity 2: Output
Interpretation
# The probability of seeing sugar sales is seen as 30%.
# Bread intake is seen as 65%.
# We can say that the support of both of them is measured as
20%.
# 67% of those who buys sugar, buys bread as well.
# Users who buy sugar will likely consume 3% more bread than
users who don't buy sugar.
# Their correlation with each other is seen as 1.05.
# As a result, if item X and Y are bought together more
frequently, then several steps can be take
110. n to increase the profit.
Glossary 1: What are Bonds and
mortgage-backed security (MBS) ?
• Securitisation is about pooling debt (such as mortgages) and
selling
their cash flows, as securities, to third party investors
• A bond is a fixed income security that provides a return in the
form of
fixed interest payments made at regular intervals over time
• A mortgage-backed security (MBS) is an investment similar to
a
bond. A MBS consists of a bundle of loans sold to investors.
• The bundles are rated between AAA (best, debts most likely to
be paid
back) through to “not rated” (worst)
111. • The bank effectively becomes an intermediary between a
person with a
mortgage and investors. See next slide
Risk Ratings
Can machine learning help classify items for investment?
Classification Tree Analysis
YES! Classification, a machine learning method can be used to
classify debt
• Statistical classification is a method of identifying categories
that a
new observation belongs to. It requires a training set of
correctly
identified observations – historical data in other words.
• Classifying customers correctly will maximise sales and
112. minimise
expenses (cost of acquisition, discounts, bad debt etc).
“Are these mortgages investment grade or sub-prime?”
AAA BBB D
Classification Tree Analysis
Statistical classification is also being used to:
• automatically assign financial documents to
categories;
• categorize customers into groupings (e.g.
insurance);
• classify transactions
This Photo by Unknown Author is licensed under CC BY-NC
113. https://www.freepngimg.com/png/48807-exchange-png-file-hd
https://creativecommons.org/licenses/by-nc/3.0/
Activity 3: Decision Trees
• Decision trees that classify items into categories are called
“Classification tree”
• Decision trees that predicts numerical values is called
“Regression tree”
Watch the video at
https://www.youtube.com/watch?v=zs6yHVtxyv8
From groups,
• Suppose that you are an analyst at the tax office. You wish to
identify which of
your clients is most likely to avoid lodging a tax return form
and thus avoid
paying tax (or even recouping funds after paying too much tax)
114. 1. Discuss the idea of using a classification tree for this pur pose
2. How would you limit so-called “overfitting”?
3. What kind of data would you collect for the classification
tree?
https://www.youtube.com/watch?v=zs6yHVtxyv8
Genetic Algorithms
Genetic algorithms are inspired by the way evolution works –
that is,
through mechanisms such as inheritance, mutation and natural
selection.
These mechanisms are used to “evolve” useful solutions to
problems that
require optimization.
“Which TV programs should we offer viewers,
115. and in what time slot, to maximize viewership?”
Genetic Algorithms
• A biology- inspired algorithm which reflects natural selection
(the fittest
individuals survive)
• Technically an optimisation method
• It has three main rules:
selection
crossovermutation
evaluation
This Photo by Unknown Author is licensed under CC BY-SA
1. “Selection rules select the
individuals, called parents, that
116. contribute to the population at the
next generation.”
2. Crossover rules represent
reproduction, i.e. combining two
parents to form children.
3. Mutation rules apply random
changes to individual parents to
create genetic diversity in children.
https://leblancfg.com/higher-level-functions-python-reduce.html
https://creativecommons.org/licenses/by-sa/3.0/
Genetic Algorithms
Genetic algorithms are being used in:
• Finance:
117. – Algorithmic trading;
– Financial statement fraud
• In accounting
– Distribution problems assigning sources to destinations
– Bankruptcy predictions
• The cobweb model in economics which explains
why prices may fluctuate in certain markets.
This Photo by Unknown
Author is licensed under
CC BY
http://brainz.org/15-real-world-applications-genetic-algorithms/
http://www.blacklistednews.com/Mysterious_Algorithm_Was_4
%25_of_Trading_Activity_Last_Wee k/21915/0/38/38/Y/M.html
https://creativecommons.org/licenses/by/3.0/
118. Activity 4: Genetic Algorithms
• Here is a video with a real-world examples of a genetic
algorithms.
Watch the video at
https://www.youtube.com/watch?v=ziMHaGQJuSI
Form groups and answer the following,
Q1. What issues do genetic algorithms appear to have at the
start?
Q2. What are the three rules used here?
Q3. What applications are shown here?
Q4. How could this be used in accounting and finance?
https://www.youtube.com/watch?v=ziMHaGQJuSI
Machine Learning
119. Machine learning includes software that can ‘learn’ from data
and generate
adaptive solutions. It gives computers the ability to compute
solutions
without being explicitly programmed along a strict instruction
set.
Applications are primarily focused on making predictions based
on known
properties learned from sets of ‘training data’.
“What other products would this customer likely
purchase, based on their transaction history?”
Extract Transform Test Validate
Machine Learning
120. Machine learning is being used to:
• distinguish between spam and non-spam email
messages;
• learn invoice coding behaviours for allocation
purposes
• determine the best content for engaging
prospective customers;
• run AI chatbots for customer enquiries
This Photo by Unknown Author is licensed under CC BY-NC-
ND
https://www.cittadiniditwitter.it/news/il-maxxi-lancia-un-
chatbot-che-guida-i-visitatori-alla-scoperta-delle-collezioni/
https://creativecommons.org/licenses/by-nc-nd/3.0/
Activity 5: Customer churn example
121. Source: https://www.kaggle.com/kmalit/bank-customer-churn-
prediction
• Watch the demo by your teacher or run the code for analysis
of
customer churn at
https://colab.research.google.com/drive/1Sgro8G9o2UtErsiEMG
-
UOe7yS-JQMqUU?usp=sharing
• Data for this script is Churn_Modelling.csv
• NOTE: This is a part of a project on Kaggle, so we took a
small section
of it to give you an appreciation of this technique
• Interpret your findings. For example, regarding churn, is there
any
difference depending on the country of origin of customers,
122. gender,
ownership of a credit card or whether or not a member is
active?
https://colab.research.google.com/drive/1Sgro8G9o2UtErsiEMG
-UOe7yS-JQMqUU?usp=sharing
Regression Analysis
• Regression analysis involves manipulating one or more
independent
variables (i.e. number of customers) to see how they influence a
dependent variable (i.e. weekly sales).
• The dependent variable is also called a target variable
• The independent variable is also called a predictor variable
“How would social, biological, demographic and
lifestyle factors affect health insurance premiums?”
124. Estimated (or
predicted) Y value for
observation i
Value of X for observation
i��� = �� + �� ��
Simple linear regression equation
for estimating values
• Example: ������� ����� = 98.248 + 0.110 Number of
customers
• Weekly sales is the target variable,
• Number of customers is a predictor variable
Simple linear regression equation
125. for estimating values
• Example: ������� ����� = 98.248 + 0.110 Number of
customers
• Weekly sales is the target variable,
• Number of customers is a predictor variable
0
50
100
150
200
250
300
0 500 1000 1500 2000
127. – Demand curves
– Predicting economic growth rate
• In Finance:
– Forecasting, e.g. revenues from Ads
– Bank performance given multiple variables
– levels of customer satisfaction affect customer loyalty
• In accounting:
– to estimate fixed and variable costs
– Cost versus hours worked
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This Photo by Unknown Author is licensed under CC BY-SA
http://www.ccpixs.com/ccimages/3d-growing-revenue-
graph/1192/
https://creativecommons.org/licenses/by/3.0/
132. Indiv Stock
Field: Indiv Stock and Field: Market appear highly correlated.
Other types of regression
This Photo by Unknown Author is licensed under CC BY-SA
T
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134. C
B
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-S
A
Polynomial regression
3-D regression movie
https://devopedia.org/types-of-regression
https://creativecommons.org/licenses/by-sa/3.0/
http://stackoverflow.com/questions/11949331/adding-a-3rd-
order-polynomial-and-its-equation-to-a-ggplot-in-r
https://creativecommons.org/licenses/by-sa/3.0/
Activity 6: Salary regression model
• We will look at a simple model of how salary is related to
years of work
135. experience.
• Data for this activity in Exploratory is Salary_Data.csv
• Open Exploratory and create a new project called Salary
analysis
• Use the Data Frames menu to load the Salary_Data.csv file
and save it
Activity 6: Salary regression model
• The Summary in Exploratory shows the distribution of the two
variables
• Click on the Analytics menu (in Green)
• Go to the model ‘Type’ menu
• Choose ‘Linear regression’ as the type
of model you want
136. • Choose ‘Salary’ as the Target variable
• Choose ‘YearExperience’ as the
predictor variable and run
Activity 6: Salary regression model
• Interpret the output in a general sense
• Click on ‘Coef. Table’ to see the values
of the coefficients for the regression
equation
• The equation is
• ������� = 25,792 + 9,449 YearsExperience
• You can make estimates from this by
substituting numbers for Years of
137. experience, e.g. 5 years of experience
gives you an estimate of
• ������� = 25,792 +9,449*5 = $73,037
• You will learn more detail on this in week 9 of
STAM4000
Create a slide deck which represents a portfolio of analytics
methods used of accounting, economics or finance. This task is
to be done as an individual. 16 slides, total 30 marks.
Assessment Description
You will discuss below five analytics methods and a financial
or accounting or economics application for each one.
· Association rule learning
· Classification tree analysis
· Genetic algorithms
· Machine learning
· Regression analysis
• Out of the five methods that you chose, investigate one in
138. more detail.
• Reflect on the limitations of the methods and possible ethical,
legal or privacy issues.
Please refer to the assessment marking guide to assist you in
completing all the assessment criteria.
Slide format should be as follows:
• Title, student name and ID [1 slide]
• Discuss any 4 analytics methods from above. Create one slide
for each analytics method and one for its application in
accounting or finance or economics. [8 slides, 16 marks]
• Discuss the remaining 1 Analytics method in detail and create
three slides for the analytics method and one slide for its
application in accounting or economics or finance [4 slides, 8
marks]
• Reflect and list the limitations of the 5 analytics methods [1
slides, 2 marks]
• Discuss in short sentences possible ethical, legal and privacy
issues. Please refer to lecture slide week 11. [2 slides, 4 marks]