A Comparative Study for Credit Card Fraud Detection System using Machine Lear...IRJET Journal
This document presents a comparative study of machine learning models for credit card fraud detection. It discusses various machine learning and deep learning techniques used for credit card fraud detection systems, including neural networks, decision trees, logistic regression, random forests, convolutional neural networks, and more. It reviews related literature on using meta learning and neural networks for fraud detection. The paper aims to compare the performance of these different models for credit card fraud detection using datasets from banks containing labeled fraudulent and non-fraudulent transactions.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Fraud App Detection using Machine LearningIRJET Journal
This document proposes a system to detect fraudulent mobile apps using machine learning. It analyzes apps based on four parameters: in-app purchases, ads, user ratings, and reviews. Three machine learning models are compared: Decision Tree, Logistic Regression, and Naive Bayes. The model with the highest accuracy for predicting fraudulent apps based on these parameters will be selected. Prior research on detecting app ranking fraud and fraudulent apps using sentiment analysis is reviewed. The proposed system architecture involves collecting data on apps from the Google Play Store and using machine learning to classify apps as fraudulent or not fraudulent.
Automated anti money laundering using artificial intelligence and machine lea...Santhosh L
1) The document discusses how machine learning and artificial intelligence can be used to automate and improve anti-money laundering processes at financial institutions.
2) Key applications discussed include using machine learning for alert routing to reduce false positives, anomaly detection to identify unusual transaction patterns, and data aggregation to create a unified customer view.
3) The document also discusses how robotic process automation can be used to automate know-your-customer checks and other compliance processes but has limitations that machine learning may be better suited for.
IRJET - Fraud Detection in Credit Card using Machine Learning TechniquesIRJET Journal
This document discusses machine learning techniques for detecting credit card fraud. It begins with an abstract that outlines how credit card fraud causes major financial losses and how machine learning can help tackle this issue. It then provides background on credit card fraud and challenges in detecting it. The document describes the methodology used, including collecting transaction data, exploring relationships between features, and training models like random forests, decision trees, and support vector machines to classify transactions as fraudulent or legitimate. It finds these models achieved high accuracy scores between 99.7-99.8% but had low precision. The conclusion states that future work could focus on improving precision and considering additional algorithms and data processing techniques.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
Advantages And Disadvantages Of Cyber-Crime In The HotelPatty Buckley
This document discusses cybercrime and transaction processing systems in hotels. It defines cybercrime and lists some types that could affect hotels, such as hacking reservations or stealing credit card numbers. It also defines a transaction processing system (TPS) as a system that processes transactions, gives examples of what it could be used for in a hotel like payroll and purchases, and lists advantages like cost effectiveness and increased efficiency and disadvantages like costs and need for maintenance. It recommends a hotel implement a TPS to help with transactions.
A Comparative Study for Credit Card Fraud Detection System using Machine Lear...IRJET Journal
This document presents a comparative study of machine learning models for credit card fraud detection. It discusses various machine learning and deep learning techniques used for credit card fraud detection systems, including neural networks, decision trees, logistic regression, random forests, convolutional neural networks, and more. It reviews related literature on using meta learning and neural networks for fraud detection. The paper aims to compare the performance of these different models for credit card fraud detection using datasets from banks containing labeled fraudulent and non-fraudulent transactions.
An Identification and Detection of Fraudulence in Credit Card Fraud Transacti...IRJET Journal
This document summarizes a research paper that proposes a system for detecting fraudulent credit card transactions using data mining techniques. The system uses the Apriori algorithm to perform frequent item set mining on a credit card transaction dataset. It then uses the Support Vector Machine (SVM) classification method to match new transactions to either a legal transaction pattern database or a fraudulent transaction pattern database that was formed based on users' previous transactions. The results showed this proposed method achieved better fraud detection with a lower false alarm rate than existing methods like Hidden Markov Models.
Fraud App Detection using Machine LearningIRJET Journal
This document proposes a system to detect fraudulent mobile apps using machine learning. It analyzes apps based on four parameters: in-app purchases, ads, user ratings, and reviews. Three machine learning models are compared: Decision Tree, Logistic Regression, and Naive Bayes. The model with the highest accuracy for predicting fraudulent apps based on these parameters will be selected. Prior research on detecting app ranking fraud and fraudulent apps using sentiment analysis is reviewed. The proposed system architecture involves collecting data on apps from the Google Play Store and using machine learning to classify apps as fraudulent or not fraudulent.
Automated anti money laundering using artificial intelligence and machine lea...Santhosh L
1) The document discusses how machine learning and artificial intelligence can be used to automate and improve anti-money laundering processes at financial institutions.
2) Key applications discussed include using machine learning for alert routing to reduce false positives, anomaly detection to identify unusual transaction patterns, and data aggregation to create a unified customer view.
3) The document also discusses how robotic process automation can be used to automate know-your-customer checks and other compliance processes but has limitations that machine learning may be better suited for.
IRJET - Fraud Detection in Credit Card using Machine Learning TechniquesIRJET Journal
This document discusses machine learning techniques for detecting credit card fraud. It begins with an abstract that outlines how credit card fraud causes major financial losses and how machine learning can help tackle this issue. It then provides background on credit card fraud and challenges in detecting it. The document describes the methodology used, including collecting transaction data, exploring relationships between features, and training models like random forests, decision trees, and support vector machines to classify transactions as fraudulent or legitimate. It finds these models achieved high accuracy scores between 99.7-99.8% but had low precision. The conclusion states that future work could focus on improving precision and considering additional algorithms and data processing techniques.
Microservices are an effective approach to orchestrate services in the cloud. The microservices architectural style is an approach to develop a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms ( API ).
To be more effective they need a contextual evaluation of the meaning of data of IoT generating always more data.Machine Learning can support Microservices to extract meaning from Big Data making Microservices smarter and speedier. Industries can have huge benefits from this approach.
Advantages And Disadvantages Of Cyber-Crime In The HotelPatty Buckley
This document discusses cybercrime and transaction processing systems in hotels. It defines cybercrime and lists some types that could affect hotels, such as hacking reservations or stealing credit card numbers. It also defines a transaction processing system (TPS) as a system that processes transactions, gives examples of what it could be used for in a hotel like payroll and purchases, and lists advantages like cost effectiveness and increased efficiency and disadvantages like costs and need for maintenance. It recommends a hotel implement a TPS to help with transactions.
MAPREDUCE IMPLEMENTATION FOR MALICIOUS WEBSITES CLASSIFICATIONIJNSA Journal
Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites — some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now
seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using Map Reduce to make the classification speeds faster than the traditional approaches.[2].
MAPREDUCE IMPLEMENTATION FOR MALICIOUS WEBSITES CLASSIFICATIONIJNSA Journal
This document summarizes research on using MapReduce to classify websites as malicious or benign more efficiently than traditional approaches. The researchers used a dataset from Kaggle containing network and application features of websites to train and evaluate machine learning models. They preprocessed the data to handle missing values and encode categorical features. Models tested included neural networks, random forests, and decision trees. Random forests achieved 100% accuracy when trained on 30% of the data and were much faster than other models. Processing time was improved when using Apache Spark on two nodes compared to a single node or traditional programming, and further speedups could be gained with more nodes.
"Risk Management in Open Finance Era" 26-12-2020Varlam Ebanoidze
"Risk Management in Open Finance Era"
This presentation on "Risk Management in Open Finance Era" is an attempt to visualize a New Operational Risk & Information Security strategies through industry development lenses, and simultaneously to "Zoom" into the details of operations, threats, and technical enablers for sound risk management to FIT the new paradigm of 'Open Finance'.
For example: to ensure a #ZeroTrust’ strategy and #ComposableArchitectures or even help the business to accelerate by ‘Capitalizing’ on Risk Data Value Chain and on #DifferentialPrivacy.
#RiskTech 4 #FinTech
How to build a highly secure fin tech applicationnimbleappgenie
Indeed, The FinTech industry is a specific sector where developing a successful mobile solution necessitates some extraordinary measures to capture clients’ loyalty. The takeaway is that a good FinTech app is more than simply an excellent companion.
with great enthusiasm Insights Success has
shortlisted The 10 Most Trusted Fraud Detection
Solution Providers, 2019, who are working round the
clock to help is clients detect fraud, faster!
Why machine learning is the best way to reduce fraud GlobalTechCouncil
Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. Sometimes, even more efficiently.
The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc.
Bank offered rate based on Artificial IntelligenceIJAEMSJORNAL
The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and AI processing are mandatory for many use cases. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
This essay contends that rather than a future of “Models will Run the World,” the route to AI software creates a focus on intelligent data. To move towards the latter, humans will need to contribute their judgement to how data is organized for machine learning to train algorithms. They will decide what biases may be included in the training data and check for any issues that might arise from these biases once algorithms are run in production.
To achieve success in this “intelligent data” world, humans will play a very different role in the workforce. Jobs will shift to those that support, conserve and evaluate the results that algorithms provide. They may also expand in “domain expertise” areas, as where knowledge of regulatory requirements for finance needs to be incorporated in new models that financial institutions want to create and the algorithms they need to run.
u
IRJET- Online Crime Reporting and Management System using Data MiningIRJET Journal
This document describes an online crime reporting and management system that uses data mining. The system allows users and police to file crime reports, complaints, and missing person reports online. It notifies users if they enter a high crime alert area and provides crime rates by area. The system registers complaints through a web app where users can upload images/videos. It uses KNN, AES encryption, and K-means algorithms. The goal is to help law enforcement track and solve crimes more efficiently.
The document proposes an online credit card fraud detection and prevention system using machine learning algorithms like random forest, decision trees, and others to classify transactions as normal or fraudulent. It discusses limitations in existing fraud detection systems and outlines the proposed system which will use a random forest algorithm to detect fraud during transactions and prevent fraudulent transactions from occurring. The proposed system aims to provide higher accuracy and security compared to existing fraud detection systems.
This document discusses fraud and risk in the context of big data. It begins by defining big data and providing examples of how large companies like Walmart and Facebook handle massive amounts of data daily. It then discusses different types of fraud that can occur, such as credit card fraud and fraud on social media. Finally, it discusses risk management and how credit and market risk analytics are used to analyze past data to predict future risks. In summary, the document outlines the opportunities and challenges of using big data for fraud detection and risk management.
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Rebooting IT Infrastructure for the Digital AgeCapgemini
The Digital Transformation Institute has launched its latest research report titled “Faster, Better, Smarter: Rebooting IT Infrastructure for the Digital Age.” The report highlights why organizations need robust and seamless IT infrastructure that keeps pace with evolving market and technology demands. IT infrastructure has always been known as a “keeping the lights on” function but now it has evolved into a core catalyst of Digital Transformation. However, as a function, IT infrastructure is yet to undergo a core transformation. The report discusses why a reboot is critical.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
The document discusses the menstrual cycle as a natural process experienced by females. It notes that in the past, most females began menstruating in their late teens or early twenties, but some now start as young as age nine. The first occurrence of menstruation, known as menarche, marks the onset of sexual maturity and is an important developmental milestone.
The document discusses the menstrual cycle as a natural process experienced by females. It notes that in the past, most females began menstruating in their late teens or early twenties, but some now start as young as age nine. The first occurrence of menstruation, known as menarche, marks the onset of sexual maturity and is an important developmental milestone.
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...IRJET Journal
This document summarizes a study that used machine learning and Python to detect online transaction fraud. It describes how online transactions and fraud are increasing. The study used a real credit card dataset to train models like KNN, NB, and SVM to detect fraudulent transactions based on user behavior patterns and restrict fraudulent users after three failed attempts. The goal was to develop a system that can detect fraud in real-time and prevent losses for banks and credit card users.
TWO-LAYER SECURE PREVENTION MECHANISM FOR REDUCING E-COMMERCE SECURITY RISKSijcsit
E-commerce is an important information system in the network and digital age. However, the network intrusion, malicious users, virus attack and system security vulnerabilities have continued to threaten the operation of the e-commerce, making e-commerce security encounter serious test. How to improve ecommerce security has become a topic worthy of further exploration. Combining routine security test and
security event detection procedures, this paper proposes the Two-Layer Secure Prevention Mechanism (TLSPM). Applying TLSPM, routine security test procedure can identify security vulnerability and defect,and develop repair operations. Security event detection procedure can timely detect security event, and assist follow repair. TLSPM can enhance the e-commerce security and effectively reduce the security risk
of e-commerce critical data and asset.
A Review of deep learning techniques in detection of anomaly incredit card tr...IRJET Journal
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
Attaining Expertise
You are training individuals you supervise on how to attain expertise in your field.
Write
a 1,050- to 1,200-word paper on the processes involved with attaining expertise, using your assigned readings in Anderson. Explain how these processes apply to attaining expertise in your current field or in the field you plan to enter. Focus on the cognitive processes that are involved in mastering knowledge and skills.
Include
a title page and references list consistent with APA guidelines.
Click
the Assignment Files tab to submit your assignment.
.
attachment Chloe” is a example of the whole packet. Please follow t.docxcelenarouzie
This document provides instructions for writing a PR packet that includes a pitch letter, news release, feature release, fact sheet, executive biography, and media alert following the example and format provided in the attachment. The writer has already completed the news release part of the packet and included it in the attached example for reference in completing the rest of the packet.
More Related Content
Similar to Available online at www.sciencedirect.comhttpwww.keaipubl.docx
MAPREDUCE IMPLEMENTATION FOR MALICIOUS WEBSITES CLASSIFICATIONIJNSA Journal
Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites — some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now
seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using Map Reduce to make the classification speeds faster than the traditional approaches.[2].
MAPREDUCE IMPLEMENTATION FOR MALICIOUS WEBSITES CLASSIFICATIONIJNSA Journal
This document summarizes research on using MapReduce to classify websites as malicious or benign more efficiently than traditional approaches. The researchers used a dataset from Kaggle containing network and application features of websites to train and evaluate machine learning models. They preprocessed the data to handle missing values and encode categorical features. Models tested included neural networks, random forests, and decision trees. Random forests achieved 100% accuracy when trained on 30% of the data and were much faster than other models. Processing time was improved when using Apache Spark on two nodes compared to a single node or traditional programming, and further speedups could be gained with more nodes.
"Risk Management in Open Finance Era" 26-12-2020Varlam Ebanoidze
"Risk Management in Open Finance Era"
This presentation on "Risk Management in Open Finance Era" is an attempt to visualize a New Operational Risk & Information Security strategies through industry development lenses, and simultaneously to "Zoom" into the details of operations, threats, and technical enablers for sound risk management to FIT the new paradigm of 'Open Finance'.
For example: to ensure a #ZeroTrust’ strategy and #ComposableArchitectures or even help the business to accelerate by ‘Capitalizing’ on Risk Data Value Chain and on #DifferentialPrivacy.
#RiskTech 4 #FinTech
How to build a highly secure fin tech applicationnimbleappgenie
Indeed, The FinTech industry is a specific sector where developing a successful mobile solution necessitates some extraordinary measures to capture clients’ loyalty. The takeaway is that a good FinTech app is more than simply an excellent companion.
with great enthusiasm Insights Success has
shortlisted The 10 Most Trusted Fraud Detection
Solution Providers, 2019, who are working round the
clock to help is clients detect fraud, faster!
Why machine learning is the best way to reduce fraud GlobalTechCouncil
Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. Sometimes, even more efficiently.
The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc.
Bank offered rate based on Artificial IntelligenceIJAEMSJORNAL
The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and AI processing are mandatory for many use cases. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
This essay contends that rather than a future of “Models will Run the World,” the route to AI software creates a focus on intelligent data. To move towards the latter, humans will need to contribute their judgement to how data is organized for machine learning to train algorithms. They will decide what biases may be included in the training data and check for any issues that might arise from these biases once algorithms are run in production.
To achieve success in this “intelligent data” world, humans will play a very different role in the workforce. Jobs will shift to those that support, conserve and evaluate the results that algorithms provide. They may also expand in “domain expertise” areas, as where knowledge of regulatory requirements for finance needs to be incorporated in new models that financial institutions want to create and the algorithms they need to run.
u
IRJET- Online Crime Reporting and Management System using Data MiningIRJET Journal
This document describes an online crime reporting and management system that uses data mining. The system allows users and police to file crime reports, complaints, and missing person reports online. It notifies users if they enter a high crime alert area and provides crime rates by area. The system registers complaints through a web app where users can upload images/videos. It uses KNN, AES encryption, and K-means algorithms. The goal is to help law enforcement track and solve crimes more efficiently.
The document proposes an online credit card fraud detection and prevention system using machine learning algorithms like random forest, decision trees, and others to classify transactions as normal or fraudulent. It discusses limitations in existing fraud detection systems and outlines the proposed system which will use a random forest algorithm to detect fraud during transactions and prevent fraudulent transactions from occurring. The proposed system aims to provide higher accuracy and security compared to existing fraud detection systems.
This document discusses fraud and risk in the context of big data. It begins by defining big data and providing examples of how large companies like Walmart and Facebook handle massive amounts of data daily. It then discusses different types of fraud that can occur, such as credit card fraud and fraud on social media. Finally, it discusses risk management and how credit and market risk analytics are used to analyze past data to predict future risks. In summary, the document outlines the opportunities and challenges of using big data for fraud detection and risk management.
Introduction
Big Data may well be the Next Big Thing in the IT world.
Big data burst upon the scene in the first decade of the 21st century.
The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Face book were built around big data from the beginning.
Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Rebooting IT Infrastructure for the Digital AgeCapgemini
The Digital Transformation Institute has launched its latest research report titled “Faster, Better, Smarter: Rebooting IT Infrastructure for the Digital Age.” The report highlights why organizations need robust and seamless IT infrastructure that keeps pace with evolving market and technology demands. IT infrastructure has always been known as a “keeping the lights on” function but now it has evolved into a core catalyst of Digital Transformation. However, as a function, IT infrastructure is yet to undergo a core transformation. The report discusses why a reboot is critical.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
The document discusses the menstrual cycle as a natural process experienced by females. It notes that in the past, most females began menstruating in their late teens or early twenties, but some now start as young as age nine. The first occurrence of menstruation, known as menarche, marks the onset of sexual maturity and is an important developmental milestone.
The document discusses the menstrual cycle as a natural process experienced by females. It notes that in the past, most females began menstruating in their late teens or early twenties, but some now start as young as age nine. The first occurrence of menstruation, known as menarche, marks the onset of sexual maturity and is an important developmental milestone.
A Comparative Study on Online Transaction Fraud Detection by using Machine Le...IRJET Journal
This document summarizes a study that used machine learning and Python to detect online transaction fraud. It describes how online transactions and fraud are increasing. The study used a real credit card dataset to train models like KNN, NB, and SVM to detect fraudulent transactions based on user behavior patterns and restrict fraudulent users after three failed attempts. The goal was to develop a system that can detect fraud in real-time and prevent losses for banks and credit card users.
TWO-LAYER SECURE PREVENTION MECHANISM FOR REDUCING E-COMMERCE SECURITY RISKSijcsit
E-commerce is an important information system in the network and digital age. However, the network intrusion, malicious users, virus attack and system security vulnerabilities have continued to threaten the operation of the e-commerce, making e-commerce security encounter serious test. How to improve ecommerce security has become a topic worthy of further exploration. Combining routine security test and
security event detection procedures, this paper proposes the Two-Layer Secure Prevention Mechanism (TLSPM). Applying TLSPM, routine security test procedure can identify security vulnerability and defect,and develop repair operations. Security event detection procedure can timely detect security event, and assist follow repair. TLSPM can enhance the e-commerce security and effectively reduce the security risk
of e-commerce critical data and asset.
A Review of deep learning techniques in detection of anomaly incredit card tr...IRJET Journal
This document summarizes a review of deep learning techniques for detecting anomalies in credit card transactions. It discusses how credit card fraud causes major financial losses and how machine learning can help identify fraudulent transactions. The document outlines the objectives of comparing support vector machines and random forests for credit card fraud detection and discusses challenges like class imbalance in the data. It presents the system architecture for credit card fraud detection and analyzes results on a dataset of European credit card transactions, finding random forests outperform decision trees. Future work to improve accuracy is also discussed.
Similar to Available online at www.sciencedirect.comhttpwww.keaipubl.docx (20)
Attaining Expertise
You are training individuals you supervise on how to attain expertise in your field.
Write
a 1,050- to 1,200-word paper on the processes involved with attaining expertise, using your assigned readings in Anderson. Explain how these processes apply to attaining expertise in your current field or in the field you plan to enter. Focus on the cognitive processes that are involved in mastering knowledge and skills.
Include
a title page and references list consistent with APA guidelines.
Click
the Assignment Files tab to submit your assignment.
.
attachment Chloe” is a example of the whole packet. Please follow t.docxcelenarouzie
This document provides instructions for writing a PR packet that includes a pitch letter, news release, feature release, fact sheet, executive biography, and media alert following the example and format provided in the attachment. The writer has already completed the news release part of the packet and included it in the attached example for reference in completing the rest of the packet.
AttachmentFor this discussionUse Ericksons theoretic.docxcelenarouzie
Attachment
For this discussion:
Use Erickson's theoretical framework to explore adolescent attachment and its developmental impact.
Choose two issues related to adolescent attachment (for example, attachment relationships with parents and peers, or the nature of attachment system in adolescence) and describe possible implications for adult life.
Support your response with APA-formatted citations from scholarly sources, including both those provided in this unit and any additional evidence you may have researched.
.
Attachment and Emotional Development in InfancyThe purpose o.docxcelenarouzie
Attachment and Emotional Development in Infancy
The purpose of this discussion is to consider the stages of attachment from birth to one year, and emotional development and psychosocial crisis in infancy.
Briefly discuss attachment patterns and what you see as the most significant impact on the development of attachment.
Describe strategies that caretakers can implement to promote the child's ability to regulate emotions as he or she develops.
Remember to appropriately cite any resources, including the textbook, that you use to support your thinking in your initial post.
.
ATTACHEMENT from 7.1 and 7.2 Go back to the Powerpoint for thi.docxcelenarouzie
ATTACHEMENT from 7.1 and 7.2
Go back to the Powerpoint for this week and reread slides 12 and 13
Select at least 5 bullet points that you think are important because they affect the way justice is carried out in the State and or at the local level.
Write your entry explaining why you chose those 5 elements. Why are they important. What would you change?
.
Attached the dataset Kaggle has hosted a data science competitio.docxcelenarouzie
Attached the dataset
Kaggle has hosted a data science competition to predict category of crime in San Francisco based on 12 years (From 1934 to 1963) of crime reports from across all of San Francisco’s neighborhoods (time, location and other features are given).
I would like you to explore the dataset attached visually using Tableau and uncover hidden trends:
Are there specific clusters with higher crime rates?
Are there yearly/ Monthly/ Daily/ Hourly trends?
Is Crime distribution even across all geographical areas or different?
.
Attached you will find all of the questions.These are just like th.docxcelenarouzie
Attached you will find all of the questions.
These are just like the others I put up before. they need to be awnsered individually. Please use APA format with in text citations and references. My book is at least required as one of the references:
Harr, J. S., Hess, M. H., & Orthmann, C. H. (2012).
Constitutional law and the criminal justice system
(5th ed.). Belmont, CA: Wadsworth.
This assignment needs to be done by Friday by 11:00 P.M Eastern Time.
.
Attached the dataset Kaggle has hosted a data science compet.docxcelenarouzie
Attached the dataset
Kaggle has hosted a data science competition to predict category of crime in San Francisco based on 12 years (From 1934 to 1963) of crime reports from across all of San Francisco’s neighborhoods (time, location and other features are given).
I would like you to explore the dataset attached visually using Tableau and uncover hidden trends:
Are there specific clusters with higher crime rates?
Are there yearly/ Monthly/ Daily/ Hourly trends?
Is Crime distribution even across all geographical areas or different?
.
B. Answer Learning Exercises Matching words parts 1, 2, 3,.docxcelenarouzie
B. Answer Learning Exercises
* Matching words parts 1, 2, 3, and 4
* Definitions
*Matching Terms and Definitions 1, 2
C. Answer the following questions base in chapter 1:
1. Define Word root, mention 5 examples.
2. Define Suffixes, mention 5 examples.
3. Define Prefixes, mention 5 examples.
4. Some prefixes are confusing because they are similar in spelling, but opposite in meaning, those are call Contrasting Prefixes; mention 5 examples and their meaning.
.
B)What is Joe waiting for in order to forgive Missy May in The Gild.docxcelenarouzie
B)What is Joe waiting for in order to forgive Missy May in “The Gilded Six-Bits”? How does period of deliberation affect his forgiveness of her – does it make more of less sincere? What does this say about their relationship going into the future?
C) How is Dave in “The Man Who Was Almost A Man” not a man? Is there one central force preventing him from becoming a man? How does he go about overcoming this? Is it even possible for him to do so?
.
B)Blanche and Stella both view Stanley very differently – how do the.docxcelenarouzie
B)Blanche and Stella both view Stanley very differently – how do they see him and what does this view say about themselves? What causes Stella to continue to return to Stanley? Does she really trust him? Does she ultimately sacrifice her sister for him?
C) What is the difference between how Blanche presents herself and what she really is? Why does she choose to present herself so differently?
250 words each
.
b) What is the largest value that can be represented by 3 digits usi.docxcelenarouzie
b) What is the largest value that can be represented by 3 digits using radix-3?
c) Why do you think that binary logic is much more commonly used than ternary logic? Be brief.
The ASCII code for the letter E is 1000101, and the ASCII code for the letter e is 1100101. Given that the ASCII code for the letter M is 1001101, without looking at Table 2.7, what is the ASCII code for the letter m?
.
b$ E=EE#s{gEgE lEgEHEFs ig=ii 5i= l; i € 3 r i.Er1 b €€.docxcelenarouzie
b$ E=EE#s{gEgE lEgEH:*EFs ig=ii 5i= l; i € 3 r ?: i.Er
1 b €€ :p€ i 3= ?it'.-'-;;= -av.-;i ;5 li,ii ;Ei+:,;i; ;iiEE: : =,E s*Ess€E;!;riit n*=! i : *:i;i-;r; >: z:t=; iE b y
ts E E :E ! i E Fif ; E5- a = '\Y q?i
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B A S I C L O G I C M O D E L D E V E L O P M E N T Pr.docxcelenarouzie
B A S I C L O G I C M O D E L D E V E L O P M E N T
Produced by The W. K. Kellogg Foundation
53535353
Developing a Basic Logic
Model For Your Program
Drawing a picture of how your program will achieve results
hether you are a grantseeker developing a proposal for start-up funds or a
grantee with a program already in operation, developing a logic model can
strengthen your program. Logic models help identify the factors that will
impact your program and enable you to anticipate the data and resources
you will need to achieve success. As you engage in the process of creating your
program logic model, your organization will systematically address these important
program planning and evaluation issues:
• Cataloguing of the resources and actions you believe you will need to reach intended
results.
• Documentation of connections among your available resources, planned activities and
the results you expect to achieve.
• Description of the results you are aiming for in terms of specific, measurable, action-
oriented, realistic and timed outcomes.
The exercises in this chapter gather the raw material you need to draw a basic logic
model that illustrates how and why your program will work and what it will accomplish.
You can benefit from creating a logic model at any point in the life of any program.
The logic model development process helps people inside and outside your
organization understand and improve the purpose and process of your work.
Chapter 2 is organized into two sections—Program Implementation, and Program
Results. The best recipe for program success is to complete both exercises. (Full-size
masters of each exercise and the checklists are provided in the Forms Appendix at the
back of the guide for you to photocopy and use with stakeholder groups as you design
your program.)
Exercise 1: Program Results. In a series of three steps, you describe the results you
plan to achieve with your program.
Exercise 2: Program Resources and Activities by taking you through three steps
that connect the program’s resources to the actual activities you plan to do.
Chapter
2
W
B A S I C L O G I C M O D E L D E V E L O P M E N T
Produced by The W. K. Kellogg Foundation
54545454
The Mytown Example
Throughout Exercises 1 and 2 we’ll follow an example program to see how the logic
model steps can be applied. In our example, the folks in Mytown, USA are striving to
meet the needs of growing numbers of uninsured residents who are turning to Memorial
Hospital’s Emergency Room for care. Because that care is expensive and not the best
way to offer care, the community is working to create a free clinic. Throughout the
chapters, Mytown’s program information will be dropped into logic model templates for
Program Planning, Implementation, and Evaluation.
Novice Logic modelers may want to have copies of the Basic Logic Model Template in
front of them and follow along. Those read.
B H1. The first issue that jumped out to me is that the presiden.docxcelenarouzie
B H
1. The first issue that jumped out to me is that the president and two vice presidents were the ones to develop the program. Our lecture notes and the text tell us that safety is one topic where management and employees can usually come to an agreement. Everyone wants a safe work environment. We are also taught that consultation is the best way to approach health and safety at work. Again, this means involving more than three people at the company. For starters, I would recommend that the safety program be dismantled and reconstructed by a committee consisting of at least 50% employees, not just senior leadership. I would keep this committee as small as possible and not have it controlled by one person only. The committee should be formed of employees from all sections and representing all possible departments where health and safety are potential issues.
2. The first issue that jumped out to me is that the president and two vice presidents were the ones to develop the program. Our lecture notes and the text tell us that safety is one topic where management and employees can usually come to an agreement. Everyone wants a safe work environment. We are also taught that consultation is the best way to approach health and safety at work. Again, this means involving more than three people at the company. For starters, I would recommend that the safety program be dismantled and reconstructed by a committee consisting of at least 50% employees, not just senior leadership. I would keep this committee as small as possible and not have it controlled by one person only. The committee should be formed of employees from all sections and representing all possible departments where health and safety are potential issues.
N S
1. 1.Top of Form
There could be a number of problems with CMI's safety awareness plan. One major one is that they could not be promoting safety. That is the first step into getting the program to work...employee involvement. First the awareness program was developed by the president and the vice presidents. A safety awareness program can be more successful if employees are involved in the development, and remain involved as it is adjusted and refined. Rules should be in place, and employers must ensure that those rules are followed and enforced consistently. Incentives and competition could be another way to promote safety in the work place. Our text cites that having employees work in teams and have them determine the incentives will keep them involved and promote safety. Also, of course keeping employees up to date on all rules will also promote safety.
2. I think the supervisor's response to employee complaints about John Randall is not appropriate at all. Even thought it is difficult, home problems should not be brought into the work place. Especially if coworkers are complaining about someone's behavior. This does not promote safety at all. To say that Randall will get over it and to disclose that he has personal problems is.
b l u e p r i n t i CONSUMER PERCEPTIONSHQW DQPerception.docxcelenarouzie
b l u e p r i n t i CONSUMER PERCEPTIONS
HQW DQ
Perceptions Impact
Your Market?
By Nicole Olynk Widmar and
Melissa McKendree, Purdue University
I aintaining existing mar-
kets for pork products,
I cultivating new markets
for existing products and
creating new products for new markets
are some avenues that the U.S. pork
industry has sought, and continues to
explore, for growth. When it comes to
maintaining markets, there are several
relationships that must be considered.
End consumers, whether in restaurant
or supermarket settings, are increas-
ingly interested in social issues and the
production processes employed in food
production. Livestock products (meat
and dairy products) certainly seem
to get the majority of the spotlight in
regard to consumers' concern for pro-
duction processes.
Shoppers in supermarkets and din-
ers in restaurants have increased access
to information via the Internet, and are
in constant communication with one
another via social media and alterna-
tive news sources about perceptions
of animal agriculture. Even though
most U.S. consumers are not directly
in contact with livestock, concern for
the treatment of animals, including
those employed in food production,
is evident — and increasing. While
in the past consumers were mainly
concerned with factors like the fat or
nutritional content of pork, for exam-
ple, today's savvy shoppers are con-
sidering other factors, like the welfare
of livestock (pigs), safety of workers
employed on farms and potential envi-
ronmental impacts (externalities) of
livestock operations.
Large-scale changes in production
practices are taking place in livestock
24 April 15, 2014
production due to pressures from vari-
ous interested parties. Changes such
as the discontinued use of gestation
stalls, for example, are being sought
via traditional regulatory channels in
some states, but are also being pushed
via non-traditional market channels.
Consider the cumbersome process
of changing regulations, versus the
oftentimes faster (and perhaps easier)
channel of influencing key market
actors. It is no surprise that consum-
ers' concerns are increasingly voiced to
supermarkets and restaurants which,
in turn, take action to satisfy their
customers by placing pressure on sup-
ply-chain players. Changes sought via
"the market," rather than legislation or
regulation, are increasingly common,
and the use of market channels for
communicating throughout the supply
chain is unlikely to stop anytime soon.
www.nationalhogfarmer.com
Figure 1. Reported Recollection of Exposure to Media
Stories Regarding Pig Welfare, by Source
7 0 %
0 %
Television Internet
Media source
Printed Magazines
Newspaper
Books I have not seen
any media stories
regarding pig
welfare.
Melissa McKendree (left) and Nicole Olynk Widmar
A national-scale study completed
at Purdue University by Nicole Olynk
Widmar, Melissa McKendree, and
Candace Croney in 2013 was focused
on assessing consumers' perceptions of
various por.
B R O O K I N G SM E T R O P O L I TA N P O L I CY .docxcelenarouzie
B R O O K I N G S
M E T R O P O L I TA N
P O L I CY
P R O G RA M
6
I . I N T R O D U C T I O N
A
s the global economy has become more integrated and urbanized,
fueled in large part by technology, major cities and metropolitan
areas have become key engines of economic growth. The 123 largest
metro areas in the world generate nearly one third of global output
with only 13 percent of the world’s population.
In this urban-centered world, the classic notion of a
global city has been upended. This report introduces
a redefined map of global cities, drawing on a new
typology that demonstrates how metro areas vary in
the ways they attract and amass economic drivers
and contribute to global economic growth in distinct
ways. New concerns about economic stagnation—in
both developing and developed economies—add
urgency to mapping the role of the world’s cities and
the extent to which they are well-positioned to deliver
the next round of global growth.1
Instead of a ranking or indexed score, which many
prior cities indices and reports have capably deliv-
ered,2 this analysis differentiates the assets and
challenges faced by seven types of global cities.
This perspective reveals that all major cities are
indeed global; they participate as critical nodes in
an integrated marketplace and are shaped by global
currents. But cities also operate from much differ-
ent starting points and experience diverse economic
trajectories. Concerns about global growth, productiv-
ity, and wages are not monolithic, and so this typology
can inform the variety of paths cities take to address
these challenges. For metro leaders, this typology
can also ensure better application of peer com-
parisons, enable the identification of more relevant
global innovations to local challenges, and reinforce a
city-region’s relative role and performance to inform
economic strategies that ensure ongoing prosperity.
This report proceeds in four parts. In the following
section, Part II, we explore the three global forces of
urbanization, globalization, and technological change,
and how together they are demanding that city-
regions focus on five core factors—traded clusters,
innovation, talent, infrastructure connectivity, and
governance—to bolster their economic competitive-
ness. Building on these factors, Part III outlines the
data and methods deployed to create the metropoli-
tan typology. Part IV explores the collective economic
clout of the metro areas in our sample and introduces
the new typology of global cities. Finally, Part V
explores the future investments, policies, and strate-
gies required for each grouping of metro areas. Within
the typology framework, we explore the priorities for
action going forward, including the implications for
governance.
REDEFINING
GLOBAL CITIES
THE SEVEN TYPES
OF GLOBAL METRO
ECONOMIES
7
U R B A N I Z AT I O N
The world is becoming more urba.
B L O C K C H A I N & S U P P LY C H A I N SS U N I L.docxcelenarouzie
B L O C K C H A I N &
S U P P LY C H A I N S
S U N I L W A T T A L
T E M P L E U N I V E R S I T Y
• To understand the power of blockchain systems, and the things they can do, it is important to
distinguish between three things that are commonly muddled up, namely the bitcoin currency,
the specific blockchain that underpins it and the idea of blockchains in general.
• Economist, 2015
WHAT IS BLOCKCHAIN?
• A technology that permits transactions to be recorded
– Cryptographically chains blocks in order
– Allows resulting ledger accessed by different servers
– Information stored can never be deleted
• A digital distributed ledger that is stored and maintained on multiple systems belonging to multiple
entities sharing identical information (Deloitte)
• Bitcoin was the first demonstrable use
HISTORY OF BLOCKCHAIN
T YPES OF BLOCKCHAINS
• public or permissionless blockchains
– everyone who wants to engage in the network can openly see all transactions. The technology is
transparent, and all who want to engage in making transactions on the blockchain can do so.
• private or permissioned blockchains
– closed and accessible only to a selected few who have permission to engage in the blockchain.
BLOCKCHAIN FEATURES
• A blockchain lets us agree on the state of the system, even if we don’t all trust each other!
• We don’t want a single trusted arbiter of the state of the world.
• A blockchain is a hash chain with some other stuff added
– Validity conditions
– Way to resolve disagreements
• The spread of blockchains is bad for anyone in the “trust business”
WHAT IS BITCOIN
• A protocol that supports a decentralized, pseudo-anonymous, peer-to-peer digital currency
• A publicly disclosed linked ledger of transactions stored in a blockchain
• A reward driven system for achieving consensus (mining) based on “Proofs of Work” for
helping to secure the network
• A “scare token” economy with an eventual cap of about 21M bitcoins
10
OTHER USES OF BLOCKCHAIN
• Supply Chain
• Online advertising
• Smart Contracts
• Voting
BENEFITS OF BLOCKCHAIN
• Consistent
• Democratic
• Secure and accurate
• Segmented and private
• Permanent and tamper resistant
• Quickly updated
• Intelligent – smart contracts
BARRIERS TO BLOCKCHAIN
ADOPTION
• Hype
• Finding the right balance in regulation
• Cybersecurity
• Ease of use over shared databases
• Lack of understanding and knowledge
SUPPLY CHAIN CHALLENGES
• Margin Erosion
• Demand changes
• Ripple Effect
• Supply Chain Risk Management
• Lack of end to end visibility
• Obsolescence of Technology
APPLICATIONS IN SUPPLY CHAINS
• Traceability
• International Trade
• Continuity of Information
• Data Analytics
• Visibility
• Digital contracts and payments
• Check fraud and gaming
EX AMPLES OF BLOCKCHAIN IN
SUPPLY CHAINS
• 300 Cubits
– Blokcchain technology for the shipping industry
• BanQu
– Payment for small businesses
• Bext360
– Social sustainability.
Año 15, núm. 43 enero – abril de 2012. Análisis 97 Orien.docxcelenarouzie
Año 15, núm. 43 / enero – abril de 2012. Análisis 97
Orientalizing New Spain:
Perspectives on Asian Influence
in Colonial Mexico1
Edward R. Slack, Jr.2
Resumen
E ste artículo investiga la totalidad de la influencia de Asia sobre la Nueva España que resultó de la conquista de Manila en 1571 y la re-gularización del comercio Transpacífico -comúnmente conocido como
los galeones de Manila o las naos de China- entre las Filipinas y Acapulco.
En sus inicios, una oleada constante de inmigrantes asiáticos, mercancías y
nuevas técnicas de producción influyeron mesuradamente en la sociedad y
la economía colonial mediante un proceso que el autor denomina “Orientali-
zación”. No obstante, en ninguna manera “Orientalización” se debe equiparar
con el concepto de Edward Said de “Orientalismo” por la relación histórica,
única e intima de la Nueva España con Asia a principios de la edad Moderna.
Abstract
This article examines the totality of Asia’s influence on New Spain that resulted
from the conquest of Manila in 1571 and the regularization of transpacific tra-
de – more widely known as the Manila Galleons or naos de China – between the
Philippines and Acapulco. In its wake, a steady stream of Asian immigrants,
commodities, and manufacturing techniques measurably impacted colonial
society and economy through a process the author calls “Orientalization.”
However, “Orientalization” should in no way be equated with Edward Said’s
1. Artículo recibido el 28 de octubre de 2011 y dictaminado el 16 de noviembre de 2011.
2. Eastern Washington University.
98 México y la Cuenca del Pacífico. Año 15, núm. 43 / enero – abril de 2012
Edward R. Slack, Jr.
concept of “Orientalism” because of New Spain’s uniquely intimate historical
relationship with Asia in the early Modern era.
Introduction
Contrary to popular belief, the Philippines Islands were more a colony of New
Spain (Nueva España) than of “Old Spain” prior to the nineteenth century.
The Manila galleons, or naos de China (China ships), transported Asian pro-
ducts and peoples to Acapulco and other Mexican ports for approximately
250 years. Riding this ‘first wave’
of maritime contact between
the Americas and Asia were tra-
velers from China, Japan, the
Philippines, various kingdoms in
Southeast Asia and India known
collectively in New Spain as chinos
(Chinese) or indios chinos (Chine-
se Indians), as the word chino/a
became synonymous with the
Orient. The rather indiscrimi-
nate categorizing of everything
“Asian” under the Spanish noun
for the Ming/Qing empire, its
subjects and export items is easily
discovered in a variety of sources
from that age. To illustrate, the
eig hteenth centur y works of
Italian adventurer Gamelli Carreri and the criollo priest Joachin Antonio
de Basarás (who evangelized in Luzon) nonchalantly refer to the Philippine
Islands as “la China.”3 Additionally, words such as chinería (Chinese-esque,
European/Mexican imitation of Chines.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Available online at www.sciencedirect.comhttpwww.keaipubl.docx
1. Available online at www.sciencedirect.com
http://www.keaipublishing.com/en/journals/jfds/
ScienceDirect
The Journal of Finance and Data Science 1 (2015) 1e10
Big data based fraud risk management at Alibaba
Jidong Chen*, Ye Tao, Haoran Wang, Tao Chen
Alipay, Alibaba, China
Received 15 February 2015; revised 8 March 2015; accepted 20
March 2015
Available online 14 July 2015
Abstract
With development of mobile internet and finance, fraud risk
comes in all shapes and sizes. This paper is to introduce the
Fraud
Risk Management at Alibaba under big data. Alibaba has built a
fraud risk monitoring and management system based on real-
time
big data processing and intelligent risk models. It captures fraud
signals directly from huge amount data of user behaviors and
network, analyzes them in real-time using machine learning, and
accurately predicts the bad users and transactions. To extend the
fraud risk prevention ability to external customers, Alibaba also
built up a big data based fraud prevention product called Ant-
Buckler. AntBuckler aims to identify and prevent all flavors of
4. White7), and is using ODPS now. Data processing and
analyzing is also improved from T þ 1 mode1 to near real-time
mode.
By adapting big data techniques, Alibaba highlights advances
made in the area of fraud risk management. It invents
a real-time payment fraud prevention monitoring system, called
CTU (Counter Terrorist Unit). And CTU becomes one
of the most advanced online payment fraud management system
in China, which can track and analyze accounts' or
users' behavior, identify suspicious activities and can apply
different levels of treatments based on intelligent
arbitration.
Fraud risk models are one of the supportive layers of CTU2
(Counter Terrorist Centre). They use statistical and
engineering techniques to analyze the aggregated risk of an
intermediary (an account, a user or a device etc). Detailed
attributes are generated as inputs. Different algorithms are to
assess the correlations of these attributes and fraud
activities, and to separate good ones from bad ones. Validating
and tuning are to make sure models apply to different
scenarios. Big data at Alibaba produces thousands and
thousands of attributes, and fraud risk models are built to deal
with various of fraud activities.
Those big data based fraud models are widely used in almost
every procedure within Alibaba to monitor fraud, such
as account opening, identity verification, order placement,
before and after transaction, withdrawal of money, etc. To
build up a safe and clean payment environment, Alibaba decides
to expand this ability to external users. A user-
friendly product is built, called AntBuckler. AntBuckler is a
product to help merchants and banks to identify
cyber-crime risks and fraud activities. And a risk score (RAIN
Score) is generated based on big data analysis and given
5. to merchants and banks to tell the risk level.
In this paper, we show that Alibaba applies the big data
techniques and utilizes those techniques in fraud risk
management models and systems. We also present the
methodology and application of the big data based fraud
prevention product AntBuckler used by Alibaba.
The remainder of the paper is organized as follows. Section 2
introduces big data applications and basic computing
process at Alibaba. Section 3 explains fraud risk management at
Alibaba in details and fraud risk modeling. Section 4
provides an explanation of AntBuckler. We conclude in Section
5.
2. Big data applications at Alibaba
Alibaba grows fast in past 10 years. In 2005, daily transaction
volume was less than 10 thousands per day. It reached
to 188 million on Nov 11th, 2013 one day. Graph 1 illustrates
that the transaction volume at Alibaba changes from
2005 to 2013 on daily basis.
With business growing exponentially, data computing,
processing system and data storage are bound to change as
well. It started from data computing platform of RAC (Oracle
Real Application Clusters (see Oracle white paper1)) in
2009, via GP and Hadoop, and is using ODPS now. Data
processing and analyzing is also improved from T þ 1 mode
1 T þ N mode: T is time, when system runs. N is time interval.
T þ 1 means the system runs on the second day.
2
CTU: Alipay's internal risk control system, which is fully
developed and designed by Alibaba. This name is inspired by
American TV series
6. <<24>>.
Graph 2. Big data computing progress at Alibaba.
3J. Chen et al. / The Journal of Finance and Data Science 1
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to real-time mode, especially in risk prevention at Alibaba,
fraud check on each transaction can be controlled within
100 ms (millisecond). Moreover, data sources are extended from
single unit data to a combination of internal group
data and external bureau data. Graph 2 illustrates that the big
data computing process at Alibaba progress extensively
since 2009. Alibaba has data not only from Taobao, Tmall and
Alipay, but also from partners like Gaode Maps and
others. Data from various sources builds up an integrated data
platform, the platform from business scenarios extends
largely as well. Marketing uses data analysis to target users
accurately and to provide customers service personally.
Merchants and financial companies need professional data
classification to filter out valuable customers. Intelligent
customer service can effectively and efficiently solve users'
requests and complaints using comprehensive data
platform. And the online payment services and systems,
Alibaba, among leaders of online payment service providers,
builds up a fraud risk management platform to ensure both
buyers and sellers with fast and safe transactions. Alibaba
deals with big data extensively on credit score and insurance
prices as well as other types of business.
3. Fraud risk management at Alibaba
3.1. Framework for fraud risks
7. Fraud risk management at Alibaba is totally different from that
of traditional financial and banking system now due
to big data. To deal with real-time frauds, new engineering
approaches are gradually developed to handle such quantity
of data. On top of hardware system, risk prevention framework
is also built up to support new methodology and
algorithm. There are a few different kinds of risk prevention
frameworks.
One fundamental framework of fraud risk that Alibaba uses is
called multi-layer risk prevention framework. Graph
3 illustrates the multi-layer risk prevention framework Alibaba
uses in Alipay System. There are total five layers in
this system.
At Alibaba, there are 5 layers to prevent fraud for a transaction.
The five layers are (1) Account Check, (2) Device
Check, (3) Activity Check, (4) Risk Strategy and (5) Manual
Review. One fraudster can pass first layer on account
check, and then there are still four layers ahead to block the
fraudster. When a transaction is initiated, the first layer is
Graph 3. Multi-layer risk prevention framework.
4 J. Chen et al. / The Journal of Finance and Data Science 1
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Account Check, which includes buyer account information and
seller account information. Several checks on the first
layer Account Check are designed as questions: does the buyer
or seller account have bad/suspicious activity before?
Is there any possibility the buyer account stolen etc? Extremely
suspicious transactions may be declined to protect
genuine buyers, or extra authentic methods may be trigged to
double confirmation in this situation. The second layer is
8. Device Check, which includes the IP address check and
operation check on the same device. Similarly, checks on the
second layer Device Check are designed from passing several
questions: whether there are huge quantify of trans-
actions from the same device? Any transaction is from bad
devices? The third layer is Activity Check, called as
Behavior Check as well, which checks historical records, buyer
and seller behavior pattern, linking among accounts,
devices and scenarios. Checks on the third layer Activity Check
are designed as questions as well: whether the buyer
or seller account link to an identified bad account? The fourth
layer is Risk Strategy, which makes final judgment and
takes appropriate action. Checks on the fourth layer Risk
Strategy are designed to aggregate all results from previous
checks according to severity levels. Some transactions are sent
to auto-decision due to obvious fraud activities. Some
grey cases are sent to Manual Review. On one hand, Alipay
would like to provide better services and experiences for
both parties. On the other hand, Alipay does not want to
misjudge any case. Without strong evidence, suspicious cases
will be manually reviewed in the last layer Manual Review,
where more evidences are revealed and some phone calls
may be made to verify or remind or check with buyers or
sellers.
Another key difference between fraud risk management at
Alibaba and “that” of traditional financial and banking
system is the risky party. Customers are evaluated to be the
main risky party in banking system. At Alibaba, there are 3
layers of risky party. The three layers are (1) Customer level,
(2) Account level and (3) Scenario level. See Graph 4.
Risk fraud prevention at Alibaba is for both customers whether
they are buyers or sellers or not, for both accounts
whether those accounts are prestigious for big company or a
single individual or not, for both scenarios whether those
activities happen during account opening or money withdrawal.
9. 3.2. CTU e fraud prevention monitoring system
CTU is a real-time payment fraud prevention monitoring
system, which can track and analyze accounts' or users'
behavior, identify suspicious activities and apply different level
of treatments based on intelligent arbitration. The first
Graph 4. Multi-layer risk prevention framework.
5J. Chen et al. / The Journal of Finance and Data Science 1
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version launched on 1st Aug, 2005. This system is
independently developed by Alipay's risk control team. At that
time, it focused more on large transaction investigation,
suspicious refund, etc. Now it extends to money laundry,
marketing fraud, accounts, and card stolen/loss as well as cash
monetization. Additionally, it is a 24 h monitoring
system, which provides throughout protection at any time.
When an event happens, it passes through CTU for judgment.
An event is defined to be that a user logins, changes
profiles, initiates transactions, withdraws money from Alibaba
to other bank accounts and others. There are hundreds
of kinds of events. A suspicious event triggers models and rules
behind the CTU for real-time computing, and within
100 ms, CTU returns the result with risk decision. If this is a
low risk from CTU return, the event is passed to continue
its operation. If this is a high risk, the CTU will direct a stop or
a further challenge step to continue the process. Graph 5
illustrates the CTU operating process.
3.3. Fraud risk modeling
The data that supports CTU judgment is from historical cases,
10. user behaviors, linking relationship and so on. Risk
models are built to analyze fraudsters' cheating patterns,
relations among fraudsters, different behaviors between a
group of good users and a group of bad ones.
There are a few factors to consider during building risk models.
Bias and Variance are usually concerned together to
balance the effectiveness and impact of a risk model. Bias is a
factor to measure how a model fits for the risk, how to
Graph 5. CTU-risk prevention system at Alibaba.
Graph 6. Three dimension of RAIN score.
6 J. Chen et al. / The Journal of Finance and Data Science 1
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find the risk of account or transaction accurately. Variance is to
measure whether a model is stable or not, whether it
can sustain a relative longer lifecycle in business. Negative
positive rate, also called wrong cover rate, is to measure
how the accuracy a model is. High negative positive rate will
bring company huge business pressure and bad user
experience. Moreover, interpretability is necessary to explain to
users the reason that a model gives such risk level to
his account or transaction. In big data age, except for factors
mentioned above, data scientists keep fighting for data
deficiency, data sparsity and data skewness.
Model building process is also relatively mature at Alibaba after
repeated deliberation. White and black samples
are chosen first. White samples are good risk parties. Black
samples are usually risky parties judged as bad. A good
model can differentiate white and black samples to the most
degree. Behavior data and activity data are collected for
both samples to generate original variables from abstracting
11. aggregated variables. Through testing, some variables are
validated effectively. They can be finally used in model
building. From our modeling experience by using Alibaba big
data, decision tree C5.0 and Random Forest have better
performance to balance between Bias and Variance. One
obvious reason is that they do not assume data distribution since
they are algorithmic models rather than data models.
When a model is able to better separate good and bad ones
among samples, the model is basically adapted to process.
However, to make sure it applicable to different scenario,
validation is also important. A model can be launched if it
works effectively and efficiently on testing and validating data.
Then, the fraud risk prevention models are needed to
be deployed in the production environment, and are used in
CTU combining with other strategies and rules.
3.4. RAIN score, a risk model
RAIN is one kind of risk models. RAIN stands for Risk of
Activity, Identity and Network. Basically, the risk of an
object (a user, an account or even a card) is composited of three
dimensions of variables, activity, identity and network.
Graph 6 illustrates the three dimensions of RAIN score.
Hundreds of variables are first selected to interpret status and
behavior of an object. Based on testing, verifying and validating
from fraud risk models, variables are selected and
kept. A RAIN score is generated based on different weight of
variables within these three dimensions. Variables and
weight of variables may differ according to different risk
scenarios. For example, for a card stolen scenario, more
Identity variables may be selected and with higher weight.
While for a credit speculation scenario, more Network
variables may be selected and are given higher rate. Weights of
variables are trained by different machine learning
algorithms, such as logistic regression.
12. 3.5. An example of network-based analysis in fraud risk
detection
Graph theory (Network-based Analysis), an applied
mathematical subject is commonly applied on social network
analysis (see Wasserman6). Facebook, Twitter apply the Graph
theory on their social network analysis. Network-based
Graph 7. Network of accounts and information.
7J. Chen et al. / The Journal of Finance and Data Science 1
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analysis plays a new role in the risk control. Fraudsters
nowadays are mischievous. They know that online risk models
are constantly checking whether fraud accounts are from same
name, address, phone and credit card, etc. Hence, they
try new ways to hide the connections. Therefore, network based
analysis is introduced to reveal the connection in this
area. For example, if each account is considered as a node,
network-based analysis is to locate edges among different
nodes if they are owned by a physical person. If there are
reasonable ways to define the edges among different nodes,
some interesting groups can be disclosed.
In Graph 7, the red nodes represent accounts and green nodes
are detailed profile information for those accounts,
such as enabled IPs, phone numbers, name, address, etc. If an
account (red node) has detailed profile information
(green node), network analysis draws a line between this red
and green node to show the relationship and the line is an
edge. Graph 7 illustrates the network analysis that both groups
have their own enabled IPs. However some accounts in
two groups shared same bind phone numbers. This exposes the
connections between two groups. Another example as
13. below.
Graph 8. Betweenness detection.
8 J. Chen et al. / The Journal of Finance and Data Science 1
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Graph 8 tells us another story. One account shares the same
register IP and register device footprint with the left
group. It also shares the same name and info number with the
right group. This is a strong evidence to prove the
connections between two groups of accounts.
Two examples above are just a simple case. In the real world,
connections are extremely complicated. We have to
use paralleled graph algorithms and special graph storage to
handle the huge network connection graph. The
betweenness nodes (concepts from network-based analysis, see
Freeman5) play important roles in finding connections
of different accounts, where betweenness nodes are the
betweenness centralities used in the network analysis.
Connections now is widely used to judge relationship of
accounts, this effectively prevent fraudsters building up their
own networks.
4. AntBuckler e a big data based fraud prevention product
To build up a safe and clean pay0ment environment, Alibaba
decides to expand its risk prevention ability to
external users. A big data based fraud management product is
built and called AntBuckler. This product is fully
developed by Alipay.
AntBuckler is a product to help merchants and banks to identify
cyber-crime risks and fraud activities. We find that
merchants generally deal with similar fraud patterns. One
example is marketing program fraud. Merchants often give
14. cash reward or voucher certificate to new users to expand their
user base. Fraudsters often take this opportunity to
create hundreds of different accounts. To merchants, the
marketing resource is not given to correct user base. To good
users, they cannot benefit the cash reward or voucher
certificate. Fraudsters may also sell their accounts with coupon
in
higher price. This not only damages the merchant's brand image
and reputation, but also confuses the market and
potential customers.
Antbuckler uses the RAIN model engine and generates a risk
score (RAIN Score) to quantify the risk level. The
score ranges from 0 to 100. The higher, the riskier. It also has
user-friendly visualization. Top reasons are shown on top
with a higher weight and brighter colors. Connection, through
account, email, phone, card and so on, are presented
using network based view. See Fig. 1. Fig. 1 is the main
interface of one risky account. The interface gives detailed
Fig. 1. User-friendly visualization of AntBuckler.
Fig. 2. Operation dashboard of AntBuckler.
9J. Chen et al. / The Journal of Finance and Data Science 1
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account information, such as name, login email and registered
time. RAIN score is shown together with a colorful bar.
Green means safer and red means riskier. The operation
dashboard tells how many accounts are judged by AntBuckler,
15. how many accounts are identified with risk, where risky
accounts are distributed, etc. See Fig. 2. Fig. 2 is an example
of operation dashboard.
5. Conclusion
Big data based fraud risk management is a new trend in payment
service business. It leads to a new generation of
fraud monitoring and fraud risk management, which is based on
big data processing and computing technology, real-
time fraud prevention system and risk models. Alibaba has
successfully used big data based fraud risk management to
deal with daily fraud events. In this paper, we outline the fraud
risk management at Alibaba and a big data based fraud
prevention product. We would like to extend the analysis to
build a safer and cleaner payment environment.
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https://www.emc.com/collateral/hardware/white-papers/h8072-
greenplum-database-wp.pdfBig data based fraud risk
management at Alibaba1. Introduction2. Big data applications at
Alibaba3. Fraud risk management at Alibaba3.1. Framework for
fraud risks3.2. CTU – fraud prevention monitoring system3.3.
Fraud risk modeling3.4. RAIN score, a risk model3.5. An
example of network-based analysis in fraud risk detection4.
AntBuckler – a big data based fraud prevention product5.
ConclusionReferences