In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
This presentation shows how Predictive Analytics can be more futuristic than BI in using past events to predict the future.
Furthermore, we explore the best practices in Predictive Analytics, the challenges in deployment and how this solution can be used to create business value for the organization.
Presented by Ajay Gopikrishnan, our expert in Predictive Analytics and Data Mining at the BA4ALL (Business Analytics Insight 2014) event in the Netherlands.
http://www.capgemini.com/big-data-analytics
Protect Your Revenue Streams: Big Data & Analytics in TaxCapgemini
The game has changed since the onset of the financial crisis. Governments aiming to reduce budget deficits can only deliver so much through spending cuts. It is now even more vital that tax agencies ensure individuals and businesses pay the tax they owe, and that welfare fraud and error are minimised. Pretty will explain how he helps tax and welfare agencies tackle noncompliance, evasion and error. He will share client stories where billions of euros were saved, generating a return of at least 25 times the original investment.
By Ian Pretty,
Vice President, Global Tax & Welfare Leader
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
This presentation shows how Predictive Analytics can be more futuristic than BI in using past events to predict the future.
Furthermore, we explore the best practices in Predictive Analytics, the challenges in deployment and how this solution can be used to create business value for the organization.
Presented by Ajay Gopikrishnan, our expert in Predictive Analytics and Data Mining at the BA4ALL (Business Analytics Insight 2014) event in the Netherlands.
http://www.capgemini.com/big-data-analytics
Protect Your Revenue Streams: Big Data & Analytics in TaxCapgemini
The game has changed since the onset of the financial crisis. Governments aiming to reduce budget deficits can only deliver so much through spending cuts. It is now even more vital that tax agencies ensure individuals and businesses pay the tax they owe, and that welfare fraud and error are minimised. Pretty will explain how he helps tax and welfare agencies tackle noncompliance, evasion and error. He will share client stories where billions of euros were saved, generating a return of at least 25 times the original investment.
By Ian Pretty,
Vice President, Global Tax & Welfare Leader
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
AI in Fintech - slides for plenary panel @ IJCAI-20 Usama Fayyad
AI in Fintech - slides for plenary panel @ IJCAI-20 – 1/14/2021: https://ijcai20.org/panels/
AI in the FinTech era powers unprecedented financial innovations that facilitate, diversify, and transform our lives, society, and economy:
What makes AI critical in innovating FinTech?
What financial challenges demand AI?
What are challenges brought by AI-empowered finance? Opportunities or challenges?
What will next-generation AI-enabled FinTech look like? and
Answering these big questions demand deep thinking, insight, knowledge, and experience in the interdisciplinary innovation. 4 outstanding leaders share their unique insights and impactful experience.
Digital, Data & Analytics, Disruption in DealsAnand Rao
Presentation at the PwC Midwest Deals Summit by Dr. Anand Rao, John Sviokla and Andrea Fishman.
The presentation looks at impact of digitization, data and analytics and the disruption they cause in retail, healthcare, agribusiness, and financial services. We look at how this is leading to more deals across the spectrum from large M&A's to private equity to venture capital
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
Aligning your business to the data driven economy, how data is the new oil, importance of algorithms in a data driven world and their benefits for different industries, and new use cases of digital data.
IT that matters in the new machine age prioritizes cybersecurity, innovation, time-to-market and customers over cost-cutting, according to our latest study. Here’s what the future looks like for IT infrastructure, including our HEROES framework to guide you along the way.
Wondering how to bring services to your clients in real time – and on their preferred device? Need to automate your financial supply chain, including risk and compliance functions, and move to a pay for performance model?
Learn about use cases from within the big data ecosystem, ranging from AML compliance, trade lifecycle, fraud detection and digital transformation, and introduce their risk data aggregation and compliance initiative. Find out how you can best leverage Open Enterprise Hadoop to achieve these goals.
What are Swiss banks doing well, and where can they improve when it comes to their range of digital offering – and to the user experience of their customers?
Big data & analytics for banking new york lars hambergLars Hamberg
BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition
AI in Fintech - slides for plenary panel @ IJCAI-20 Usama Fayyad
AI in Fintech - slides for plenary panel @ IJCAI-20 – 1/14/2021: https://ijcai20.org/panels/
AI in the FinTech era powers unprecedented financial innovations that facilitate, diversify, and transform our lives, society, and economy:
What makes AI critical in innovating FinTech?
What financial challenges demand AI?
What are challenges brought by AI-empowered finance? Opportunities or challenges?
What will next-generation AI-enabled FinTech look like? and
Answering these big questions demand deep thinking, insight, knowledge, and experience in the interdisciplinary innovation. 4 outstanding leaders share their unique insights and impactful experience.
Digital, Data & Analytics, Disruption in DealsAnand Rao
Presentation at the PwC Midwest Deals Summit by Dr. Anand Rao, John Sviokla and Andrea Fishman.
The presentation looks at impact of digitization, data and analytics and the disruption they cause in retail, healthcare, agribusiness, and financial services. We look at how this is leading to more deals across the spectrum from large M&A's to private equity to venture capital
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
TechConnex Big Data Series - Big Data in BankingAndre Langevin
TechConnex is an industry forum for Canadian IT executives. This presentation from the fall of 2015 provides a survey of Hadoop adoption in the Canadian banking industry. Most adoption is driven by BCBS-239 implementation projects. The talk provides a broader risk systems perspective on Hadoop and discusses challenges and opportunities around the technology.
Aligning your business to the data driven economy, how data is the new oil, importance of algorithms in a data driven world and their benefits for different industries, and new use cases of digital data.
IT that matters in the new machine age prioritizes cybersecurity, innovation, time-to-market and customers over cost-cutting, according to our latest study. Here’s what the future looks like for IT infrastructure, including our HEROES framework to guide you along the way.
Wondering how to bring services to your clients in real time – and on their preferred device? Need to automate your financial supply chain, including risk and compliance functions, and move to a pay for performance model?
Learn about use cases from within the big data ecosystem, ranging from AML compliance, trade lifecycle, fraud detection and digital transformation, and introduce their risk data aggregation and compliance initiative. Find out how you can best leverage Open Enterprise Hadoop to achieve these goals.
What are Swiss banks doing well, and where can they improve when it comes to their range of digital offering – and to the user experience of their customers?
Analytics and MBA is a great career choiceHimanshu Arora
This presentation explains how MBA and Analytics go hand in hand. MBA and Analytics skill compliment each other and the combination of two has great demand in industry
Penser Consulting answers the key questions:
- What is big data, and why does it matter?
- How can big data drive business decisions?
- How can you build data analytics capabilities in your organisation?
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The Data Driven Enterprise - Roadmap to Big Data & Analytics Success
Presentation used at the series of Breakfast seminar around Australia hosted by Lenovo/Intel/SAP/EY
Artificial Intelligence: Evolution and its Impact on MarketingZenith
In one real-life minute, Google receives over 4 million searches, 2.5 million pieces of content are shared on Facebook, and Pandora users listen to 61 thousand hours of music. The amount of data that is produced in a day is massive that the world has began to turn to artificial intelligence to make use of this data. Read here to learn about the way that artificial intelligence is revolutionizing the use of big data and how this will impact the world of marketing and business.
Big Data presence in the high volume in the data storages can help in various ways to learn more about the need and trends of the current market which will be useful for all type of organizations. Modern information technology used to analyze the relationship between social trends and market insights is a useful way to have indirectly interlinked to customers and their interests from unstructured and semi-structured data. Such analysis will give organizations a broader view towards the practical needs of customers and once banking industry or any industry could know the customers, they can serve better and with more flexibility. In this presentation, team has primarily created the platform and designed the architecture in big data technology for banking industry to maximize the users of credit card.
BDW16 London - Amjad Zaim, Cognitro Analytics: How Deep is Your Learning Big Data Week
Deep learning, a new class of AI (Artificial Intelligence) algorithms is making big promises to unlock an unprecedented level of intelligence from voluminous forms of structured and unstructured data produced from online data factories and internet-enabled smart devices. But despite the big hype about big data, deep learning and AI in general, less than half of the projects undertaking by companies looking to push the boundaries of analytics through data science fail to deliver the expected results according to a recent Gartner’s study. From our experience, a major factor in this failure is the myopic view of technology coupled with lack of understanding of what’s needed to build an ecosystem of analytics technology architecture, talent resources and systems of governance. We present a national e-health analytics transformation case study where we describe the recipe for how we envision analytics to be able to create the spin-off factor to reshape and revolutionize the industry landscape through our tested and proven framework of “Transform and Digitize”, Inform and Contextualize”, Embed and Institutionalize, “Innovate and Evangelize”. For organizations, large and small, to deepen their learning and win with analytics a holistic approach has to address all the underlying components across the full analytics value chain…. it’s a never-ending journey!
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How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
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Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
2. AGENDA
ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW
MACHINE LEARNING – OVERVIEW
TYPES OF MACHINE LEARNING
MACHINE LEARNING – PROBLEM CLASSIFICATION
CASE STUDIES & DEMO
3. ARTIFICIAL INTELLIGENCE – INDUSTRY OVERVIEW
Investment in AI is growing at high rate
In 2016 , Companies invested
$26B to $39B
in artificial intelligence
TECH GIANTS
$20B to $30B
STARTUP
$6B to $9B
3x External Investment growth since 2013
AI Adoptions – Top 3 industries spending ( % of Spend)
Banking
20%
of Spend
Intelligent Financial
health assessment
Credit scoring worthiness
Healthcare
18%
of Spend
Rapid diagnostic with
Medical images
X-Ray, MRI etc
Retail
17%
of Spend
Interactive personalized
experience analyzes
Shoppers History , mood
, expressions etc
Data around the World Top 5 AI Companies
Face Recommendation Navigation Product Recommendation Speech Recognition Medical System
Benefits
Digitially Mature
Larger Business
• JP Morgan has been able to save 360,000 work hours by automating part of their lawyer work by leveraging an
operation efficacy applications
• UBS implemented AI based platform for post-trade allocation requests . Investment banker took about 45 minutes,
to complete the end to end process , AI implementation helped banker to do task in 2 minutes only
• Korean airlines built AI based expert system to redcue the number of flight dealy and cancellations and shortened its
mainteanance lead times by 90%
• NetFlix leaverages AI to tag its movie content and built recommender system for improving customer experience by
offering personalized product recommendations and helping customers to find desired product faster
Increased Automation Productivity
Uncovering new insights
Increased Employee Productivity
• We are pursuing AI to empower every person and every institution ..so that they can go on to solve the most
pressing problems of our society and economy - Satya Nadela , Microsoft
• Building genral artificial intelligence (AI) in a way that helps people meaningfully – I think the word moonshot
is an understatement for that . - Sunder Pichai , Google
• We are living in the golden age of AI , We are now solving problems with machine learning and artificial
intelligence - Jeff Bezos Amazon
• Artificial Intelligence is the new electricity. - Andrew Ng , Baidu
Top Industry Leader Statement on AIAI Use Cases
90 % of World‘s
Data created in past
2 years
3 Billion Online
In 2000, only 738 million
used internet, In 2015
number grew to 3.2 Billion
According to World
Bank, 75% of people
own a cell phone
204 Million
emails are
sent each minute
4. MACHINE LEARNING OVERVIEW
Machine Learning Used
Machine Learning Tools and Services
Trend Forecasting Fraud Detection Price Prediction Product Recommendation
Interesting Case Studies
Large US based Retail Grocery Store
Observation : Men between 30-40 years in age shopping between 5
PM and 7 PM on Fridays, who purchased diapers along with beer in
their shopping cart
Analysis : Data Analysis revealed that new fathers tend to buy more
beer , because they are spending less time at the pub.
Solution: Used Data Mining and Anaytics approach Retailer shop
move the beer isle closer to diaper isle
Benefits :35 % increase in sales of both
Large Global Insurance company - AXA
Use Case : Approximately 7-10% of AXA’s customers cause a car
accident every year, about 1% are so-called large-loss cases that
require payouts over $10,000.
Objective : AXA adjusters to understand which clients are at higher
risk in order to optimize the pricing of its policies.
Solution : AXA’s R&D team analyzed the historical data ( Age of
Driver , Address , Age of car etc) and using Machine learning
algorithm to optimizing price by large-loss traffic accident
Benefits : Team achieved 78% accuracy in its predictions
Significant advantage for optimizing insurance cost and pricing,
Machine Learning - How it works
Learn from Experience Learn from Data Follow Instructions
What is Machine Learning ?
Machine learning is an application of artificial
intelligence (AI) that provides systems the
ability to automatically learn and improve from
experience without being explicitly
programmed.
Human ComputerMachine Learning
5. TYPES OF MACHINE LEARNING
• Make machine learn explicitly
• Data with clearly defined output is given
• Direct feedback is given
• Predicts outcome or future
Supervised Learning
Teacher teaches kids and
providing the solution to them
Unsupervised Learning
• Machine understand the data ( identifies
pattern and data
• Evaluation is qualitative or indirect
• Does not predict or find anything specific
Predicting the weather forecast
Kids are taking decisions out of
their own understanding
Reinforcement Learning
• Reward based learning
• Learning from +ve and –ve reinforcement
• Machine learns how to act in certain environment
• To Maximize rewards
Kids will take actions on his own
from past exp, and parent
providing the feedback
Decision Tree Random Forest Naïve Bayes Support Vector machineK-Means ClusteringTensorFlow
MACHINE LEARNING ALGORITHM
You Tube Recommendation videos System which play chess
Linear Regression Logistic Regression
6. MACHINE LEARNING – PROBLEM CLASSIFICATION
Classification Algorithm
Anomaly Detection Algorithm
Regression Algorithm
Clustering Algorithm
Reinforcement Algorithm
• Used to classify record
• Limited number of answers
• Analyze the pattern
• Alerts in case of change in pattern
• Used to calculate numeric value
• Predicts outcome
• Sepeartes the data into groups or clusters
• Ease out the interpreation of data
• Design how brains of humans respond
• Learn from outcome , reward , punishment
Email Spam for Gmail and Yahoo
Prediction of house price
QUESTIONS ML ALGORITHM ALGORITHM DESCRIPTION EXAMPLE
Fraud Detection for Credit card
Product Recommendation
Google self drive car
How much or how many ?3
Is this weird ?2
Is this A or B?1
How is this organized ?4
What should I do next?5
7. Business Problem
ML DEMO - PORTUGUESE BANK
age job marital education default balance Housing loan contact day month duration campaign pdays previous poutcome Y
30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown No
33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no
30 management married tertiary no 1221 yes no telephone 25 jul 279 4 -1 0 unknown yes
• There has been a revenue decline for the Portuguese bank and they would like to know what actions to take.
• After investigation, they found out that the root cause is that their clients are not depositing as frequently as before.
• Knowing that term deposits allow banks to hold onto a deposit for a specific amount of time, so banks can invest in higher gain financial products to make a profit.
• In addition, banks also hold better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues.
• As a result, the Portuguese bank would like to identify existing clients that have higher chance to subscribe for a term deposit and focus marketing effort on such clients.
To predict which clients are more likely to subscribe for term deposits
Data Set Description
Name Description and Values
Personal Client Information
Age Age at the contact date (Numeric ≥18)
Marital Status Married, Single , Divorced, Widowed etc
Sex Male or Female
Job Unemployed , 'entrepreneur, technician
Name Description and Values
Bank Client Information
Annual Balance in Euro currency (Numeric )
Default ( Credit) Yes , No , Unknown
Housing Loan Yes , No , Unknown
Pesonal Loan Yes , No , Unknown
Name Description and Values
Last contact information
Contact Type contact communication type
Date When the contact was made
Duration Duration of the contact
Name Description and Values
Other Attributes
campaign No of contacts performed during this campaign
pdays No of contacts performed before this campaign
poutcome Outcome of the previous compaign
Output variable (desired target):
y - has the client subscribed a term deposit? (binary: 'yes','no')
Data Set
Business Goal
8. PYTHON DEMO STEPS FOR BANKING SCENARIO
Import Python Library Packages Pandas , numpy, matplotlib
Loading the Banking data and reading the file
Drop the variables which are not required for prediction
Spliting the data into training set and testing set
Applied logistic regression model using training data set
Checking the score or accuracy of the model
Library File
Data Set
Splitting the Data Set
Data Filter
ML Model
Accuracy
DESCRIPTION PYTHON CODESTEPS
9. REFERENCES
Machine Learning book - Tom Mitchel The Elements of Statistical Learning book -Trevor Hastie
Video Lecture - Andrew NG
Video Lecture - Yaser Abu Mostafa
Artificial Intellignece Reports from IDC , PWC , DB and McKinsey