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Using Machine Learning & AI to Enhance Fraud Detection

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Using Machine Learning & AI to Enhance Fraud Detection

  1. 1. ASSET FINANCE INTERNATIONAL Machine Learning & AI Richard Harris | Head of International Operations
  2. 2. © 2016 Feedzai 2 Car sales circa 1950
  3. 3. © 2016 Feedzai 3
  4. 4. © 2016 Feedzai 4 Still our model for selling cars
  5. 5. © 2016 Feedzai 5  43% ’fear’ sales person pressure  33% worry about negotiating  23% find it hard to find the right dealer Auto Trader Study But are customers satisfied?
  6. 6. © 2016 Feedzai 6  48% - Finance amongst most difficult part of car purchase  56% - Want to manage car finance online  75% - Of the industry has not digitized loan originations by consumers Intelligent Environment Study But are customers satisfied?
  7. 7. © 2016 Feedzai 7 We_now_live_in_a knowledge_economy #brainup
  8. 8. © 2016 Feedzai 8 A_new_era manual labor INDUSTRIAL REVOLUTION brain power COGNITIVE REVOLUTION
  9. 9. © 2016 Feedzai 9 AI_is_making_it_possible_for big_or_small_to_join_the cognitive_revolution • J.P. Morgan’s banking house • John D. Rockefeller’s Standard Oil Company • Andrew Carnegie’s Carnegie Steel • Ford Motor Company 1900’s REVOLUTIONARIES • Google • Uber • Facebook • nuTonomy 2000’s REVOLUTIONARIES • Netflix • Amazon • …and you!
  10. 10. © 2016 Feedzai 10 Those who adapt will win
  11. 11. © 2016 Feedzai 11 nuTonomy_beats_Uber_&_Google AI startup in Singapore offers world’s first self-driving taxi service #underdog WINNERS
  12. 12. 13 EASI’R predicts when you’ll buy a new car before you know it yourself
  13. 13. © 2016 Feedzai 14  Buy or Sell Your Car Online  Use Your Credit Cards To Pay
  14. 14. © 2016 Feedzai 15 What we all want: My __ within arms reach of desire
  15. 15. DATA IS DRIVING ALL OUR DECISIONS © 2016 Feedzai 16
  16. 16. BUT WE JUST DROWN IN IT… © 2016 Feedzai 17 DATA, MORE DATA • 90% of world’s data created within last 2 years • IOT – Internet of Things is coming
  17. 17. UNTIL MACHINE LEARNING CAME ALONG © 2016 Feedzai 18 US UK
  18. 18. © 2016 Feedzai 19 ML AUTOMATES AUTOMATION “3D Printer” RULES/PROGRAMS AUTOMATE “Tools” VS BUILT TO TAME COMPLEXITY
  19. 19. WHAT CAN IT DO FOR YOU? © 2016 Feedzai 20 Influence Discovery Right Offer Right Time More Loan Sales Less Risk
  20. 20. HOW DOES IT WORK? © 2016 Feedzai 21 • Influence Discovery • Right Offer, Right Time • More Loan Sales, Less Risk Machine learning models 1. Offers engine 2. Next best action 3. Credit models 4. And more… Omnichannel Data Combine data sources from all channels
  21. 21. 4 CRITICAL FACTORS 22 To having ML be effective for you 1.Use ‘Segments of One’ for personalization and high precision 2.Integrate cross channel data for better decisions 3.‘Whitebox’ decision making for compliance 4.Real-time decision making for great customer experiences
  22. 22. SEGMENT OF ONE | OBLITERATE LOOSE-FITTING COHORTS © 2016 Feedzai 23 Hyper-granular level of understanding of each data element, whether that be customers, devices, products, etc. Example entities • Customer Segment – Baby Boomer, Millennial, etc. • Account Type – Gold, Platinum, Silver, etc. • Merchant Category – Digital Goods, Fashion, etc. • Device Class – iPhone, iPad, etc. Only dozens, hundreds of cohort groups Cohorts are loose, generic profiles that group together very different entities 1
  23. 23. SEGMENT OF ONE | OBLITERATE LOOSE-FITTING COHORTS © 2016 Feedzai 24 Hyper-granular level of understanding of each data element, whether that be customers, devices, products, etc. Entity • Each Customer – John Smith • Each Account # – 134149553 • Each Device ID – WERB590902BYT Segments of One are very precise profiles for just one entity (across a breadth of time). 1 Profiles learn over time Knows that Martha just got promoted, loves BMWs and has a birthday coming up. Knows that Jake (thin file) pays rent and dues on time for the last three years.
  24. 24. @2016 Feedzai BLACK BOX TO CLEAR BOX 25 Typically Machine Learning is ‘Black Box’ – cannot see why decisions were made. By changing this, we have fundamentally shifted the possibilities for Machine Learning. 3/23/16 MURKY DECISIONS CLEAR ANSWERS 2
  25. 25. INTEGRATE CROSS CHANNEL INTELLIGENCE © 2016 Feedzai 26 • Companies with omni-channel customer engagement retain 89% of customers • 68% of fraud is cross channel • Fraud for multi-channel merchants via the the online channel has gone up by 31% Omni channel visibility increases fraud detection while preserving customer experience 3
  26. 26. REAL TIME DECISION-MAKING IS TABLE STAKES © 2016 Feedzai 27 • More and more we expect instant response… • Same day delivery? • Expectations are always increasing • Yet you have to treat everyone as an individual. Self learning models increase the speed of detection 4
  27. 27. © 2016 Feedzai 28 Knowledge will_set_you_free #brainup
  28. 28. 29 Feedzai is Artificial Intelligence. Keep commerce safe and create a better customer experience through machine learning. INVESTORS QUICKFACTS MISSION WHAT OTHERS SAY The U.S. market fraud prevention just got a new player. Feedzai’s machine learning is the next wave. Ranked as a cool technology to watch. Startups that are owning the data game. Payment Card Management: Essential tools for U.S. card issuers • Top 50 High Growth startups in Europe • Founded by data scientists and aerospace engineers in 2009 • 130+ employees and doubling • Offices in Portugal, Silicon Valley, New York City, London • Series B funded by Citi, Capital One, Oak HC/FT, DCVC and Sapphire Ventures (SAP) © 2016 Feedzai
  29. 29. © 2016 Feedzai Confidential 30 INTRODUCING A NEW BREED OF A.I. SYSTEMS 3. Maximum performance due to iterating over 200- 300 risk models in days/weeks, not months. 1. Time taken to develop new risk models: 2-4 weeks 2. A single platform that launches new use cases in weeks. A PLATFORM THAT CREATES THOUSANDS OF SMART RISK MODELS
  30. 30. FEEDZAI WORKS ACROSS THE ENTIRE CUSTOMER LIFECYCLE Managing risk and creating the optimal customer experience using machine learning. Account Opening Real-time Marketing Lending Decisions Churn Prediction Reactivation Retargeting Fraud Screening Sanctions Screening KYC Trans/Merchant Monitoring Cross-sell/Up-sell Login Screening/ATO NEW DECISION CAPABILITIES NO LONGER CONSTRAINED BY DATA SILOS ACTIVATION ACTIVITYATTRITION ACQUISITION © 2016 Feedzai Confidential 31
  31. 31. THANK YOU Richard Harris | richard.harris@feedzai.com

Notes de l'éditeur

  • http://www.lookers.co.uk/news/auto-trader-car-buying-study/
  • http://www.intelligentenvironments.com/info-centre/press-releases/the-car-finance-industry-is-in-dire-need-of-disruption-financial-technology-firm-intelligent-environment-says
  • We now live in a knowledge economy.
  • In the Industrial Revolution, it was about manual labor.
    In this new era, it’s about brain power winning over muscle power.
  • AI is powering the cognitive revolution, and making it possible for companies large and small to compete. It’s leveling the playing field.
  • There will be winners and losers.
    A winner is nuTonomy. They have the worlds first self-driving taxis, before Uber.
  • Insurify, a startup out of MIT, announced the launch of Evia (Expert Virtual Insurance Agent), an artificially intelligent virtual insurance agent that aims to find you better car insurance using a photo of your license plate.

    However, Insurify simplifies the way it gets you that quote by asking you to snap a photo of your license plate and text it to EVIA. The robo-agent then scours millions of records to verify personal information and driving history and then delivers policy quotes and recommendations back to you via text message.
  • “Silicon Viking” startup — EASI’R — has launched its bid to disrupt the old, antiquated car sales industry with an intelligent algorithm that predicts when customers will buy new vehicles, even before the customers know it themselves.

    EASI’R’s new algorithm works within its CRM from the first second it is switched on, thanks to over 20 million customer interactions collected over a ten-year period.

    The solution analyzes patterns in customer behavior, then clusters those customers by demographic data, online search behavior, and transactional data, even drawing on sales interaction records held in the CRM. Using that information, and its understanding of millions of customer interactions, it predicts the most promising next steps for each customer.

    And it does this throughout the buying cycle, guiding salespeople to take the next steps and helping them send the right information at the right time.

    For example, EASI’R can predict when a customer is going to buy a new car, a trigger every car salesperson wants to understand.
    If a customer has rejected an offer, for example, the algorithm predicts when the ideal time would be to contact them again, and with which counteroffer, to increase the likelihood of closing an alternate deal.

    With its knowledge of car-buying customer patterns, the algorithm can tell the automotive dealers when to send content, what specifically should be sent, and which content delivery channel would be most effective at that particular time.
  • There are lots of apps that help you research prices for buying or selling a car, and they’ll even hook you up with a dealership when you’re ready to buy.

    But Beepi lets you buy and sell used cars in the US without using a dealership — or a test drive — at all.

    So how do you buy a car with an app but without testing it? “The same way you buy anything online,” Resnik said. You find a car you like at a price that works for your budget and buy it. A Beepi representative goes to the seller, gives her the money and delivers the car to you. Then you get what Beepi calls a 10-day test drive. If the car doesn’t work out for you, you get your money back.

    Part of expanding the market is offering a streamlined financing process. Beepi has partnered with Chase and credit unions to offer traditional financing and, as of this morning, Ally Financial to offer leasing. You can even pay for the entire car with a credit card or Bitcoin.

    With Beepi, “there’s no break between online research and online buying,” said Resnik. “You start the process online and finish online. That gives us the advantage.”
  • Data enables machine learning to make our lives easier
    Self-driving cars
    Spam filters
    Drone delivery
    Space and the unknown
  • ALL INSIDE A COMPUTER THE SIZE OF A HOUSE BRICK
    $2 Billion per day in volume
    400 dimensions
    3000 transactions per second
    99.999% availability
  • Machine learning is to cognitive labor
    What 3D printers are to human labor

    The world has evolved from humans just building a machine to get their work done faster
    Its about building a machine that builds machines that do 100-1000 times as much work as before
  • E.g. Big Data allows us to offer a better customer experience by…..
    talk about how we solve a bank’s “thin-file” account applicants who are credit-worthy and credit-history-poor. They are however “data-rich” so we tap into alternate data such as college attended and education major to make informed decisions using better indicators of default loss than traditional car/home payment history.


  • E.g. Big Data allows us to offer a better customer experience by…..
    talk about how we solve a bank’s “thin-file” account applicants who are credit-worthy and credit-history-poor. They are however “data-rich” so we tap into alternate data such as college attended and education major to make informed decisions using better indicators of default loss than traditional car/home payment history.


  • Data is being created across multiple channels. In 2016, 1/3 of online transactions are predicted to happen on mobile.
    Lines between online and offline are blurring.
    Information silos limit the opportunity to identify fraud that crosses channel borders or offer richer experience to customers that matter.

    Financial companies have realized that they are not utilizing customer data as they effectively as Google or Facebook do. For example, if a valued customers has a credit card, or a mortgage loan that could benefit from refinancing. A simple adoption of machine learning is to make them a relevant offer.

    When the customer accesses the bank’s online channel, calls a call center, or visits a branch, that information is available to the online app, or the sales associate to present the offer.




    Data sources
    Xx
    Lexis Nexus true cost of fraud 2015
    Aberdeen study
  • Fulfillment delays leads to customer dissatisfaction
    Digital downloads with instant delivery models are exploding – digital music, game downloaded, instant money transfer are changing the speed of commerce

    In high-volume environments, that data arrives at incredible rates, yet still needs to be analyzed and stored.

    Which is the way big data gets big - through a constant stream of incoming data.

    Making decisions during customer engagement window when it matters



  • Point solutions -> Unified platform supporting all channels and business lines
    Member credit unions buying solutions for unmet needs -> CO-OP offers customized solutions per credit union or segment
    Static risk models -> A machine that iterates over hundreds of models for maximum effectiveness
    Old technology, e.g. Neural Networks -> State-of-the-art technology, e.g. Random Forests, Deep Learning, Unsupervised Learning
    No models for new types of business/channel fraud, e.g. Kiosk fraud, Account Takeovers -> Be able to generate robust models for new fraud types in weeks

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