IBM Counter Fraud Management for Safer Payments (powered by IRIS)

Transcription

1 IBM Counter Fraud Management for Safer Payments (powered by IRIS) Fraud & Compliance - The Way from Prevention to Deterrence Jacek Stańczyk CEE Safer Planet Cognitive Solutions Bucharest 17th of November

2 Agenda The magnitude of the fraud problem in the Industry What is missing with today s systems A new approach from IBM The value of IBM Counter Fraud Management (CFM) for Safer Payments Customer examples IBM Corporation

3 Pressures abound to deliver an optimal Fraud Prevention Program while supporting a bank s omni-channel strategy to help drive business growth A changing cashless payments industry is under attack with new and sophisticated modalities where time to react is dramatically shorter and current defences are hard to adapt Faster payments, rise of alternate payment methods and the adoption of new payment regulations (US adoption of Chip and Pin) Fraudsters are increasingly technologically sophisticated and organized, systematically probing to spot vulnerabilities and exploit them There s less time to evaluate risk, so it is vital to adapt faster, spot new patterns quickly, and have the control to apply countermeasures. Legacy solutions are often hard to adapt black box solutions, do not look across payment types and channels, generating too many false positives 3

4 Why Us, Why Now - Safer Payments Key Differentiators Fraud prevention in any kind of cashless payment system via multiple payment and interaction channels using machine-learning Adaptable Full control over models to adapt to new threats quickly using inhouse skills, no vendor dependency critical for new payment types with evolving threat patterns Fast Industry-leading real-time performance with 1000s of TPS throughput and latencies of <10ms on tens of billions of transactions Effective Ultra low false positive rates using machine learning with real world results in the 1:1-1:3 range* Robust Change data, models, UI, software while running at full load without bringing down the system % Availability. Fully PCI-DSS Certified. Easy Implement in weeks rather than months, change models in minutes rather than days or weeks Distributed Made to be hosted in global, multijurisdictional environment supports hundreds of concurrent portfolios and their models with inheritance 4 *Verified production results. False positive rates in any given scenario depend on environment, data, and risk tolerance.

5 IBM is pioneering new cognitive capabilities to mitigate payment fraud Exceptionally High Performance Cognitive computing Payments Industry ready Ultra-low false positives Proven in real-world environments with industry-leading false positive ratios in the 1:1-1:3 range Democratize modeling Machine Learning with automated model generation, limiting need for scarce data scientists Multi-tenancy with PCI-DSS Single software installation and fully Payment Card Industry-Data Security Standard certified Superior industry real-time performance Process thousands of transactions per second with latencies of a few milliseconds White Box approach, adapt faster Customers can change models in minutes, understanding both lift and false positives No Downtime Inspect the rules, use production data, review the outputs and evaluate a rule s effectiveness without stopping the system Exceptional availability Operates at high availability, activeactive-active Virtual Analyst Advanced analytics techniques allowing rules experts to build models with machine assistance Short Installation Cycle Implement in full production in weeks vs months with light footprint* * short implementation cycles were based on actual client situations

6 Model efficiency 50:1 3:1 VS False positive True fraud Black Box = Vendor Has Control Adapt Faster to Changing Threats White Box = You Have Control 6 Market benchmark Safer Payments

7 IBM CFM for Safer Payments create a Human-Machine partnership, enabling unprecedented agility to respond to fraud threats Cognitive Computing approach: Helps bridge the gap between expert-driven rules and traditional predictive modeling by applying artificial intelligence to partner with human experts in suggesting best fit analytics interactively. Adapt Faster (minutes, no downtime): Adapt faster to address new episodic threats by rapidly developing, testing, and deploying countermeasures More Control: White box approach to analytics helps gives organizations visibility into model results, control to adapt models quickly without vendor inputs, and flexibility to apply new countermeasures in minutes by themselves without advanced kills. High Speed, Scale, and Accuracy: Process more data at faster speeds with better false positive ratios than existing fraud detection systems Democratizes Modeling: Does not require advance data science skills

8 IBM CFM for Safer Payments: Helping to protect the payments ecosystems Payments Processors Merchant Acquirer/ Gateway Network Bank/ Issuer Alternative Payments Providers

9 Safer Payments placement in payment process 9

10 Safer Payments placement in payment process Safer Payments fraud detection 10

11 Safer Payments placement in payment process Safer Payments fraud detection Card profile Cardholder profile Safer Payments fraud detection Merchant profile Safer Payments fraud detection Safer Payments fraud detection 11

12 Safer Payments placement in payment process Safer Payments fraud detection Safer Payments fraud detection Safer Payments fraud detection Safer Payments fraud detection 12

13 What kind of data are used for fraud detection? 13

14 Multi-channel fraud Consumer Loan Log new loan $ balance inquiry new loan $5, new loan $3, Equity Line Log balance inquiry withdraw $8, payment $ withdraw $8, Debit Card Log balance inquiry withdraw $5, deposit $ withdraw $8, transfer $8, withdraw $ Credit Card Log deposit $3, balance inquiry withdraw $ transfer $ withdraw $8, transfer $7, withdraw $ Recap for Mary Smith transfer $7, new loan $5, withdraw $8, withdraw $8, withdraw $ withdraw $5, Mary s accounts are being drained. + New borrowing + She s usually a saver! = Trouble, visible only across accounts

15 How works Safer Payments? 15

16 Business secure approach and very fast 16

17 Safer Payments fraud prevention application levels Transaction level Monitor transaction level activity to identify abusive behaviour Real-time fraud detecion for transactions including CNP (card not present) transactions Account level Monitor account level activity to identify abusive behavior Abusive patterns include frequent payments, suspicious profile change Network level Monitor account-to-account interaction Frequent transfer of money from several accounts to one central account Anti-Money Laundering

18 Examples of Scale, Speed, Accuracy French national payments switch sees 75% of French cards (4.7B txns per year) at 1ms response time. Scores all authorizations, debit and credit, domestic and cross-border in real-time for both issuing and acquiring Protects 61 million cards; 1.8 million merchants; 73,000 ATMs; mobile wallets Provides two-factor authentication with discretionary SMS 1 ms, end-to-end processing time on one cluster Tested for 4,000 transactions/second sustained, 12,000 TPS peak; 5ms roundtrip max response; operating at 1ms With an average response time of less than five milliseconds per transaction even during peak periods when we are processing over 750 transactions per second, IBM Safer Payments enables us to detect potential fraud without adding any notable overhead to our service. In addition, we are able to respond to newly identified fraud patterns by deploying new countermeasures in a few hours without taking down the system. 18 Pierre Juhen, Deputy CEO of STET Leading alternative payments network for 17 Eastern European countries stays open while competitive networks shut down Quintessential Honey Pot for Fraud 75 million consumers / active users 20 million VISA pre-paid cards (virtual and plastic) 200,000 ATMs 15.5 million e-wallet accounts 50B rubles in payments per month IBM Safer Payments provides us with the confidence that we can immunize our services against any type of fraud patterns within a matter of hours. - Anton Kuranda, Chief Security Officer US Commercial Card Issuer enjoys 3x higher fraud loss protection than industry with 1:3 false positives Our MasterCard portfolio had 12 basis points of fraud. With IBM Safer Payments we were able to reduce this to 2 basis points. At the same time we reduced our decline false positive rate to 1:3. - Freddy Ramirez, Vice President, Risk & Compliance, Comdata

19 Welcome to the New World

20 IBM Counter Fraud and Financial Crimes Thank You IBM Corporation 14 December 2016

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