Solving the Data Challenge in Compliance

2. Hanjo Seibert Accenture – MD, Compliance Lead hanjo.seibert@accenture.com +49 15157142279 Isabelle Flückiger Accenture – MD, Data Analytics Lead Isabelle.flückiger@accenture.com +41 79-654-0726 SPEAKERS 2Copyright © 2019Accenture.All rights reserved.

3. TODAY: MISSION IMPOSSIBLE 3 The banking industry needs to identify moneylaunderers, terroristsand fraudsters, howevertraditional approaches struggle to “discriminate”well Without access to relevant data, everyone looks the same Client #2 Client #4 Client #5 Client #1 Client #3 Client #6 Copyright © 2019Accenture.All rights reserved.

4. IMAGINE … 4 How much more is possible by bringing integratingdata and applying new analytical approaches With all our data in the right place we are able to “connect the dots” Client #2 Client #4 Client #5 Client #1 Client #3 Client #6 Copyright © 2019Accenture.All rights reserved.

5. 5 Data sharing acrosssilos ... All the information is in the right place, but... Shared & integrated analytics ... It only comes together with the right analysis being performed... Data & insights driven business decisions ... With business learning how to take insights driven decisions... WHAT IT TAKES X Copyright © 2019Accenture.All rights reserved.

6. SHARING: WHAT IT CAN LOOK LIKE One centralplatform for E2E processes Business Rules Data Quality Data Model INTEGRATED ANALYTICS PLATFORM Data Layer 1 Data Layer 2 Data Layer .. SOURCES Ext.data sources Internaldata architecture Static data Products Transactions SKILLED TEAM Governance & security Usecases News Authorities Lists …TM KYC TAX RM Filt. … Streamlined processes AI & ML technology Integrated Regtech Advanced analytics Drivers • Business SME • Business expert • Data scientist • IT specialist • ... Up-skilled, integrated team Integrated data set Intelligence & analytics led 6Copyright © 2019Accenture.All rights reserved.

7. SHARING: BENEFITING THE ENTIRE ORGANIZATION Increased efficiency and reduced cost – enabling savings of > 50 % Avoided duplicate data sourcing and data infrastructurecosts by using a single platform < 20m Derisking Compliance by increasing rigor and traceability of Financial Crime data 7 Quick data analytics in 2 to 4 weeks possible through bundling relevant data in one central place Copyright © 2019Accenture.All rights reserved.

8. CASE STUDY: BRINGING ANALYTICS TO ACTION 8 New, machine learning approaches (random forest) are able to significantlyboost performance of detectingSARs Machine learning model Static data Machine learning model Static, account & TM data SAR hit ratio: 66% False positive ratio: 94% SAR hit ratio: 91% False positive ratio: 89% Total alerts raised: 1,700 Total alerts raised: 300 Installed monitoring tool Transactiondata SAR hit ratio: 7% False positive ratio: 99% Total alerts raised: 2,500 Copyright © 2019Accenture.All rights reserved.

9. CASE STUDY: FUZZY NAME MATCHING 9 Additional / ad-hoc analyses can easilybe created,using the createddata infrastructureand analytics layer... Fuzzy name process Leak data sources Bank Data Comparison List on entry matches Dictionary & name harmonization Dictionary & name harmonization Matching logic example1 Firestone LTD Fivestore LTD 2 Leaked data Client data Differentpositions Matching results Distance Client base Perfect match >1002 1 Position 300 2 Postitions 2,000 • The developed crawler screened over 1 million leak data entries • Dictionary was setup and data sources were cleansed for the screening process 1Levenshtein distance The final list contained findings for further investigation on potential fraudulent behaviour and the connection to financial crime activities 2anonymized “Cleaned” leak data sources “Cleaned” Bank data Copyright © 2019Accenture.All rights reserved.

11. WHY TRADITIONAL APPROACHES WILL FAIL Difficulty deciding how and when to source, progress and move new data sources Systems are not capable of supporting new and evolving data sources New capabilities are often siloed efforts or POCs without clear vision Difficulty building and managing complex, hybrid data platforms New self-service BI and analytics models require an evolution of traditional operating models Considering talent pools is increasingly important when making tool and technology choices 11 Analytics ProcessesData Copyright © 2019Accenture.All rights reserved.

12. IN OUR VIEW A DECOUPLED ECOSYSTEM IS AT THE HEART OF THIS CHANGE 83% of C-suite professionals believe that their legacy systems hold value that they do not want to lose by changingto new systems 12 TOO LONG (3-5 years) TIME CURRENTLY WE ARE HERE Recognizing that legacy approaches are too slow to adjust A program to fix things EFFICIENCY Cruising, focus on new things Slow decline Re-boot… (consolidate, replace, multi-year programs) … and it continues, faster and in shorter cycles Copyright © 2019Accenture.All rights reserved.

13. 13 Streamline Reduce high cost workloads by moving the data out of the core Build out new functionality not possible in the core Complement Hollow-Out Replace the core, piece-by-piece Replace Replace the core, at once 5 components to decouple successfully New Build out something that did not exist before GETTING THERE USUALLY REQUIRES BANKS TO TAKE A STRUCTURED AND SEQUENCED APPROACH 1 2 3 4 5 Copyright © 2019Accenture.All rights reserved.

14. … Modern Components … … Coexisting Legacy as Systemof Record Legacy in closed book run-off or decommissioned .. … … … Channels … Greenfield Core Real-time Data Lake Single Version of the Truth Enterprise Back-End Legacy Core HR … Proc … … … … … … … … … .. … .. … … .. … … .. … … … … … … … … Pool 2 Data Access Protection MDM Pool 3 Pool n…Staging Area Pool 1 BCBS239 Reg Reporting IFRS Reporting Posting Engine Enterprise MDM Doc Mgmt & Archiving Ops Compliance GL … … … … … ……… … … Write APIs & messages Events Events Change Data Capture Streams Digital DecouplingFilter relevant clients and dataTransform for consumption StraightThroughProcessDIGITAL DECOUPLING IN ACTION Event Streams 14 Decoupling Layer Copyright © 2019Accenture.All rights reserved.

16. 16 Recommended approach 1. Focus on solving the data challenge, not the compliance challenge 2. De-couple your ecosystem / infrastructure 3. Consolidate relevant data sources 4. Setup integrated, shared analytics HOW TO GET STARTED... Copyright © 2019Accenture.All rights reserved.