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Graphs for Finance - A technological background

Carsten Weske, Neo4j

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Graphs for Finance - A technological background

  1. 1. How come In the United States, AML compliance staff increased up to tenfold at major banks between 2012 and 2017 ! McKinsey Report: New Frontier in AML Laundering There is still no automated STP Path automatisation ?
  2. 2. Cross-Influence Credit Risk do not lose funds neither customer’s nor own Due Diligence do not support any kind of financial crime know customer backgrounds Payments transfer funds from A to B instantly and at lowest cost
  3. 3. Origins AML Criminal Activity Onboarding Underwriting Counterparty Domestic Cross-Border Due Diligence Credit Risk Payments STP Transparency Ultimate Entities
  4. 4. Origins | Ultimate Entities Beneficiary name & address Transaction service levels & fees Fees charged to whom Sender’s reference Beneficiary bank details Sender Name & address Currency and amount (Transaction Monitoring) Transaction settled date Account credited or debited Transaction ID Transaction start date Intermediary bank Sending bank Payment message network
  5. 5. Origins | Ultimate Entities Beneficiary name & address Transaction service levels & fees Fees charged to whom Sender’s reference Beneficiary bank details Sender Name & address Currency and amount Transaction settled date Account credited or debited Transaction ID Transaction start date Intermediary bank Sending bank Payment message network Zaki-ur Rehman Lakhvi Mastermind 2008 Mumbai attacks one of them School of Thought Ahl-i Hadith Charity Foundation Falah-e Insaniat Terrorist group Lashkar-e Taiba supportsvarious underprivileged schoolsinIndia
  6. 6. Origins | Ultimate Entities Payment Messages queue Not suspicious to payment processing system Suspicious to investigation Source: ACAMS - Association of Certified Anti-Money Laundering Specialists http://www.acams.org/wp-content/uploads/2015/08/AML-Rule-Tuning-Applying-St atistical-Risk-Based-Approach-to-Achieve-Higher-Alert-Efficiency-U-Luccehtti.pdf Transaction Monitoring System Matching Rules Engine (nickname “Bucket”)
  7. 7. Origins | Ultimate Entities Sender Depth 1 Beneficiary Depth 2 Beneficiary Middleman Depth 3 Sender Middleman Depth 2 Ultimate Beneficiary Depth 4 Ultimate Sender Depth 3 Ultimate Sender Depth 3 Depth MySQL Neo4j 2 0.016 0.010 3 30.267 0.168 4 1,543.505 1.359 5 Not finished in 1 hour 2.132 Execution time in seconds for 1,000 users https://neo4j.com/news/how-much-faster-is-a-graph-database-really/ Real Transaction Depth 5 not automated = not scalable
  8. 8. KYC / DD • Onboarding, PEP, Watchlists, CRM, News Aggregators • Transaction History, Payment Patterns Origins | Ultimate Entities Screening Analysis • effective false positive / negative reduction • improved detection capability (i.e. fraud, mirror trades, criminal activity funding, tax evasion) • lower opex • faster decisions equals faster funds releases
  9. 9. Origins | STP Transparency SWIFT 40 USD Paris, France EUR Fax 0 USD Henan, China Renminbi 7 days to pay invoice
  10. 10. Origins | STP Transparency 1 day 3 days SWIFT SWIFT Cirrus Cirrus Fax 20 USD Geneva, Swiss EUR 20 USD Zhenzhou, China EUR -> Renminbi 40 USD Paris, France EUR 2 days 1 day Fax Link Fax SWIFT Link 15 USD Yokohama, Japan USD -> Renminbi 10 USD Rome, Italy EUR -> USD 0 USD Henan, China Renminbi SWIFT Fax 80 USD Singapore EUR -> Renminbi 1 day 1 day 1day 5days
  11. 11. Payment Efficiency Data Files • Correspondent Management System • Rating Agencies • KYC & DD Origins | STP Transparency Screening STP Transparency • always optimal path • less manual payment repairs • end to end transparency • full integration with compliance • less need for recouping funds • loop back into payment repairs
  12. 12. Origins Not Symptoms lower risklower cost closer to real-time get 3 out of 3, not 2 out of 3

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