Developing Efficient Transaction Monitoring Processes with Limited Resources

Transcription

1 Developing Efficient Transaction Monitoring Processes with Limited Resources 10 December Moderator: Ursula M'Crystal, Head of Money Laundering Surveillance, Global Financial Crime, Standard Bank Presenters: Dr. Ana Cristina Hopffer Almada, Programme Manager, African Innovation Foundation Solomon Kofi Dawson, Head, Compliance & AMLRO, unibank Ghana Limited Chris McAuley, Director of Fraud & Financial Crime, Advanced Analytics Business Unit (AABU), SAS

2 Developing Efficient Transaction Monitoring Processes with Limited Resources 10 December Dr. Ana Cristina Hopffer Almada, Programme Manager, African Innovation Foundation

3 Discussion Item #3 Exploring affordable tools and resources for monitoring suspicious transactions 3

4 Discussion Item #3 Resources Human Technological 4

5 Discussion Item #3 Human Resources Knowledge Compliance Culture Training Relation management 5

6 Discussion Item #3 Tools (AML Organizational Components) Primary Level Governamental Level 6

7 Discussion Item #3 Primary Level Technologies Risk Management Software Identification Software 7

8 Discussion Item #3 Role of Technology What can do? What cannot do? 8

9 Discussion Item #3 What can you do? 9

10 Discussion Item #3 Thank you! 10

11 Developing Efficient Transaction Monitoring Processes with Limited Resources 10 December Solomon Kofi Dawson, Head, Compliance & AMLRO, unibank Ghana Limited

12 Transaction Monitoring Process in TBML Risk Based Approach for Customer Onboarding and screening Improve on Customer Acceptance and Nature of business alignment Expected volume of transaction through internal thresholds Trade Documentation Review through key document 12

13 Risk Based Approach for Customer On-boarding and screening Low Risk Medium Risk. Medium High. High Risk. 13

14 Nature of business alignment Synchronizing customers CDD responses to the trade transactions Compare nature of business with nature of trade transaction Expected volume of transaction Key trade documents review 14

15 Developing Efficient Transaction Monitoring Processes with Limited Resources 10 th December Chris McAuley, Director, SAS Institute

16 Common Objectives INCREASE DETECTION RATES Identify more sources of non-compliance Ensure fewer cases go undetected ACCURACY Reduce false positives Focus on cases with higher yield EFFICIENCY Work cases and inspections faster Remove time wasted on data collection TOTAL COST OF OWNERSHIP Single, integrated platform Leverage investment over multiple business areas 16

17 The Importance of Analytics Predictive modelling (example): Finding customers with sources of funds similar to other entities in the Enterprise that case officers have determined are US liable Text mining (example): Examination of customer correspondence (inc s) to find phrases or words indicative of an association with a US entity. Anomaly detection (example): A customer with a higher ratio of US destined transactions than the peer group Anomaly Detection Business rule (example): An applicant providing a US address Automated Business Rules Predictive Modeling Analytic Decisioning Engine Database Text Mining Searches Social Network Analysis Database Searches (example): Looking for matches across the known industry watch lists SNA (example): A number of people on a network who are US tax liable, together with ones who appear to have avoided internal DD illustrated using sample FATCA examples 17

18 Deployed in an Industry Standard way 18