1. Technology & Analytics
in implementing a risk based approach
Banking Compliance Symposium (Sri Lanka) Aug 2016

2. Necessary Features Analytical Tools Profiling & Peer Analytics
Rule Builder
Rule Library
Profiling & Peer Analytics
Analytical Tools
Comprehensive Case Management
Workflow Management
Link Analysis
Rule Simulations

3. Customer Demographics
Risk Profiling
Customer Risk Categorisation (CRC) is the process of assigning a value to the attributes of a customer of a financial institution, grouping them by and identifying the higher, the neutral (medium) and the lower risk customers.
Risk Variables
Customer Demographics
Product
Transaction
Risk Categories
Geography
Customer Type;
Length of Relationship;
Occupation;
Industry Sector; and
KYC Compliance
Risk Categories
Insurance
Risk Variables
Less than 1 year
One to three years
Greater than three years
Risk Factors
Risk Factors1
1 –for illustrative purposes only.

4. Transaction Monitoring
Patterns help in identifying complex activities involving multiple rule breaches over a period of time
Aggregating Transactions into Events, Patterns, Alerts and Cases for effective, centralized and customer level monitoring
Rules are the basic monitoring algorithms which run as per benchmarks defined
All the Events and Alerts triggered for a customer are presented as a single case of consolidated investigation
Pattern 1
Alert 1
Alert 2
Alert 3
Pattern 2
Event 1
Event 2
Event 3
Event 4
Event 6
Event 7
Rule 1
Rule 2
Rule 3
Event 5
Case 1
Non-patterned events are also displayed in the case
Customers who trip rules will trigger events which are then used for tracing patterns
Events of a customer are analyzed to find patterns which are in turn marked as alerts

5. Workflow Analyst Reviewer Approver Rules Library Assignment Rules
- Customer Type
- Product Type
- Risk
- Branch / Zone
- Segment
- User Defined
- In-built
- Manual
Source Data
Alerts & Cases
Rules Engine
Assignment Engine
Forward
Forward
Analyst
Reviewer
Approver
Re-Assign
Re-Assign
Comments
Documents,
Links & Tags
Notifications
Reminders &
Escalations

6. Rule Builder
GUI based Rule builder to assist business, risk and compliance analytical teams to build scenarios for detecting patterns

7. Peer Groups
Dynamic self and peer profiling to establish base-line profiles for customer segments using customer demographic and transaction data.

8. Analytics
Dashboards to manage and prioritize customer alerts.

9. Customer Level View
Data visualizations of customer transactions overlaid with summaries, alerts indicators and key information changes quickly allow analytical teams to hone in on areas of relevant interest

10. Peer Analytics
Comparison of transaction Peer Profiles can indicate customers who can potentially be offered more products/incentives due to use of a particular channel or are high risk and are conducting potentially suspicious activity

11. Customer Networks

12. Network Analysis
Quick configuration of network depth is an essential feature for analysis
Filters on Transaction Types, Channels, Counter Countries, Aggregates and Volumes are key for quickly dispensing on false positives and homing in on the most relevant & crucial items

13. Simulation
Rapid Simulation to better deploy resources and understand business, compliance & risk patterns on historical data is a key component of automated analytics

14. Simulation
Statistical distribution patterns are essential to save time, money and consequently effort on a large scale enterprise analytics & risk management. Such analysis helps teams prioritize and focus efforts on higher risk cases/alerts or patterns

15. Thank You
Questions