Business Integrity Screening: A Case Study from the Insurance Industry. Stephan de Jong, Luc Hartering, 15/03/2018

Transcription

1 Business Integrity Screening: A Case Study from the Insurance Industry Stephan de Jong, Luc Hartering, 15/03/2018

2 Who are we? Stephan de Jong Manager Business Intelligence (BW/BO/GRC/FRD) and Strategic lead SAP Responsible for: - All strategic topics related to SAP SAP BI & BASIS teams - Innovation and automatization (CI/CD) - Further development Business Integrity screening Platform Lead SAP Luc Hartering Responsible for: - Strategic platform developments - Leading 2 teams with the purpose to enable innovation and increase maturity of security - Managing contract Hana Enterprise Cloud

3 About NN Group Operating in 18 countries - Insurance - Retirement services - Banking - Investment Management Complex landscape Multiple non sap source systems Since 1845 Target SAP Landscape 15 million customers Listed in Euronext Amsterdam since employees Public listed company since 2014 Acquisition of Delta Lloyd

4 Insurance fraud trends and facts 10% 100 CIS M/F 70%/30% Percentage Fraud comprises 10% off all claims * Costs Fraud costs are about 100 euro per household on a anual basis * Top 3 Most fraud cases related to car insurance, second household insurance and third liability insurance Social Social media data can uncover suspicious activities Application stage Suspicious customer behavior in the application stage can be used for fraud detection CIS Data sharing in insurance consortium level can uncover customer link suspicious networks. Male/Female Men commit insurance fraud more often than women * Cyber Cyber protect insurance to ensure Identity theft * According to figures by Dutch insurers association

5 NN Group challenges Longer end-to-end fraud detection times with high manual intervention and delay in fraud detection time Variety and Volumes in Insurance fraud trends Complexity in utilizing past fraud trends to predict fraud Claimants Employees Higher reactive vs. lower proactive fraud detection Fraud sources High cost to ROI ratio in manual detection methodologies. No single accessible interface to process, maintain and mitigate fraudulent cases Evaluation of the authenticity of the claim is time consuming Policy holders

6 Objectives implementation 1 Detect anomalies earlier to reduce financial loss - Leverage the power and speed of SAP HANA - Integration into business processes - Alert notification and management 2 Improve the accuracy of detection at less cost Title Goes Here of lorem ipsum available majority have suffered - Minimize false positives with realtime simulations - Ability to handle ultra-high volumes of data by leveraging SAP HANA - Detection based on rules and predictive analytics to adapt to changing patterns - Identification of control enhancement needs 3 Better predict and prevent future occurrence

7 Current landscape Fraud Management Business Warehouse ERP/BP/CM/Other sources Payment factory

8 Fraud detection strategy 01 Set strategy Define needed objects Define methods Like age, region, amount of invoice, period, etc. Define weight factors Per method 04 Simulate! Define detection strategies based on fine granular criteria. The business defines the individual weight factors and thresholds. Business can set up and calibrate fraud detection strategies with minimal IT involvement

9 Simulation and calibration Business can do the real-time simulation and calibration on the fly. This reduces false positive and streamlined fraud detection. Based on this mass detection can be executed to process high volume of data.

10 Mass detection results-alerts Alerts will be generated from the mass detection. It will also provide the risk ratings. Based on the risk rating, and other parameters they will be assigned to Fraud Investigators.

11 Investigate The alerts are investigated by the Security and risk officers and status is updated

12 Journey ahead System configuration Initial setup of application and connect first sources Mass detection Conversion of phase-1 mass detection rules to online detectable rules Online detection Marketing the software within the organization to increase adoption Scale up Scaling up the application with more no. of rules and use more sources Apply data science Apply data science methodologies to detect complex fraud patterns

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