business integrity screening: a case study from the insurance … · 2019-01-25 · a case study...
TRANSCRIPT
Business Integrity Screening: A Case Study from
the Insurance Industry
Stephan de Jong, Luc Hartering, 15/03/2018
Who are we?
Stephan de Jong
Manager Business Intelligence (BW/BO/GRC/FRD) and Strategic lead SAP
Responsible for:- All strategic topics related to SAPSAP BI & BASIS teams- Innovation and automatization (CI/CD)- Further development Business Integrityscreening
Luc Hartering
Platform Lead SAP
Responsible for: - Strategic platform developments- Leading 2 teams with the purpose to enableinnovation and increase maturity of security - Managing contract Hana Enterprise Cloud
About NN Group
Operating in 18 countries
- Insurance- Retirement services- Banking- Investment Management
Acquisition of Delta Lloyd
Complex landscape
Listed in Euronext
Amsterdamsince 2014
Public listed company
since 2014
Target SAP Landscape
15 million customers
Since 1845
17.500 employees
Multiple non sap source systems
Insurance fraud trends and facts
PercentageFraud comprises 10% off all claims *
Top 3Most fraud cases relatedto car insurance, second household insurance and third liability insurance
Application stageSuspicious customer behavior in the application stage can be used for fraud detection
CostsFraud costs are about100 euro per householdon a anual basis *
SocialSocial media data can uncover suspicious activities
CISData sharing in insurance consortium level can uncover customer link suspicious networks.
10%€100 CIS
Male/FemaleMen commit insurance fraud more often than women *
M/F70%/30%
* According to figures by Dutch insurers association
CyberCyber protect insurance to ensure Identity theft
NN Group challenges
Complexity in utilizing past fraud trends to predict fraud
High cost to ROI ratio in manual detection methodologies.
Evaluation of the authenticity of the claim is time consuming
Variety and Volumes in Insurance fraud trendsLonger end-to-end fraud detection times with high manual intervention and delay in fraud detection time
No single accessible interface to process, maintain and mitigate fraudulent cases
Higher reactive vs. lower proactive fraud detection
Claimants
Employees
Policy holders
Fraudsources
Objectives implementation
1 Detect anomalies earlier to reduce financial loss
2 Improve the accuracy of detection at less cost
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3Better predict and prevent future occurrence
- Detection based on rules and predictive analytics to adapt to changing patterns
- Identification of control enhancement needs
- Minimize false positives with real-time simulations
- Ability to handle ultra-high volumes of data by leveraging SAP HANA
- Leverage the power and speed of SAP HANA
- Integration into business processes
- Alert notification and management
Current landscape
Fraud Management
ERP/BP/CM/Other sources
Business Warehouse
Payment factory
Fraud detection strategy
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
01 Set strategyDefine needed objects
02 Define methodsLike age, region, amount of invoice, period, etc.
03 Define weight factorsPer method
04 Simulate!
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.
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.
Investigate
The alerts are investigated by the Security and risk officers and status is updated
Journey ahead
System configurationInitial setup of application and connect first sources
Mass detectionConversion of phase-1 mass detection rules to online detectable rules
Online detectionMarketing the software within the organization to increase adoption
Scale upScaling up the application with more no. of rules and use more sources
Apply data scienceApply data science methodologies to detect complex fraud patterns
2017 2018-2019