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