SAP Fraud Management implementationSession ID# 7525

Stephan de Jong : NN GroupPrabhu DC : Cognizant Technology Solutions

About NN Group

Insurance

Retirement services

Investment management

Banking

NN Group’s LoBs

Complex IT setup

Pro-SAP landscape

Heterogeneous source systems – Main-Frames, Java, SAP etc.

Agile project execution

Multiple parallel project development tracks

NN Group’s landscape

Customer base in 18 countries – Europe & Japan

Organization with history dating back to 1845

15 million customers

Listed in Euronext Amsterdam

Public listed company since 2014

Currently merging with Delta Lloyd

Around 11,500 employees( + Delta Lloyd – 6,000)

Complex landscape with mergers over the years

Digital oriented organization - Two hackathons in 2016

NN Group

Business challengesLonger end-to-end fraud detection times with high manual intervention

Variety and Volumes in Insurance fraud trends

Complexity in utilizing past fraud trends to predict fraud

Delays in fraud detection time

Higher reactive vs. lower proactive fraud detection

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

Fraud sources

Policy holders

ClaimantsEmployees

SolutionSeparation of duties between IT and business

Good response-times/real-time detection

Tightly integrated with a pro-SAP landscape

Capable of handling batch and real-time detection

Future-ready/Scalable

Capable of deterministic and non-deterministic fraud detection

Detection Investigation Reporting

o Real-time modeo Batch modeo Quick calibration & simulationo Mix of standard and custom

algorithmso Minimize false positives

o Sift through large volumeso Intuitive alert mechanismso Approval workflows to handle alerts

o Network analysis o Web-based executive dashboardo Critical relevant KPIs

SAP Fraud Management Process

Hig

hlig

hts

SAP UI5/Fiori

HANA In-Memory

Predictive algorithms

Integration with R

BigData capable

Project approach

Rule prioritization - Fraud frequency, Legal obligatory, Financial volumeData source definition for rule evaluationDefinition of technology architectureApprovals & TollgatesTechnical team trainingSystem setup – Connectivity, Security

Rule elaboration Rule configuration in SAP Fraud managementIdentify business relevant detection strategiesImplement provisioning of dataDevelopment of SAP HANA procedures for detection methodsCalibration and optimal threshold definition End-User training & How-To documentationGo-Live and business hand-over

Pre

par

atio

nEx

ecu

tio

n

Agile-scrum project execution(Jira)Sprint cycle of 3 weeks

DevOps teamsIT & Business embedded project team

Architecture

Predefined rules

Predictive analysis

Online

Mass

Alerts

Worklists Approvals

KPIs

Performance

Event flow

ReportingInvestigationDetection

Planning

+

Calibration

Automatic Manual

Fraud detection principle

Claim Payments

Lack of witness?Claim date & policy open

date too close?Claimant already has a

different claim?

HANA Proc HANA Proc HANA Proc

SAP FIORI+

NW ABAP

HANA100 100 100

W30 W65 W70

95

T = 90

Detection method result = (Individual detection result / 100 ) X weight

95 > 90

InsuranceDomain

Investigation Strategy Home Insurance Claims

Detection Strategy

Detection Method

Evaluate fraudulent claim payments made in the Insurance domain under home insurance!

1 1 0

Technical configurationSA

PH

AN

AA

BA

PC

on

figu

rati

on

FIO

RI/

UI5

Co

nfi

gura

tio

n

Selection view

Association view

Enrichment view

Selection

procedure

Execution

procedure

Additional info.

procedure

Domain

Field catalog

Detection object type

Investigation object type

Detection strategy Detection strategyDetection strategyDetection method(1..n)

1

2

3

3

Lessons learned

Access into source systems – huge lead times/high dependencies

Plan ahead for system setup related overhead and delays

Performance of SDA

Problem of high-false positive

Relevance of a data scientist in the project teams

Organization/process limitations for setting up online detection

Journey ahead

Mass detection

System setupconfiguration

Online detectionPerformance adoption

Mass detection Online detection

Mass detection Online detection

Ph

ase

-IP

has

e-II

Ph

ase-

III

• Conversion of phase-1 mass detection rules to online detectable rules• Scaling up the application with more no. of rules in Phase-II & Phase-III• Marketing the software within the organization to increase adoption• Apply data science methodologies to detect complex fraud patterns

Insert Presentation TitleInsert Speaker Name(s)

& Company

Thank you for attending my session!

For questions, contact me at: Prabhu.dc@cognizant.com

Don’t forget to fill out the Session Evaluation on the Mobile App!

SAP Fraud Management implementationSession ID# 7525

Stephan de Jong : Nationale-NederlandenPrabhu DC : Cognizant Technology Solutions

Follow Us

Thank you for your timeFollow us on at @ASUG365