Global Trends in Insurance Fraud : Next Generation Network ... · IFBA estimates that insurance...

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SAS® FINANCIAL CRIMES EXECUTIVE FORUM Toronto, 2018

Global Trends in Insurance Fraud : Next Generation Network Analytics

David Hartley, Global Director

SAS


IntroductionGlobal Trends in Insurance Fraud


Global Insurance P&C Claims FraudCAIF estimate $80bn losses annually. III estimates $32bn for P&C alone

GDV estimates that insurance fraud costs circa €4bn per annum

ABI estimates that undetected fraud cost insurers £3bn a year

IFBA estimates that insurance fraud costs more than $2 billion annually

VvV estimate that fraud has increased 25% in last 5 years adding 150€ to a policy & that 10% of claims may be fraudulent

ICB estimate that insurance fraud inflates the cost of insurance by 15%

Schweizerischer Versicherungsverband estimate that 10% of claims paid are fraudulent

ALFA estimate that fraud 15% of claims paid, or 4-8% of premiums collected equating to €2.5bn per annum

Svensk Försäkring estimate that 5-10% of claims include fraud and that 75% of people convicted for insurance fraud had previous criminal convictions and insurance fraud is more socially acceptable than other types of criminality

FFI 2014 survey - 19% said they knew a person “who has deceived his/her insurance company”.

DIA estimates that it costs the honest insurer holder up to DKK 500 per year (2013)

ARIA estimates that 30% of compulsory motor, 20% of casco motor & 15% property claims are fraudulent

GIA of Singapore estimates are 20% of claims are fraudulent or overinflated at a cost of 140 M annually.

Korean Financial Supervisory Service (FSS) estimates fraud losses $4.5 billion per year (13.5% in Life and 86.5% in Non-Life)


AVIVA Canada


Healthcare FraudGlobal study says costs of fraud and error is higher than expected

Source: The Financial Cost of Healthcare Fraud 2015 – University of Portsmouth Centre for Counter Fraud Studies & PKF Littlejohn LLP

The research published in this report covers 14 different types of healthcare expenditure totalling $4.44 trillion (USD), in 33 organisations from 7 countries: UK, USA, France, Belgium, Netherlands, Australia & New Zealand

http://www.port.ac.uk/media/contacts-and-departments/icjs/ccfs/The-Financial-Cost-of-Healthcare-Fraud-Report-2015.pdf


FINDING THE FRAUD 100 MOTOR CLAIMS

100 Motor Claims


THERE MAY BE 10 POTENTIAL FRAUDS CASES …

10 potentialfrauds cases

100 Motor Claims

FINDING THE FRAUD


WITH BUSINESS RULES THE INSURER MAY FIND…


100 Motor Claims

Business rules only

FINDING THE FRAUD


LEAVING THE ANALYTICAL OPPORTUNITY…


100 Motor Claims

Business rules only

FINDING THE FRAUD


Insurance Fraud Customers

Plus others under non-disclosure agreements

Admiral

Aksigorta

Allianz

Alm Brand

Aviva

Catalana Occ.

Ethniki

Generali

ICB

Ingosstrakh

Lusitania

Poste Vita

SBM

VIG

Ydrogios

ACC

China Life

Dongbu

Hanwha

Hyundai

Nonghyup

Kyobo

IAG

Ping An

Zhongan

Amica

Caixa

eSurance

Chubb

Grange

HDI

Qualitas

90+


Aksigorta Turkey

• Large insurance company in Turkey

• Part owned by Ageas

• 66% increase in fraud detection since using the SAS solution

• Real time scores – 8 seconds

• And making full use of social network analysis

2.40%3.00%

4.80%

6.20%

Proven & Captured Fraud RatesBased on # of claims

2014 2015 2016 2017

3.1

4.7 4.8

5.7

Proven & Captured Fraud Rates€m

2014 2015 2016 2017


CZ-Groep

• Third largest healthcare insurer

• Moved from a ‘pay and chase’ model to prepayment control

• Increased savings in first 12 months from €65m to €97m; +50%

• Now successfully rolled out across:• Physiotherapy

• Mental health

• Pharmaceuticals

• Medical devises

• Next Stages• General practitioner Care

• Hospital Care

• Dental

“Prevention is always better than cure. Through intelligent analysis, we can stay ahead of faulty statements and fraud.”

Marnix Suijkerbuijk

Director of Health Care and Statement Service

Full Story here

Netherlands

https://www.sas.com/en_us/customers/cz-nl.html


So How Can Analytics Help?Global Trends in Insurance Fraud


The Devil is in the Data1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 0

1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 00 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 10 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 11 1 1 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 11 1 1 0 1 1 0 1 0 0 1 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 00 1 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 1 1 0 0 1 1 11 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 10 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 1 01 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 1 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 11 1 1 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 10 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 11 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 0

0 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1

0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 10 1 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1


Data

• Structured & unstructured data sources

• Data cleansing• Data integration• Variable extraction &

sentiment analysis with text mining

• Entity resolution

SAS Analytical Methodology - End to End

Reporting

• Dedicated dashboards, easy to use web-based interface

• Workflow analysis• Full business intelligence

reporting capability• System and case

management integration

Investigation

• Automated alert generation• Advanced ranking

technology• Custom alert queues• Alert qualification and triage• Powerful user interface with

single and holistic views

Detection

• Business rules• Anomaly detection• Advanced predictive models• Watch lists• Profiling• Social network analysis and

network-level analytics

Discovery

• Dynamic data exploration• Advanced query of integrated data• Detection performance analysis• New modus operandi discovery

• Accelerated design and constant improvement of the detection logic

• Alert suppression & routing rules• Simulation and testing of new risk

assessment methodologies


SAS Hybrid Scoring Approach for Fraud

Alert Generation

Process

Database Searches

Text Mining

Machine Learning

Anomaly Detection

Automated Business Rules

Levels Of Detection

Event

Entity

Network


Fraud DetectionThe evolution of fraud detection solutions

Initially fraud solutions were all based on simple binary rules

Any item hitting on of the key risk factors would be sent for investigation and some of these would end up as fraud

Rules FraudInvestigation Rule triggered?



Next advanced analytics was brought into the picture:

• Anomaly detection and text mining was used to discover new rules

• Predictive models were used to generate an overall scorecard using the historic outcomes

Scorecard FraudInvestigation

Fraud outcomesUse of analytical models to find new rules and produce weighted scorecard

Score > threshold



Internal and external watchlists were then added based on both exact and fuzzy matching to further improve the scorecard



Score > threshold

Fraud watchlist



For more complex cases some customers would create link diagrams. Sometimes this was done by hand on paper, other times tools like I2 were used to help to create these on a case by case basis



Link AnalysisScore > threshold

Complex case

Fraud watchlist



In the final step, rather than creating the link charts manually for only high risk cases, new technology enabled the generation of these social networks automatically up front, enabling network level variables to be used in the score models and making them more accurate:



Link AnalysisScore > thresholdSocial

Network Analysis

Fraud watchlist


EntitiesP&C

• Person - Party ID – policyholder, claimant, supplier owner, insurance employee etc.

• Address - Insured address, risk address etc.

• Telephone numbers - Landline, Mobile (sequential)

• Email address

• IP address

• Suppliers – garages/repair shop, medical, legal, hire car, tow truck etc.

• Insurance agent

• Bank account – in/out


EntitiesLife

• Person - Party ID – policyholder, beneficiary, insurance employee etc.

• Address - Insured address etc.

• Telephone numbers - Landline, Mobile (sequential)

• Email address

• IP address

• Suppliers – medical service providers (disability, dread diseases) etc.

• Insurance agent

• Bank account – in/out


Doctors

Hospitals

Pharmacies

Dentists

Chiropractors

MedicalEquipment Suppliers

Optometrists

Home Health Care

Laboratories

Retail HealthOutlet

Infusion Centers

LandTransport

PodiatristsImaging Centers

Dialysis Centers

Substance Abuse

Facilities

Substance Abuse

Facilities

Personal Care Assistants

Birthing Centers

Physical Therapists

Therapeutic Massage

Therapists

Mobile Health

Urgent Care Facilities

Ambulatory Surgical Centers

HospiceCenters

Psychiatric Facilities

InpatientRehabilitation

Centers

ESRD Treatment Facilities

Rural Health Clinics

OutpatientRehabilitation

Centers

Intermediate Care Center

Air Transport

Trauma Centers

Pathology Laboratories

Occupational Therapist

Social Worker

EntitiesHealth - Providers


Social NetworksVisualising Unexplained Relationships

Compounds: An understanding of the real world is used to combine elements into combinations that represent real-world items within the data. E.g:

Fore+Sur+DoB

Fore+Sur+Hse#+Street+Zip

Social Security #

Entities: The real-world items that we are trying to model within our fraud solution. E.g.

Individual

Address

Landline

Bank Account

Email Address

IP Address

The high level process for producing social networks is as follows:

Elements Compounds Entities Networks

Elements: fields within the incoming data that partially or completely identify an entity. E.g.

Forename

Surname

Date of Birth

Social Security #

Telephone number

Networks: Groups of strongly connected documents and entities

Documents

Documents: Often best thought of as “what you might find on a piece of paper” these contain all of the data relate to a business understood concept. E.g.

Insurance Claim

Insurance policy application


Agent GamingSocial Network Analysis

Example: 5 written off policies connected to the same phone number


Internal FraudNetwork Analysis


The Challenge


The Challenge

How to identify a potential organised crime ring

in less than 10 minutes

sas.com


http://www.sas.com/

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