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
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IntroductionGlobal Trends in Insurance Fraud
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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)
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AVIVA Canada
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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
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FINDING THE FRAUD 100 MOTOR CLAIMS
100 Motor Claims
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THERE MAY BE 10 POTENTIAL FRAUDS CASES …
10 potentialfrauds cases
100 Motor Claims
FINDING THE FRAUD
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WITH BUSINESS RULES THE INSURER MAY FIND…
10 potentialfrauds cases
100 Motor Claims
Business rules only
FINDING THE FRAUD
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LEAVING THE ANALYTICAL OPPORTUNITY…
10 potentialfrauds cases
100 Motor Claims
Business rules only
FINDING THE FRAUD
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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+
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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
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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
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So How Can Analytics Help?Global Trends in Insurance Fraud
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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
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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
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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
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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?
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Fraud DetectionThe evolution of fraud detection solutions
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
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Fraud DetectionThe evolution of fraud detection solutions
Internal and external watchlists were then added based on both exact and fuzzy matching to further improve the scorecard
Scorecard FraudInvestigation
Fraud outcomesUse of analytical models to find new rules and produce weighted scorecard
Score > threshold
Fraud watchlist
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Fraud DetectionThe evolution of fraud detection solutions
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
Scorecard FraudInvestigation
Fraud outcomesUse of analytical models to find new rules and produce weighted scorecard
Link AnalysisScore > threshold
Complex case
Fraud watchlist
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Fraud DetectionThe evolution of fraud detection solutions
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:
Scorecard FraudInvestigation
Fraud outcomesUse of analytical models to find new rules and produce weighted scorecard
Link AnalysisScore > thresholdSocial
Network Analysis
Fraud watchlist
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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
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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
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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
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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
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Agent GamingSocial Network Analysis
Example: 5 written off policies connected to the same phone number
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Internal FraudNetwork Analysis
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The Challenge
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The Challenge
How to identify a potential organised crime ring
in less than 10 minutes