how aviva health prevents fraud using sas, vasilij nevlev ... · aviva health (uk): •1.2 million...
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How Aviva Health prevents fraud using SAS
Presenter: Vasilij Nevlev
Aviva Plc and Aviva Health
Aviva plc:
•33 million of customers
•29,600 employees
•16 countries
•30,7 billion paid out
Aviva Health (UK):
•1.2 million of customers
•900 employees
•United Kingdom
•600 million paid out
Aims and objectives of the project:
Aviva becomes a strong player in the supply market, embedding a robust and
proactive approach to managing supplier behaviours.
Aviva able to demonstrate excellence in managing policyholder fraud including
the widening international market using clinical and forensic intelligence.
Financial Loss activities inform the operational and strategic objectives of:
Financial Crime Prevention, Claims, Distribution and Margin Management
and wider Group activities.
Financial Loss ContinuumWhy proactivity is Vital to Success……because it reduces margin overspend and improves customer
experienceHigh Value
Lack of Controls Gaming Abuse Fraud
Low Value
High Low
Ease of Investigation/Implementation
Code Combination
Post Op Pain Relief
Oncology
Upcoding
4+ procedures
Wilful deception
Mar
gin
£Controls implentedto prevent unbundling of activities considered as part of the
Controls implentedto prevent addional charges for pain relief considered as part of the procedure
Refresh to fee guidelines reducing the number of procedureswe pay in one theatre session in line with the
Risk of paying for a more complex procedure than was carried out -unknown value
Consultants billingfor repeat or similar consultations or treatment carried out by nurse - value
Customer: High volume of referredcases although minimal financial loss
Supplier:Low volume of cases currently reviewed, medium value of recovery (£30k per
Relationship with suppliers
… is well regulated and relatively automated
• Every supplier must be registered with the insurer
• Every claim is usually preapproved
• Suppliers are usually check that the insured has a valid open claim
Anti-fraud community
• Data Protection Act of 1998 Section 29.3 supports the data exchange
between parties in case of reasonable suspicions of fraud
• Senior managers regularly meet at steering committees
• Investigators attend conferences and training courses, certification paths
are available
Hypotheses review process
Decision making process
Loss prevention framework
Feed
back
Internal Sources
Data Warehouse
External Sources
Detection
Rules
Case Management
Software
Predictive
Models
Scoring Rules
Case Outcomes
Change Priorities
Add New Data
Source
Amend Rules
Use Different Data
Links Analysis
Detailed view of the loss prevention framework
Feed
back
Internal Sources
Data Warehouse
External Sources
Detection
Rules
Case Management
Software
Predictive
Models
Scoring/Optimisation Rules
Case Outcomes
Change
Priorities
Add New Data
Source
Amend Rules
Use Different
Data
Links Analysis
Business outcomes:
• Increased margin
• Improved risk mgmt
• Improved customer
experience
• Increased COR
• Reduction in fraud
and gaming
• Case study for
predictive
modelling,
optimisation data
analytics
