Policyholders in the UK and Ireland have been hit by a series of large price increases in motor and home insurance policies. There are many factors driving these hikes and insurance fraud is one of them.
According to Insurance Confidential, fraud costs the Irish insurance industry €200 million every year and motor insurance policies are, on average, €50 more expensive because of it[1]. There is a similar impact in the UK market with an estimated addition of £50 to every insurance policy. UK insurers uncovered fraudulent claims in 2014 worth more than £1.3 billion[2].
The problem is hardly unique to the UK and Ireland. Of the 1.2 million claims AXA handled in Spain in 2015, 1.3% of them were fraudulent[3]. That is close to the Spanish average of 1.37%, up from 1.12% in 2014. Some lines of business have seen a 15% annual increase in fraud with motor insurance being responsible for 80% of all the fraudulent claims discovered.
Fraud affects all business lines and different areas where customers interact with insurers; from non-disclosure at the time of underwriting to making fraudulent claims. It can be organised, with several individuals planning and working together, or it can be opportunistic. Fortunately there are tools to help insurers detect these types of fraud.
A solution to non disclosure
Non-disclosure is not always done in bad faith and customers may simply not be aware of important issues that can be relevant. Nevertheless, whether it is accidental or intentional, statistics have a way to identify potential cases where non-disclosure may be likely. Probabilistic Graphical Models and Bayesian Networks are useful tools which identify cases with statistically unusual combinations, recommending a closer inspection.
It is impossible to investigate all applications but these methods provide a list of cases the system suspects of not having disclosed everything. Data analytics does a good job at spotting potential inconsistencies in application forms. It allows insurance companies to operate with more confidence, knowing that the risk they take is appropriately assessed and priced. It also provides a better customer experience by reducing the number of claims rejected due to non-disclosure.
Those interested in how these statistical techniques work may want to check Applied AI’s blog[4] for a three part series on Probabilistic Graphical Models. The last two entries[5][6] focus on insurance.
Preventing claims fraud
Regarding insurance claims, the vast majority are legitimate and insurers expect them to happen. But a small proportion of fraudulent claims are generated by organised teams. This has a disproportionate impact in the claims experience of insurers, causing huge unnecessary losses[7]. Because they involve several individuals, the best tool for the job is a network analysis (graph databases).
Network analysis is a powerful weapon when fighting organised fraud as it finds a way to show what fraudsters have in common[8]: for example they live in the same house or close together, work together, they are friends in social networks[9], etc.
There are cases however, where policyholders see the possibility of making a false or an exaggerated claim and decide to act on it. This is not organised crime but opportunistic and network analysis may not be useful in spotting this. More common data analytics techniques can be used here and the challenge posed by the rarity of these fraudulent claims can be overcome with some preparation. Those interested in the mechanics may check Dr Peter Brennan’s work on the subject[10].
But what is really happening?
Researchers are investigating increasingly sophisticated techniques to detect fraud, some including image and voice recognition. In addition, the Association of British Insurers announced plans to invest more than £11 million to fund an expansion of the Fraud Enforcement Department in the City of London Police[11].
Insurers are beginning to up their game in relation to fraud analytics. AIG has developed an in-house system that identifies twice as many fraudulent claims than the tools provided by the leading vendors in the fraud analytics market[12].
Irish Health insurer VHI set up a fraud investigation unit that has recovered over €47 million since 2009. Liam Downey, from VHI, said that “widespread use of data analytics had been very effective”[13]. Also in healthcare, the US company Optum provides data analytics based solutions to prevent fraud, waste, abuse and enhance payment integrity[14], helping their clients make significant savings.
It is clear is that the impact insurance fraud has in companies and customers is too large to ignore and data analytics is, so far, the best solution we have. Insurers are adopting data analytics as a very effective tool in the fight against fraud.
Notes
Image source: Steve Buissinne / Pixabay
[1] http://www.insuranceconfidential.ie/
[2] https://www.abi.org.uk/Insurance-and-savings/Topics-and-issues/Fraud
[3] http://www.communityofinsurance.es/productos-de-seguro/el-fraude-en-el-seguro-en-2015-es-incontenible-o-podemos-mejorar
[5] http://blog.applied.ai/probabilistic-graphical-models-for-fraud-detection-part-2/
[6] http://blog.applied.ai/probabilistic-graphical-models-for-fraud-detection-part-3/
[7] https://hub.premium-credit.co.uk/news/are-brokers-doing-their-bit-in-the-fight-against-fraud
[8] http://thenewstack.io/how-graph-databases-uncover-patterns-to-break-up-organized-crime/
[9] http://www.independent.ie/irish-news/courts/husband-jailed-but-wife-walks-crash-couple-sentenced-in-insurance-fraud-uncovered-by-facebook-profiles-34236725.html
[10] http://www.dataminingmasters.com/uploads/studentProjects/thesis27v12P.pdf
[11] http://www.insurancenexus.com/fraud/role-data-and-analytics-insurance-fraud-detection
[12] https://hbr.org/2014/10/how-aig-moved-toward-evidence-based-decision-making/
[13] http://www.irishtimes.com/business/vhi-complained-to-garda%C3%AD-over-alleged-fraud-by-provider-1.2110218
[14] https://www.optum.com/about/news/optum-and-sas-align-to-help-prevent-health-care-fraud-waste-and-abuse.html
Data Driven 