On a daily basis, p&c insurance providers minethrough vast repositories of data to validate and process thousandsof claims.

Yet, billions of dollars are lost annually becauseof fraudulent insurance claims. In order to provide qualityservices to their customers, providers need to recover this lostmoney. Preventing fraud requires mining and analyzing massivevolumes of data to gain better insights and, in turn, improvedecision-making ability.

According to a recent survey by FICO¹and PropertyCasualty Insurers Association of America (PCI), 45 percent ofinsurers estimated that insurance fraud costs represent 5 to 10percent of their claims volume, while 32 percent said the ratio isas high as 20 percent. More than half (54 percent) of insurersexpect to see an increase in the cost of fraud.

Now let's evaluate some of the challenges that persist and thechanging insurance landscape that is driving innovative solutionsand approaches.

Challenges include:

• Information overload and the rise in the number of securitythreats and frauds.
• Technological limitations that make it challenging to processand analyze data in a timely manner.
• Information silos, disparate systems and departmental processesthat create information leakages.
• Evolving demand to keep up with changing compliance andregulations requirements.
• Lack of skilled resources to investigate and address fraudulentactivities.

A Changing Landscape Leads To NewRealizations

While the insurance industry has matured over the past fewyears, traditional methods of fraud detection are unable to keeppace with the rapid advances in technology. Criminals these aredays are sophisticated, constantly change their tactics and arevery skilled in identifying the loop holes. As such, carriers needto implement a robust strategy built on advanced analytics foundation that iscapable of handling security issues that arise because of information siloes as well detect fraud promptly.

Fraud can occur at any stage of the claims process, leading to asecurity breach, which is one of the biggest concerns for both forconsumers as well as the insurance companies. The results of theJavelin Strategy & Research study, “Identity Fraud Report:Consumers Taking Control to Reduce their Risk of Fraud”revealed the number of people affected by data breaches has grown67 percent since 2010.

In 2011, more than 11.6 million adults in the United States werevictims of identity fraud and the number is increasing year onyear. Organizations must start analyzing national and publicsecurity trends holistically across the organization to preventlarge-scale threats. Using robust advancedanalytics, organizations can accomplish the followinggoals:

• Advance the precision of fraud detection.
• Reduce false positive ratios.
• Detect fraudulent claims before payment at a faster rate.
• Reduce financial liability.
• Reduce operational cost by using a common technology platformfor fraud and security issues.

Organizations can also deploy these measures to counterfraud:

• Connect the enterprise tightly. Deploy acommon infrastructure across the organization and ensure a smoothflow of information across various systems to make it easy toanalyze data from across channels such as users or accounts andprovide a comprehensive view of an individual's relationship withthe organization.
• Monitor continuously. Leveraging advancedanalytics to monitor and authorize transactions in real-timeenables proactive identification of a fraudulent transaction withno negative effect on the customer experience, thereby protectingbrand reputation.
• Discover relationships in your data. Establishlinks between your data entities such as customers, products,accounts or services to easily identify organized or collaborativefraud activities that would otherwise go unnoticed.
• Mash up structured and unstructured data.Useful information can often reside in unstructured dataformats like claims log, survey reports, emails, social media andgeospatial information. Once mashed up with the structuredinformation from internal systems, this unstructured data canprovide valuable insights into detecting claim patterns.

No single analytics approach is perfect and each organizationneeds to experiment and evolve to come up with a fraud detectionsystem that works the best for them. That said, it is important toincorporate a hybrid analytics approach which combines variousmethods including monitoring past patterns and business rules,detecting anomalies, predictive data mining, analyzing social,champion-challenger adaptive segmentation with advanced neuralnetworks, and relationship mapping for greater accuracy andimproved detection.

Secondly, as mentioned earlier, combining information fromstructured data with the unstructured data is important to linkunrelated events, unearth hidden facets and detect security threatsand fraud in the early phases.

Footnote

¹FICO PCI Insurance Fraud Survey, FICO, October4, 2012.