Data Analytics for Insurance Fraud Prevention

The insurance industry in general has been on an expansion mode with various players cropping up and transformations in regulatory framework and evolving technology pursuits. With the rise of big data and analytics, there has been immense cross-churning of information in both the life and non-life insurance sector. This has led to immense potential of positive opportunities in the insurance industry. However, like two sides to a coin, there are also possibilities of misuse of data and chances of fraudulent practices, both by the customer and the insurer.

Seller-side insurance fraud can be ascribed to several reasons. Promotional schemes such as ‘flavor of the month’ can lead to improper customer- product fitment, lack of proper governance at the time of sale and customer-onboarding process can result in misrepresentation of information and unscrupulous practices such as selling policies to non-existent customers or churning the policies of the existing base with the intention to maximize earnings are all fraudulent practices.

The insurance fraud from the buyer can take place due to policy-holders’ lack of awareness or limited understanding of policies, or possibility of a more likely deliberate attempt to claim higher amount of money. Fraudulent claims are a grave financial load on insurers and result in higher overall insurance overhead costs.

Fraud in the protection space has led to substantial underwriting losses throughout the insurance industry. Yet insurance fraud is going to take place and remains a grim harsh reality and an unfortunate unavoidable factor of the industry.

Claims and medical billing fraud has been a continuous threat to the viability of the health insurance business. Even though health insurers constantly clamp down on devious healthcare providers, fraudsters have the potential to continuously exploit any new loopholes with forged documents contending to be from leading hospitals. The life insurance business, on the other hand, has been facing fraud that is largely data driven and can be pulled down with effective use of data analytics.

The fast-emerging alternate sources of data are disrupting the financial space. These mostly relate to social media data, data manufactured by the Internet of Things (IoT): telematics for car insurance, smart home solutions for property insurance, fitness trackers for health insurance, etc., and other readily available data due to the vast usage of smart-phones. As alternative data is gathered from non-traditional data sources, and when analytics is applied to such data, it boosts the insights further as it complements the information which is collected from traditional sources. Some benefits of using big or alternative data are:

• Better business decisions.
• One of the main advantages of this type of data, especially social media data is its global standardization.
• Gain deeper knowledge of the risk profile of each of the consumers.
• One expectation is that with the use of alternative data, the imbalance & skewed nature of information between consumers and providers can be remolded to result in a cumulative effect in favor of the providers.
• It culminates in reduction of the overall cost of fraud detection and improving the overall ROI of insurance fraud resolutions.

Traditionally, insurance companies have been using statistical models to identify fraudulent claims. With the advent of analytics & big data, has helped improve detection capabilities in probable occurrence of fraud, but it also allows an insurer to assess a fraud-risk. This in turn shall heighten the service quality to other innocent customers and cut down on unwanted ‘chasing or reactive’ costs post any such fraud.

An algorithm can predict potential fraud. Analytics has the capability to run through all critical data.

Predictive modeling can be used to thoroughly analyze instances of fraud. This, in turn, helps in identification of low incidence events. The purpose is to balance speed with thoroughness. The ultimate goal is to avoid the need to look for fraud after an insurer has made a sale.

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The ultimate determinant to analytics adaption will be how committed and prepared each insurer is to respond quickly to a rapidly changing economy and society. The question it boils down to before making heavy weight investments in information systems: comparative analysis of current fraud scenario impact on overall customer experience vs. the likely improvement in fraud detection and honest customer claims processes by making substantial infrastructure investments. In order to cut down on heavy initial investments, analytics can be inducted on a project basis and on course as benefits get evident, then these platforms can be extended to more divisions.

Fraud can occur at a number of source points: claims or surrender, premium, application, employee-related or third-party fraud. Analytics is an aid to construct a thorough global perspective of the anti-fraud efforts and can develop into a culture of enterprise-wide solution.

Analytics plays a vital role in integrating data. Effective fraud detection capabilities can be built by combining data from various sources. Analytics can also assist to integrate internal data with third-party data that could have predictive value, such as public records. Data sources with deprecating characteristics are usually public records which can be integrated into a model, which when utilized resourcefully can be a gold-mine for companies investigating medical fraud.

Analytics helps in deriving the best value from unstructured or big data. While a lot of structured data is stored in the database due to the filled-out applications, much of the critical data pertaining to a fraud is unstructured. This is crucial for fraud analysis. Reports do not all the time carry direct textual data, hence text analytics can play a vital role in reviewing unstructured data to provide some valuable insights in fraud detection.

Predictive analytics makes the use of text analytics and sentiment analysis to sift through big data for fraud detection. It can also significantly contribute to identifying distributor nexus, mis-selling and repeated misrepresentations. Relationship analytics can help the insurer determine the relationship between the insured, the owner and the beneficiary. This can also enable to figure out whether those individuals are connected to other suspicious entities or display any such suspicious behavior patterns.

In order to detect any kind of fraud, image recognition and voice scrutiny are most likely going to be used in the near future.

Social media data is the rolling ball of modern-day analytics. Insurance companies can connect social media to their CRM. As and how social media gets integrated within multiple layers of the organization, it enables greater transparency with customers. The customer centric ecosystem is not only beneficial to the customer, as they remain in control but also the business is able to leverage the collective intelligence of its customer base. It can be used as a ‘listening’ tool to extract data from the social chatter. This can act as a reference data along with the readymade info stored in the CRM is rendered to a case management system. The outcome from the system is then taken over by human investigative intervention, as social analytics is only an indicator and cannot be relied upon for final judgment of accepting or rejecting a viable claim. Analytics can be of assistance to assess customer behavior and hence determine social brand equity, and thereby boost marketing ROI and leave the customer better satisfied.

What needs to be improved and an ideal fraud detection method, is to integrate siloed databases and remove inefficiencies from processes and redundancies from data sources. The structured data on policies, claims and customers should be plotted on a common shared platform in real-time. Insurers should update the repository with claims, policy and distributors’ data on a regular basis. Such data reservoirs can provide clues to detect patterns, locate a ganged-up series and track mis-selling.

An important fraud detection method is one that utilizes data mining tools to build models that produce fraud propensity scores linked to unidentified metrics. Claims are automatically scored to look for any indication of a discrepancy or fraud. Post which, the results can be accessed for further review and analysis.

To sum up, analytics is a long-term investment game, which enable proactive detection of fraud in the early stages of the insurance lifecycle. Insurers are required to leverage both internal data and external data analytics can then better comprehend fraud risks throughout their customer life cycles and will be in a better position to mitigate those risks. The future of fraud detection, however, cannot be chalked by only the analytics route. The human element in assessing risk will continue to be a vital link to proper detection. Data can definitely expedite the pace of fraudulent activity and patterns detection. While analytics engines may get much of the coverage, the laudable fraud detection unit of tomorrow shall no doubt comprise of highly well-educated staff. All that advanced systems can deliver is a data product or generate vast bundles of reports, not a refined sorted information piece. Humans still need to sieve, analyze and crystallize the data into actionable intelligence. What the insurance world needs is a proper mix of machine and human review such that fraud detection can be brought to a higher level, at the same time analytics acts as a support system to weed out human subjectivity and assures optimum objectivity.