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Using Advanced Analytics to Combat P&C Claims Fraud


Advanced analytics — text, social media, link and geospatial analysis — can help P&C insurers combat sophisticated claims frauds, reduce related losses, condense claims cycle and improve customer satisfaction.

Insurance fraud is the second biggest white-collar crime in the U.S. after tax evasion, according to the National Insurance Crime Bureau (NICB).1 Not only on average, insurers lose $30 billion to fraudulent claims2, estimates show that fraud increases premiums by $200 to $300 per family, annually.

The growing complexity of fraud has exposed the limitations of traditional fraud-detection systems, such as internal audits, whistleblower hotlines and software that flags anomalies based on pre-defined set of rules. Not surprisingly, fewer than 20% of fraudulent claims are detected.3

Learn more by expanding our infographic:

Analytics for Improved Fraud Detection

Predictive analytics

The use of regression models and advanced techniques such as neural networks — helps to quickly and accurately determine the legitimacy and complexity of the claim. Customer data generated by insurers, such as policy details, previous claims and information gathered from adjusters, can be used in combination with data from industry sources such as NICB to run predictive analytics to identify fraudulent claims early in the process. For example, NICB's ForeWARN database allows member companies to search and identify information to develop fraudulent patterns and trends.4

By combining social network and social media analytics, text mining, link analysis and geospatial analysis, insurers can identify fraud that is hard to detect using traditional methods.

Social Network and Social Media Analytics

Social network analytics help to identify relationships among people, groups, organizations and related systems. This exposes a customer's affiliation to any fraudulent group and helps predict the chances of a particular customer committing fraud. By tracking social media accounts and applying social network analytics, insurers can gain information about claimants, medical providers, body shops, etc., as well as a claimant's connection with organized crime networks.

Text Mining

Text analytics helps companies gain critical insights from large volumes of unstructured data, such as adjuster notes, first notice of loss, e-mail and accident descriptions. Based on the key words used to describe an incident, text analytics helps insurers detect attempted fraud by flagging questionable incidents, exaggerated injuries and treatment costs, reckless driving, etc. and recommends actions

Link Analysis and Geospatial Analysis

While link analysis allows investigators to gauge whether the parties involved in a large group of injury claims are interrelated, geospatial analysis can provide location-based information related to a claim, as well as the physical proximity of the claimants and others involved in a claim. In the case of a staged accident, geospatial analysis provides information about the location of the accident, the distance between the various claimants' residences and their proximity to resources such as a lawyer, a body shop and a medical provider. This provides investigators with evidence to pursue a hunch and to identify potential fraud rings.

Geospatial analysis can also be used to identify the exact area affected by a natural disaster or an explosion, which helps determine the amount of risk to insured properties and weed out claims that are filed from areas that are not located in the affected zone.

Embracing Analytics as a Service

We believe an ideal fraud detection approach must combine the best of analytics and rules-based approaches. While in-house solutions offer greater control over development, "operationalizing" a fraud detection model and the infrastructure required to implement and run an analytical solution can be expensive.5 Vendor solutions, on the other hand, offer lower total cost of ownership.

However, deploying analytics is no easy task. The traditional IT infrastructure deployed by most insurers is insufficient to analyze large volumes of data and requires organizations to invest in people, processes, IT tools and infrastructure.

Choosing the Right Partner

A subset of business process as a service (BPaaS), analytics as a service (AaaS) combines traditional knowledge process outsourcing (KPO) and business process outsourcing (BPO) capabilities with more efficient, cloud-enabled ways of delivering analytical insights. This can save precious Cap-Ex by transferring the cost of acquiring expensive hardware, software and key talent through consumption-based pricing models.

Organizations should seek a partner that can seamlessly marry analytics with technology rather than a pure-play analytics player. The partner must have expertise in extracting meaningful insights from insurance-related social networks and social media and perform complex analyses on the data. The technology component includes the partner's ability to integrate advanced analytics with insurers' claims systems, create new claims efficiencies and improve overall claims effectiveness.

As analytics processes become standardized and can uniformly be applied via cloud-enabled models (harnessing the growing clout of utility computing architectures), we believe that insurers stand to benefit greatly by associating themselves with partners that have invested in such capabilities.

Looking Forward

To experience the potential of analytics, we believe that insurers should:

  • Develop an enterprise-wide data architecture.

  • Identify key areas for deploying analytics.

  • Design a comprehensive strategy for adoption and implementation of analytics, including information technology.

  • Develop a fact-based decision-making culture focused on achieving specific goals.

  • Formulate customized strategies to capitalize on unique data.

  • Continuously innovate and renew analytics implementation.

  • Enter into relationships with partners capable of providing AaaS to advance competitive advantage.

For detailed analysis, please read our whitepaper 'Using Advanced Analytics to Combat P&C Claims Fraud' (PDF). Also visit Cognizant's Enterprise Analytics practice for more insights.


1"Insurance Fraud: Understanding The Basics," NICB, April 21, 2011

2"Insurance Fraud," Insurance Information Institute, June 2012

3"Predictive Analytics: A Powerful Weapon In Fight Against Fraud," PropertyCasualty360, April 4, 2011

4"Join NICB," NICB

5"Operationalizing a Fraud Detection Solution: Buy or Build?" Insurance & Technology, May 16, 2013

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