Using HPC-Powered Analytics to Predict Fraudulent Activity

By Bill Mannel, VP & GM, HPC Segment Solutions and Apollo Servers, Data Center Infrastructure Group, Hewlett Packard Enterprise

October 5, 2017

According to the Insurance Information Institute, fraud accounts for about 10 percent of the property/casualty insurance industry’s incurred losses and loss adjustment expenses each year. This costs the U.S. insurance agencies up to $34 billion each year. That means, in the time it takes to read this sentence, approximately $8,000 has been paid out in fraudulent claims.

Difficult economic conditions have caused a surge in suspicious activity over the past few years, driving up expenses for insurers, threatening profitability, and forcing companies to compensate by charging exorbitant policy premiums. More than 7,000 companies make up the insurance industry, and they collect over $1 trillion in premiums each year. The Federal Bureau of Investigation estimates that insurance fraud costs the average U.S. family an extra $400-$700 per year in increased premiums.

As data volumes continue to rise, financial institutions are striving to identify and even predict fraudulent activity. Breakthrough capabilities like high performance computing (HPC) are helping IT departments combat increasingly complex and expensive issues. In order to operate safely and confidently, these organizations need to invest in the latest HPC solutions in order to combat rising security risks.

Fraudsters are continuously finding new methods of deceiving insurance companies, challenging IT departments to adapt their anti-fraud efforts and data management capabilities. Advanced analytics and modeling tools powered by HPC allow insurers to rapidly analyze incoming data, delivering business insights which help them identify falsified claims, faster and with more accuracy.

Predictive modeling employs a rules-based engine to quickly process vast quantities of data and uncover suspicious patterns. By aggregating structured and unstructured data from historical databases, claims management systems, and even third-party sources like social media, analytics helps organizations compare the characteristics of new claims against those of past losses in order to flag potentially fraudulent activity.

Predictive analytics can bolster anti-fraud efforts through a number of applications:

  • Popular fraud methods can be identified in newly-opened claims and new fraudulent techniques pinpointed in real time.
  • Text mining solutions can be leveraged to collect streaming data, which assists claim investigators (e.g. data collected from a claimant’s social media page may be analyzed to determine the viability of their claim details, such as the extent of their injuries).
  • Scoring, which predicts the likelihood of fraud, can be delivered early in the claims intake process, so specialists can ask more probing questions or immediately involve investigators to confirm fraudulent activity.
  • Claims processing can be optimized, also helping to improve the accuracy and efficiency of underwriting, subrogation, and recovery.

For today’s insurers, survival is dependent on transforming outdated IT infrastructures to include the right mix of computational speed and storage to power Big Data analytics. By adopting a scalable, high performance Big Data platform, insurers can harness real-time insights to better forecast risk and considerably reduce losses from fraudulent claims.

For the insurance industry, profit is inextricably linked to risk reduction and prevention, and data-driven insights are key to detecting new methods of fraud. Organizations who leverage the speed and performance of HPC technologies can not only scale to accommodate immense data growth but also quickly identify fraudulent claims, enhance the efficiency of investigations, and ensure business longevity.

To learn more about how HPC-powered analytics can improve and protect your business, I encourage you to follow me on Twitter at @Bill_Mannel. You can also visit @HPE_HPC for the latest updates in HPC for fraud detection.

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