The Future is Now for Life Insurers Who Leverage Advanced Analytics to Mitigate Fraud Loss
Blog: Capgemini CTO Blog
Globally, fraud-related organizational losses are, minimally, at 3%, and more likely to be around 6%, according to a report by UK audit, tax, and advisory firm, Crowe Clark Whitehill. The Coalition Against Insurance Fraud estimates that U.S. insurers lose $80 billion annually to fraud, across all lines of business. The far-reaching cost of fraud includes more than simply incorrect claims payments, or the expenses incurred by fraud-prevention and detection units. Fraud stifles firm growth and costs consumers money and convenience.
However, today, there are ways for the industry to mitigate losses with available data and analytical tools that increase transparency and improve customer trust.
More and more life insurers are leveraging advanced analytical capabilities―along with AI and machine learning (ML) techniques―for better fraud detection and early identification of fraudulent cases. With the increased use of mobile devices and social media, more customer behavioral data is available to be analyzed for fraudulent behavior or patterns. AI can help insurers derive keener customer insights and pattern identification from unstructured data, while ML systems can upgrade pattern and anomaly detection capabilities.
Insurers now use analytics, not only for fraud management but also for product cross-selling, onboarding, and bringing down fraudulent claims. Moreover, predictive models are being leveraged to flag potential frauds in real time during policy underwriting or claims processing. For example, international financial services group Manulife uses advanced analytics anomaly detection and ML for fraud detection in claims. Similarly, India-based Reliance Nippon Life Insurance uses propensity-based analytics for fraud management.
As reported in the Top-10 Technology Trends in Life Insurance: 2018 report, increased adoption of digital channels heightens chances for misrepresentation. That is why know-your-customer (KYC) efforts are critical to keep fraud in check. Insurers can also use fraud detection models to analyze their distribution network’s performance and to check for misselling, misrepresentation, and other fraud such as commission rebating. 
Advanced analytics help firms to quickly identify agent fraud through pattern identification that can significantly improve an insurers distribution network. The use of advanced fraud analytics systems enable life insurers to detect fraud and identify new fraud patterns, and that means lower fraud losses and better underwriting results thanks to reduced claims leakage. Advanced fraud detection systems support claims processing, too, by reducing processing time and false positives, which makes customers happier.
Life insurers looking to improve firm capabilities in fraud detection, pattern identification, and claims’ leakage, owe it to themselves to learn more about advanced analytics technologies that can be used now by leveraging available data.
 “The Financial Cost of Fraud 2017”, Jim Gee, Crowe Clark Whitehill, February 15, 2017, accessed October 2017 at https://www.croweclarkwhitehill.co.uk/financial-cost-fraud-2017/
 Coalition Against Insurance Fraud, http://www.insurancefraud.org/statistics.htm#1, Accessed January 2018
 Manulife website, accessed October 2017 at http://www.manulife.com/AdvancedAnalytics?ocmsLang=en_US
 “Insurers use analytics to detect fraud, cross-sell products”, M Saraswathy, July 16, 2016, Business Standard, accessed October 2017 at http://www.business-standard.com/article/finance/insurers-use-analytics-to-detect-fraud-cross-sell-products-116071600761_1.html
 Misselling is the deliberate, reckless, or negligent sale of products or services in circumstances where the contract is unsuitable for the customer’s needs. E.g., selling life insurance to someone who has no dependents.
 “Life Insurance – Combating Fraud and Minimizing Losses”, Cecil Ramotar, Gen Re, November 2013, accessed October 2017 at http://www.genre.com/knowledge/publications/bulletinlh1310-en.html