Global insurance premiums passed the $5 trillion mark in 2018, accounting for more than 6% of global GDP. Although global insurance premiums are forecast to grow by 3%, this trend is led by the emerging markets and is considerably lower in advanced economies. The market is segregated into life insurance and non-life insurance–both characterised by different features and dynamics.

Despite the overall growth predictions, the insurance industry has faced a series of challenges, including increased customer expectations with regards to premiums and the range of coverage. Insurance fraud has been a serious issue in the industry for several decades, undermining the competitiveness of policy pricing. It is estimated that fraudulent claims account for up to 10% of all claims expenditure, resulting in considerable losses for both insurers and customers, as the additional costs translate into higher premiums for everyone. Because of the sheer volume of claims received, many insurers struggle with unreliable systems that fail to flag potentially fraudulent claims–and subsequently lead to undue payouts.

Deploying Machine Learning algorithms, our solution improves detection of suspicious claims. It will help you identify all potential attempted fraud and require limited resources for their investigation. Our solution uses data from multiple sources, such as claim descriptions and the insurance policy, incident details and textual data describing it, as well as historical records related to the parties involved. By reducing the number of predictors, it makes the model simpler and more robust. As a result, our solution can improve your detection rates by as much as 60% and deliver ROI within weeks of implementation.