AI Powered Fraud Detection Systems: Enhancing Risk Assessment in the Insurance Sector

Abstract

The Insurance Sector, like many organizations, is in a state of flux. Artificial Intelligence, Intelligent Automation, Machine Learning, Business Analytics — all these are buzzwords yet to become part of the vernacular business model. The Insurance Sector, which traditionally flourishes on the risk assessment model, aspires to adapt and embrace these changes to make more accurate predictions. Some of the sources of noise and sources of change are external, like syndromes of social and economic relevance; some of it emanates internally, from the Company itself, as a result of mis-selling or fraud. Fraudulent activities not only impact the bottom line but also the reputation and brand, making the need for fraud analytics with noise and change models ever so important.

The challenge, therefore, is to build a model which addresses the above sources and effect of fraud, by effectively analyzing the noise and change triggers behind the phenomenon, by tracking the social, psychological and economic syndrome. Being an adaptive model, it would adapt to the changes in the company policies, change in sale and the claims pattern. Leveraging the huge data stores in the company, and using Artificial Intelligence-based framework for fraud analytics emerges as the optimal solution. This chapter highlights the role of and impact of data, Artificial Intelligence and Machine Learning algorithms on source detection and adds a new dimension to the traditional risk assessment model.