Ways Insurance Industry Can Manage Risks With The Help Of Ai

Insurance CIO Outlook | Sunday, May 22, 2022

Insurers are majorly using AI to identify underwriting risks and optimize risk selection.

Fremont, CA: Artificial intelligence (AI) has emerged as a game-changing technology in the insurance industry during the last decade. Apart from driving data transformation, it has also played an important role in developing more effective claims application and administration systems and complementing hyper-personal insurance products and services. But probably its most major influence is in risk management, notably in claims and underwriting, where it is used in conjunction with other technologies like Machine Learning (ML) to identify and mitigate risks, detect fraud, and strike a balance between risks and opportunities.

Improving risk selection

Insurers are using AI to identify underwriting risks and optimize risk selection. Smart algorithms sift through industry databases for relevant client data, efficiently categorizing them into pre-determined price groups. Credit risks, governance and compliance risks, operational risks, market risks, liquidity risks, trading risks, cyber risks, and criminal risks, including fraud or money laundering, are all identified using AI-based risk detection.

Claims processing that is astute

Intelligent technologies have fundamentally altered claims processing, making it more efficient while decreasing risks, from chatbots for speedy resolutions to ML applications. In terms of risk management, data analytics has made significant progress in automating fraud detection, recognizing patterns in claim numbers, and enhancing loss analysis.

Analytics that predict

Predictive risk management is an essential component of every insurance company. However, while underwriters exercise proper risk selection when determining price, a person can only digest so much data. With the vast data available today, AI-based solutions have inevitably supplanted predictive analytics. For example, smart prediction algorithms may analyze data for trends in outlier claims, which result in unexpectedly large losses.

This enables insurance firms to arrange their policies in order to limit the likelihood of outlier claims. Predictive analytics may also assist in identifying common risk factors to incentivize cautious behavior, decreasing total claim volumes. Health insurtech, for example, examines hospitalization data to identify high-risk behaviors. As a result, the insurance company may incentivize safe habits that lower the likelihood of hospitalization amongst subscribers.

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