Ways Artificial Intelligence can help the insurance sector handle Risks

Insurance CIO Outlook | Saturday, July 02, 2022

Artificial intelligence (AI) is being used by insurers to identify and optimize the selection of risks for underwriting. Using sophisticated algorithms, data about clients is culled from industry databases and sorted into pre-determined pricing groups.

FREMONT, CA: Artificial Intelligence (AI) has emerged as a game-changing technology in the insurance industry throughout the past decade. In addition to promoting data transformation, it has been essential in developing more effective claims application and administration systems and enhancing hyper-personalized insurance products and services. But probably its greatest influence is in risk management, notably in claims and underwriting, where it is used with other technologies such as Machine Learning (ML) to identify and mitigate risks, detect frauds, and find a balance between risks and opportunities.

Maximizing risk choice

The use of artificial intelligence by insurers to identify underwriting risks and optimize risk selection. Intelligent algorithms sift through industry databases to extract relevant client data, efficiently classifying them into pre-determined pricing groups. Credit risks, governance and compliance risks, operational risks, market risks, liquidity risks, trading risks, cyber risks, and criminal risks such as fraud and money laundering are identified using AI-based risk detection.

Embedded AI and real-time interaction with industry databases have also improved client experience by making the underwriting process, including risk selection and pricing, faster and more efficient. These technologies quickly develop as a crucial competitive tool for insurance companies’ customer acquisition and retention. Given the prevalence of the Internet of Things (IoT) and monitoring devices in our daily lives and their access to accurate and vital data, AI-related technologies will assume a greater role in data analysis, risk selection, and pricing.

Effective claims handling

Intelligent tools, including chatbots for quick resolutions and machine learning applications, have transformed claims processing, making it more efficient and minimizing risks. Regarding risk management, data analytics has made great strides in automating fraud detection, recognizing claim volume patterns, and strengthening loss analysis.

Claims fraud is one of an insurance company’s greatest fears. Investigating each allegation can consume precious time and resources. Visual analytics, which involves the study of images and videos, has accelerated operations today. Insurers may conduct preliminary investigations with little resources and rely on highly accurate data, thereby eliminating false claims.

Forecasting analytics

Predictive risk management is an essential component of any insurance company. While underwriters make careful risk selection when determining price, a person can only digest so much information. With the vast volumes of data available today, predictive analytics has been replaced by technologies based on artificial intelligence. Smart prediction algorithms can examine data to detect patterns in outlier claims or those that result in significant unanticipatedly losses.

This enables insurance firms to arrange their policies to minimize the likelihood of outlier claims. Additionally, predictive analytics can aid in identifying common risk factors to incentivize safe behavior, reducing overall claim volumes. For instance, health insurtech examines hospitalization data to determine lifestyles associated with high risk. Therefore, the insurance company can incentivize safe habits that lower the likelihood of hospitalization for its clients.

Managing liabilities

Fixing liabilities is one of the greatest issues offered by AI-based solutions. The transition from human to technological decision-making produces a decision-making gray area that may potentially lead to governance and compliance difficulties. As integrated AI technologies become an integral part of the underwriting process, people must be cognizant of the unexpected biases resulting from their adoption. Algorithms are marketed as infallible systems for calculating risks, but they must be implemented with particular socio-cultural aspects in mind, and this is where machines might make mistakes.

Failure to account for these characteristics can result in two significant liabilities: discriminatory claim settlement and underwriting. Insurtech algorithms determine underwriting costs depending on gender, creditworthiness, and socioeconomic status, among other variables. Even if the other variables satisfy the desired criteria, the model output may contain a bias against any one element. Similarly, it can reject legitimate claims based on a mistake in fraud detection when it comes to claims.