Artificial selection

How artificial intelligence can help the insurance industry manage…

Artificial intelligence (AI) is used by insurers to identify and optimize the selection of risks to be underwritten. Using sophisticated algorithms, customer data is extracted from industry databases and sorted into pre-determined pricing groups.

FREMONT, Calif.: Artificial intelligence (AI) has become a breakthrough technology in the insurance industry over the past decade. In addition to driving data transformation, he has been instrumental in developing more efficient application and claims administration systems and improving hyper-personalized insurance products and services. But its greatest influence is probably in risk management, especially in claims and underwriting, where it is used with other technologies such as machine learning (ML) to identify and mitigate risk, detect fraud and find a balance between risks and opportunities.

Maximize the choice of risk

The use of artificial intelligence by insurers to identify underwriting risks and optimize risk selection. Intelligent algorithms scour industry databases to extract relevant customer data, efficiently categorizing it 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.

Built-in AI and real-time interaction with industry databases has also improved the customer experience by making the underwriting process, including risk selection and pricing, faster and more efficient. These technologies are rapidly developing as a crucial competitive tool for insurance company customer acquisition and retention. Given the prevalence of Internet of Things (IoT) and surveillance devices in our daily lives and their access to accurate and vital data, AI-related technologies will play a greater role in analytics data, risk selection and pricing.

Efficient claims handling

Smart tools, including chatbots for quick resolutions and machine learning applications, have transformed claims handling, making it more efficient and minimizing risk. When it comes to risk management, data analytics has made great strides in automating fraud detection, recognizing claim volume patterns, and enhancing loss analysis.

Claims fraud is one of an insurance company’s greatest fears. Investigating each allegation can consume valuable time and resources. Visual analysis, which involves the study of images and videos, has now accelerated operations. Insurers can conduct preliminary investigations with few resources and rely on highly accurate data, eliminating misrepresentation.

Forecast analysis

Predictive risk management is an essential component of any insurance company. Although underwriters carefully select risks when determining the 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. Intelligent prediction algorithms can examine data to detect patterns in outlier claims or those that result in large, unforeseen losses.

This allows insurance companies to organize their policies in such a way as to minimize the likelihood of outlier claims. Additionally, predictive analytics can help identify common risk factors to incentivize safe behavior, thereby reducing overall claims volume. For example, healthcare insurtech looks at hospitalization data to determine which lifestyles are associated with high risk. Therefore, the insurance company can encourage its customers to adopt safe habits that reduce the likelihood of hospitalization.

Liability management

Fixing responsibilities is one of the biggest problems with AI-based solutions. The transition from human decision-making to technological decision-making produces a decision-making gray area that can potentially lead to governance and compliance challenges. As embedded AI technologies become an integral part of the underwriting process, people need to be aware of unexpected biases resulting from their adoption. Algorithms are marketed as infallible risk-calculating systems, but they must be implemented taking into account particular socio-cultural aspects, and this is where machines can make mistakes.

Failure to consider these features can result in two significant liabilities: discriminatory claims settlement and underwriting. Insurtech algorithms determine underwriting costs based on gender, creditworthiness, and socioeconomic status, among other variables. Even if the other variables meet the desired criteria, the model output may contain bias against any element. Likewise, it may reject legitimate claims based on claims fraud detection error.