--Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations.
--It could automate much of the traditionally arduous claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours.
--Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity.
To bolster innovation, insurers are turning to a technology that, in its short lifetime, has already created massive changes in business and the world. Generative AI, a type of artificial intelligence that can create content, rather than simply analyzing existing data, has been at the core of experiments in trying to optimize insurer processes, predict risk and develop customized policies for individual customers.
There are three key areas in which generative AI could transform insurers: underwriter workflow, claims processing and fraud detection.
1. Underwriter workflow
Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations. This can save time and improve efficiency, allowing underwriters to focus on underwriting. For example, on new business, generative AI can analyze past quotes to assist in risk triage, scoring and classifying policies to assist underwriters in risk selection. This is particularly useful for lines of business where there are significant policy volumes such as financial lines and general aviation.
With the explosion of data, accurately assessing risk now can mean analyzing vast amounts of information. Generative AI can evaluate this data in real time, allowing insurers to identify emerging risks as they enter the picture and tailor their policies accordingly. With this information in hand, they can work to develop customized policies reflecting the specific risks faced by individual customers in different regions. Looking at other types of non-natural risks, generative AI can analyze social media data and other sources of information to identify and predict the likelihood of incidents related to cybercrime, fraud or other emerging threats.
2. Claims Processing
For as long as it has been an established practice, claims processing has been a labor-intensive and time-consuming process, involving extensive paperwork, manual verification and often lengthy delays. Generative AI could be the end of this traditionally arduous process, as it helps insurers automate much of the claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours.
For example, using natural language processing, generative AI tools can understand and analyze claim forms, quickly identifying discrepancies and pinpointing gaps in information. As a result, the claims process speeds up, and the likelihood of errors and inaccuracies drops drastically. On top of this already welcome improvement, generative AI can be used to automate claims verification, using machine learning algorithms to identify potential fraud or other irregularities, helping insurers reduce the risk of fraudulent claims and ultimately improving the overall efficiency.
3. Fraud Detection
Fraudulent claims cost the industry billions of dollars each year. Generative AI can be a powerful tool in the fight against fraud, allowing insurers to identify and prevent fraudulent activity, instead of scrambling to ameliorate its effects after the fact.
Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity. This analysis can result in surfacing patterns of behavior consistent with fraudulent claims, such as multiple claims filed within a short time, injuries claims inconsistent with the reported incident or those filed from locations known to be associated with fraud. By identifying these patterns early on, insurers can block payments to claims highlighted as fraudulent, reducing financial losses and protecting customers from potential harm.
As the market continues to evolve and generative AI tools become more sophisticated and powerful, insurers that embrace these technological developments will gain a competitive advantage and thrive in the face of disruption. However, it is important to recognize that there are also challenges associated with the adoption of generative AI in the insurance industry, including data privacy concerns, regulatory compliance and the need for skilled data analysts and other professionals to manage and interpret the data generated by these systems.