A good indicator is usually how investment or insurance reacts.
Generative AI is Coming for Insurance (May 2023 Fintech Newsletter)
by Joe Schmidt, Sumeet Singh, and Seema Amble
fintech Generative AI
By Andreessen Horowitz
TABLE OF CONTENTS
Generative AI is Coming for Insurance
Opportunities & Risks with Third-Party Payment Links
Visa+, Interoperability, and Creating Clearinghouses for New Payment Methods
Featured Tweetstorms
More From the Fintech Team ...
Generative AI is Coming for Insurance
Because underwriting, selling, and servicing rely so heavily on humans processing large quantities of written or verbal communication, existing tools have struggled to properly automate these services and materially impact loss ratios (losses on written premiums) and expense ratios (underwriting and servicing written premiums). Large language models (LLMs), with their ability to proficiently collect and distill large amounts of data, could change this as they can augment or fully replace the process of a human combing through large amounts of data.
While current machine learning technology allows for improved decisioning on simple products like auto and home insurance, more complex underwriting processes like commercial and life insurance remain challenging. This has less to do with the process of decisioning relevant data and more to do with collecting and synthesizing the relevant data. While traditional ML models have helped dramatically improve more standardized underwriting processes like home and auto, LLMs could potentially help with the more complex group by gathering data to help underwriters make better decisions, especially in more intricate cases like large commercial policies where more context and follow-up questions are required. For example, most large commercial policies cover dozens or more locations, and each location has specific nuances (such as electrical panels, fire doors, sprinkler density/effectiveness, management effectiveness, amount of combustible storage) that must be gathered from the applicant, understood by the underwriter, and evaluated against underwriting guidelines. LLM-powered workflow software for underwriters could drive down underwriting time and cost while increasing accuracy.
On the sales side, considered purchases, like life or disability insurance and annuities, are primarily sold offline through human agents and brokers because they’re complicated products that buyers often have questions about. (Consumers are quicker to buy mandatory insurance products, like home or auto insurance, online.) LLMs trained on customer data or materials on what policies are appropriate for a certain customer situation could help answer complex questions for consumers about what policies they should buy and how that policy might impact their unique needs.
And finally, carriers and agencies employ large policy-servicing divisions to help with changing policies, customer support, and claims, as well as “internal wholesaler” teams to constantly monitor and service the production of affiliated agencies or brokerages. Think of these as vertical-specific call centers where a representative needs to distill what a customer, agent, or broker actually needs during a conversational dialogue, and either respond with the answer or enter the appropriate information into a system. Allowing LLMs to manage some of these conversations could dramatically improve efficiency and profitability. ...
—Joe Schmidt, a16z fintech partner (And More)
And further from GPT:
Artificial intelligence (AI) is increasingly being used in the insurance industry to enhance various aspects of operations, risk assessment, underwriting, claims processing, customer service, and fraud detection. Here are some ways in which AI is being applied in the insurance sector:
1. Risk Assessment and Underwriting: Insurers are using AI algorithms to analyze large volumes of data, including customer information, historical claims data, and external data sources, to assess risks more accurately. AI models can help automate the underwriting process, improve risk prediction, and determine appropriate premiums based on individual risk profiles.
2. Claims Processing: AI-powered systems can automate and streamline the claims handling process by analyzing claim data, validating policy coverage, and determining claims eligibility. This helps expedite the claims process, reduce manual effort, and improve accuracy.
3. Customer Service and Personalization: AI chatbots and virtual assistants can assist customers in answering queries, providing policy information, and guiding them through the insurance process. Natural Language Processing (NLP) enables these AI systems to understand and respond to customer inquiries effectively, enhancing customer experience and reducing response times.
4. Fraud Detection: AI algorithms can identify patterns and anomalies in data to detect fraudulent activities. By analyzing various data sources, including historical claims data and external databases, AI can flag suspicious claims for further investigation, helping insurers combat insurance fraud more efficiently.
5. Risk Management: AI can help insurers assess and mitigate risks by leveraging real-time data from various sources, such as sensors and Internet of Things (IoT) devices. This data can provide insights into potential risks and allow insurers to offer risk prevention and mitigation strategies to policyholders.
6. Actuarial Analysis: AI techniques can assist actuaries in analyzing complex data sets and developing models for pricing, reserving, and risk assessment. AI models can help actuaries make more accurate predictions, identify emerging risks, and improve overall actuarial analysis.
It's worth noting that while AI offers significant opportunities in the insurance industry, there are also ethical considerations, such as data privacy, fairness, and transparency. Insurers must ensure that AI systems are designed and implemented responsibly, with appropriate governance and oversight.
Overall, the integration of AI in insurance has the potential to improve operational efficiency, risk assessment accuracy, customer experience, and fraud detection, ultimately benefiting both insurers and policyholders.
No comments:
Post a Comment