Show Notes:

In this episode of Unleashed, Astrid Malval-Beharry discusses an AI case study with a top 50 homeowners insurance carrier in the US. Astrid was approached by their underwriting and innovation teams to digitally transform their underwriting workflow. Astrid shares an overview of the industry at present. The industry is facing challenges due to an increase in natural catastrophes, inflation, disruptions in the supply chains, and policyholders who prefer to have an Amazon or Uber experience with their insurance carrier. The client had three goals for the digital transformation project: increasing the level of straight-through processes, improving risk assessment, and realizing greater investment in inspection. Astrid explains what straight-through processing is and how it works using data analytics and AI-based and technology solutions.

The second goal was to improve risk assessment by analyzing the location of the property, the condition of the property, and the policyholders themselves. The client wanted to know how AI solutions could help enhance risk assessment, reduce premium leakage, and charge the right price for coverage.

The third goal was to improve the inspection process, which currently costs carriers a lot of money but only yields a few actionable insights. To achieve this, Astrid’s team shadowed underwriters across both regions and senior IDI to understand how consistently underwriting guidelines are being applied. The team also interviewed and benchmarked against competing carriers, InsurTech carriers, and carriers that look at the underwriting workflow with a different lens. This allowed them to see the art of the possible and make informed decisions about their underwriting practices without disrupting the workflow.

Employing AI Solutions for Insurance Companies

Astrid talks about what follows the research and benchmarking exercise and how they mapped the workflow and the ideal future state. Premium leakage occurs when insurance companies charge less for a policy than the actual premium should be to reduce losses and charge the right price for the coverage. The inspection process is often done by agents or license inspectors, leading to a lack of actionable insights. To address this issue, a preferred digital transformation engagement was conducted by shadowing underwriters across both regions and senior IDI. This allowed the team to understand the consistency of underwriting guidelines and the impact of different levels of underwriters on the process.

Competitive intelligence benchmarking was conducted against carriers with similar profiles and InsurTech carriers. This allowed the team to map the workflow as the ideal future state from an underwriting workflow perspective. However, the change should not be too abrupt, as the procurement process in the insurance industry is notoriously long.

A middle ground was identified by analyzing claims activities on the book of business NIS to identify the biggest losses and how implementing AI solutions would give the highest return on investment. Change management is also important, as it involves both technology and people and processes. The organization's readiness to implement new digital tech-driven solutions is also crucial.

Astrid also touches on the convergence of people and processes when implementing technological solutions in change management.

Questions to Ask an AI Vendor

Astrid shares a list of questions to ask an AI vendor, including accuracy, model explainability, model bias and fairness, and scalability. She has experience working with insurance carriers, analytics, technology vendors, and private equity firms, giving her a deep understanding of what solutions work and don't work. When selecting an AI vendor, it is important to understand a series of fundamentals about the solution.

The first question is about the accuracy and performance of the AI model. It's crucial to understand how the vendor measures accuracy and how they handle situations where the model may not perform as expected.

The second question is about model explainability, which is crucial in the highly regulated insurance industry.

The third question is about model bias and fairness, and how the vendor addresses and mitigates biases in their AI models.

The fourth question is about scalability. While some solutions are considered vaporware, and Astrid explains what vaporware is, there are legitimate, enterprise-grade solutions that have legitimate AI technology. By asking these questions, clients can better engage with the right AI vendor and ensure the right decision-making process. She states that licensing data from a vendor is the right path due to the ongoing maintenance required. AI vendors are now incorporating large language models, such as chat GPT, into their AI models. However, this is not the core competency of an insurance carrier, which is to assess risk.

Astrid stresses that results should not be expected too quickly. However, she does mention that they are already seeing results. She mentions a project that has been 16 months in development, and it is not expected that a solution will immediately bring new business or reduce expenses. However, the results have been significant, with a client seeing a 75% increase in straight-through processing and reduced manual injury interventions. Operational efficiency has also soared, and better risk assessment has been achieved.

Timestamps:

01:02 Digitally transforming underwriting workflow for a top 50 US homeowners insurance carrier

03:08 AI solutions for insurance industry digital transformation

07:14 AI implementation in insurance industry

13:42 AI model accuracy, explainability, bias, and scalability in insurance industry

17:54 Evaluating AI vendors for insurance industry use cases

Links:

Website: https://www.stratmaven.com/

LinkedIn: https://www.linkedin.com/in/astridmb/

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558. Astrid Malval-Beharry: AI Project Case Study

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