AI Can Streamline Agent, Underwriter, and Customer Interactions — Great American Insurance Group Data Science Manager

(US & Canada) Nasser Mooman, Data Science Manager at Great American Insurance Group, speaks with Gavroshe Chairman Derek Strauss in a video interview about the future of insurance and AI, how Great American is exploring AI for risk prevention, and the application of AI in various sectors of insurance.

Mooman opines that AI’s transformative impact will be like that of the internet and mobile technology. AI will serve as a foundational framework, shaping who uses it and what can be automated and optimized to use as part of the ecosystem.

Sharing an instance from academia, Mooman says that, traditionally, students spent years memorizing information, which tools like ChatGPT can provide quickly and on demand. He maintains that the internet and mobile technology have created a revolution, and AI is following the path and will gradually become a global transformative force.

From the insurance perspective, Mooman states that Great American looks at all the good uses of AI. The data science team at Great American is exploring AI’s role in risk prevention. He reiterates the use of IoT technology such as sensors for flood, fire, and smoke detection to monitor potential risks in real-time.

Further, telematics data is already being used in automotive applications but could be expanded for risk prevention in the future. Additionally, Mooman affirms that AI can help streamline interaction between agents, underwriters, and customers. He notes that it is not about replacing human roles but rather simplifying and optimizing the policies accurately.

For instance, Mooman says that while drafting a policy, one must consider the consumer's background and history while referencing the policy information for effective risk prevention. He adds that while humans are prone to mistakes, generative AI (GenAI) can be leveraged to accurately summarize extensive policy data, leading to a well-suited policy.

Whether it is car, health, or home insurance, AI can analyze factors like location and demographic data to recommend a tailored policy, says Mooman. In addition to assisting adjusters and underwriters, this approach also incorporates proactive risk prevention, making it a promising AI application in the insurance sector.

Another potential AI application according to Mooman would be the streamlining of policy processes. For instance, AI could accelerate form-filling and make it accessible to insurers at their convenience.

Elaborating on AI-driven form generation, Mooman says that this would work by converting spoken or chat-based interactions into text, which AI would then use to fill out necessary policy forms. In the case of an accident, a customer could leave his details via voice message, and AI would summarize the claim details, thus simplifying the process.

Mooman affirms that intelligent form-filling cuts down on wait time and makes the process more user-friendly. Moreover, he mentions AI can enhance personalization and help modernize legacy technology within the insurance field.

Furthermore, organizations can also use synthetic data instead of real customer data to train models. This ensures better data security, says Mooman. However, since data remains critical, he asserts doing a lot of anonymizations and masking private data, such as addresses, SSNs, health numbers, etc.

In conclusion, Mooman states that after utilizing synthetic data to train a model during development, if the model’s accuracy is satisfactory, it can be moved to production. With this approach, end-users’ private data remains protected and unexposed, effectively functioning as a black box.

CDO Magazine appreciates Nasser Mooman for sharing his insights with our global community.