3 min. read

Six Emerging Ways for Insurers to Realize Value from AI

Author

Sankar Narayanan

Chief Practice Officer, Fractal Analytics

Artificial Intelligence (AI), along with its related technologies are set to impact all aspects of the insurance industry, right from underwriting, claims to pricing. Advanced technologies and data are enabling the insurers to form quick decisions and make progress. We can already see it affecting distribution, with policies priced, purchased, and bound in real-time.

The winners in the AI-based insurance will be enterprises that use new technologies to create innovative products, streamline processes, and go beyond customer expectations for individualization and dynamic adaptation.

Even though all of this is known, it is not often clear how insurance companies can make AI deliver outcomes. Here’s Sankar Narayanan (SN), sharing his insights on the below six emerging ideas for realizing value out of AI for insurance companies.

Transform Underwriting: Text mining offers one of the most interesting solutions to improving underwriting processes and outcomes, and it has the potential to reduce the time to draft policies by 20 to 25 percent.

Improve the customer experience and deliver next-best actions: One of the highest opportunity areas to apply AI within Insurance, across most lines of businesses, is in driving customer engagement and experience.

Reduce customer friction and the cost to serve: There is considerable opportunity to reduce friction that customers face especially on the digital assets (e.g., websites) of insurance companies through a three-step approach of sensorizing the various parts of the digital assets that cause breaks in customer journey, applying sophisticated algorithms for anomaly detection and root cause identification and A/B testing at scale.

Application of AI across the insurance value chain: Insurers need to deploy a fail-fast approach and run multiple rapid Minimum Viable Proposition (‘MVP’) initiatives, in parallel. The most impactful emerging sources of internal data from our recent experience include Voice of Customer data (calls, social, etc.) and telematics, and our experience suggests that about 25-30% of MVPs will result in eventual operationalization at the enterprise level, which makes it critical to have a fail-fast mentality.

Beyond AI: A 2019 Gartner CIO survey indicates that most P&C and life insurers are still building out the foundation for analytics and facing legacy system challenges. With more sophisticated approaches emerging, the challenge of improving transparency and accountability of AI methods is becoming important to manage. This requires a behavioral sciences based approach to empathize, up-skill, and engage the people that need to act on the insights driven by AI.

Make measurement a priority: The philosophy towards measurement is critical to get right. It is important to internalize that the value of AI is not realized from the methods or number of models or statistical uplifts but from the execution of actions coming out of these models.

Author

Sankar Narayanan

Chief Practice Officer, Fractal Analytics