1 13 15 50-51

Emerging Tech Impact Radar: AI in Insurance

The use cases for NLP can include conversational user interfaces to improve personal and business efficiency, enhancing customer and employee interactions and satisfaction, transcription and translation, enablement of smart office/smart home, and even robotics. NLP will also support the transformation of human-machine interaction by enabling more natural voice communications. This can lead to significant disruption for existing products and technology providers that decline to incorporate NLP into their software, devices and robot solutions, since they will be at a competitive disadvantage or may even become obsolete. Insurers have frequently deployed NLP alongside chatbots and VAs for customer service. They’ve also used them alongside technologies such as ML and robotic process automation (RPA) in support of claims and fraud detection functions as sentiment analysis techniques are used to augment analysis of transactional data. NLP techniques are primarily deployed in policy issuance, customer service and sales/distribution, but can also be used in agent support, sales/distribution and billing/payment processing functions. Although some point solution deployments have been observed, particularly in individual life, more complex deployments have also been observed in property and casualty. These span multiple functional areas as insurers look to simultaneously scale and increase the efficiency of the business.

Recommended Actions:

Align with business stakeholders on accuracy and efficiency baselines for the process, which may vary across LOBs. Evaluate the entire front- and back- office elements of the service model in tandem to understand where and how NLP solutions can be integrated. ■ Treat NLP as a component that integrates with other platforms/applications, such as intelligent document processing solutions, and prioritize loose coupling based on workflow or event triggers. ■