By KV Dipu – Senior President and Head of Operations & Customer Service, Bajaj Allianz Life Insurance
My earlier blog on AI use cases in insurance covered use cases such as customer conversations, motor on the spot claim servicing, integration with voice, fraud detection and cross-sell / upsell opportunities. Since there was a request for an encore, covering lesser known use cases, here we go.
Claims beyond motor insurance
AI-infused automation of claim processes in lines other than motor have picked up speed. While experts may still be required for assessment in complex and high value cases, initial steps such as entry, hard perusal, the classic step of “smelling” the low-hanging “rat” etc can be done by the AI engine, thereby freeing up the claims team for higher value work.
Paperless processing
When you have data coming to you in myriad forms (handwritten forms, printed forms, text, images, voice, video etc), the ability of AI engines to ingest this data at both scale and agility, and churn out meaningful insights is just amazing! This is precisely where AI engines can help create a competitive advantage.
Granular underwriting
As insurers move away from a one-size-fits-all approach, the ability to pick data across hundreds of variables to create multiple cohorts is a key factor in terms of both profit maximization and loss minimization. And this is precisely where AI comes in, not only from the point of view of a heatmap comprising strike zones or prohibited zones, but also in terms of equipping people at the front end via immediate output at the click of a button when they are interacting with customers and partners.
Joining the dots
The paradigm shift in data collation from complete data collection from the customer to minimal data collection and auto fill from legititmate third party sources has been so profound that this trend is hardly even noticed as a change element! In this context, the ability of AI engines to collate data from multiple sources in a seamless manner plays an extremely critical role in terms of customer satisfaction.
Pricing & risk management
The ability of an insurer to assess risks well and come up with a win-win pricing model is one aspect which sets it apart from the others. In this context, as risks evolve with the addition of many more dimensions, and pricing is impacted by an increasingly complex landscape, firms can leverage AI to automate various aspects such as scenario building, assessment, constant recalibration, competitive mapping et al to gain an edge over peers.
Product recommendations
As customers leave behind their digital footprint on social media, insurers can leverage AI to understand patterns, conduct deep-dives and help customers navigate through the ocean of plenty (in terms of the plethora of products available) and help them with real-time recommendations.
Damage analysis
When firms grapple with large assessments, such as an aerial survey of land with damaged crops, AI engines come to the fore, via tool-based capture (drone images, satellite mapping etc) and assessment of thousands of images at speed to come up with clear insights and recommendations.
These use cases clearly indicate the massive scope for AI beyond the pale of the obvious. The key is “customer first, AI or technology second” rather than the other way round.
As Ray Kurzweil says, “Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.”