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 1. 
 2. Technology
 3. Generative AI in insurance seen to accelerate within 12-18 months: expert


GENERATIVE AI IN INSURANCE SEEN TO ACCELERATE WITHIN 12-18 MONTHS: EXPERT


EXPERT WARNS ABOUT 'UNPREDICTABLE' DEVELOPMENT



TECHNOLOGY

By Gia Snape

Aug 04, 2023 Share


The adoption of generative artificial intelligence (AI) like ChatGPT is
projected to take off across the insurance landscape, with one expert putting
the timeline at 12 to 18 months.

Vikas Bhalla (pictured), executive vice president and head of insurance at data
analytics and digital solutions company EXL, said that most insurance companies
will be exploring use cases for generative AI and large language models across a
range of functions during that period.

But he cautioned that even as traction grows for AI, it’s extremely difficult to
predict what its use will look like.

“What you will see over the next 12 to 18 months is a progression, as the
technology becomes more recognised and more accepted,” Bhalla said.



“People will learn how to manage the risks associated with it, and insurance
organisations will move from employee-facing to rep-facing to customer-facing
uses of AI. You’ll see the impact really going up, and that is going to be a big
change.”


‘EXTREMELY DIFFICULT’ TO PREDICT AI DEVELOPMENT

Chubb CEO Evan Greenberg was the latest to convey a sober stance on the impact
of AI on insurance, even as he confirmed Chubb is looking to scale its use of
the technology claims over the next two to three years.

In a Q2 2023 earnings call, the CEO told investors that applications of large
language models would be iterative, and therefore take more time to produce
benefits for insurance companies than “breathless rhetoric” in the industry
implies.

Bhalla agreed that it’s too soon to see what form such technologies will take
even as observers speak about AI’s increasing ubiquity.



“The form that such technologies will take six months to a year from now will be
very different… because the pace at which new disruptive technologies is
increasing,” Bhalla told Insurance Business. “It’s extremely difficult for one
to predict what a form of that is going to be.”

Despite this, insurance companies are keen to deploy customer-facing AI
solutions, according to Bhalla. EXL, which works with large insurers and brokers
worldwide, said it has seen a “frenzy” of client interest in ChatGPT over the
past few months.


WHAT ARE THE MOST POPULAR GENERATIVE AI USE CASES AMONG INSURANCE COMPANIES?

According to EXL, the most popular initial applications for generative AI in
financial services, including insurance, include:

 * Customer service agent assistance – these include bots that search customer
   activity, claims and payment and investment histories to furnish live
   customer service agents with scripts to answer questions more effectively.
 * Contract analysis and drafting - AI solutions to scour finance, legal or
   insurance contracts to extract key information, flag risks, or remediate
   issues.
 * Audit - AI that helps analyse 100% of compliance documents, versus the
   old-school approach of sample-based compliance.
 * Code generation - using generative AI to write code, check for bugs and
   streamline the product development process.

However, there are hurdles for insurance companies to overcome before any
significant generative AI usage takes off, EXL cautioned.

The company tells clients that data governance, data migration, and
silo-breakdowns within an organisation are necessary to get a customer-facing
project off the ground.

“Will insurers have tried [generative AI] in something [within 12 to 18 months]?
I think yes,” Bhalla said.

“Would they have scaled it up significantly? In my view, that's going to take a
bit more time. It will depend a lot on the learnings and constraints that we
see. There's still a lot of regulatory approvals and changes needed before
companies can scale up.”


THREE RECOMMENDATIONS FOR SCALING GENERATIVE AI

Bhalla shared three recommendations for companies experimenting with generative
AI: using closed data sets, keeping a human in the loop, and slowly progressing
usage over time to minimise risk.

“When you look at creating of your first few implementations, the AI should be
applied only to closed data sets,” he said. “You can take a pre-trained large
language model, but you need to train it on your own data limits initially.”

Organisations should avoid combining their internal data with external ones, and
refrain from exposing their data to the external, Bhalla advised.

“The second thing we telling clients is to have human in the loop,” the
insurance head continued. “You can’t delegate the decision making and running of
the operation [to AI], whether it is new business, underwriting, or claims. A
human in the loop is important because you need to make sure that there is a
checking mechanism.”

Finally, insurance companies can manage their risks by progressing the
penetration of disruptive AI technology. Customer-facing AI applications are
deemed the highest level of use, and therefore the riskiest.

“We recommend our insurance clients to start with the employee-facing work, then
go to representative-facing work, and then proceed with customer-facing work,”
said Bhalla.

Is your organisation exploring use cases of generative AI? Tell us about your
experience in the comments below.




RELATED STORIES

 * AI no 'silver bullet' for insurance – Chubb CEO
 * Trust in AI lower in high-tech countries, report finds


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