Insurance is a data-driven industry. Data qualifies risk and is used to perform pricing on policies that mitigate that risk. During the process, different parties get involved.
Because insurance carriers want to sell products that cover the risks, they perform algorithms against large datasets that are capable of uncovering risks.
Agents want to better understand the needs of their customers and prospects. They want systems that enable them to punch in a name so they can query public and private data on a prospect—with the dual go
Finally, there are the insured customers: They want better products and claims processes.
“All of these parties benefit when they can obtain more intelligence from data,” said Andy Cassel, vice president of data and analytics at VertaFore, a software provider for the insurance industry.
To maximize the value they derive from their data, Cassel said insurance companies must do three things, which universally apply to all industries.
- Try not to boil the ocean. “If you don’t have a target use case, take the time to develop one,” Cassel said. “It doesn’t do any good modeling data if you don’t solve needs.”
- Once you define an artificial intelligence (AI) project, look at whether the project needs a technology or a process change. “Many organizations when they use AI tend to treat the technology as something they just bolt into their existing work environment,” Cassel said. “But introducing a technology like AI impacts people’s jobs. This is more than just a technology or a process change: It requires full-scale change management.” Change management is a major challenge in most corporate AI implementations that implementers don’t consider.
- Understand how the AI you create will integrate with existing workflows. No one has to use AI to do their job well,” Cassel said. “What you have to do is make AI a natural part of the job that makes sense and offers value to those who use it.”
Insurance is just getting started with AI
The insurance industry is only beginning to adopt these caveats. Meanwhile, companies understand that they have to overcome many years of accumulating hardcopy documents, referred to as “dirty data,” because the documents can’t be readily digitized.
“The insurance industry still largely relies on paper,” Cassel said. “So much data is locked up in paper and pdf files, and it has to be manually extracted by someone. In the process, there are errors and omissions.”
If AI and machine learning can transform these troves of documents into more usable, accessible information, employees, agents, and customers will get more value from the data.
To facilitate the digitization process, data can be scanned into a system, data patterns can be identified by machine learning, and value from the data can be extracted through a highly automated process.
Some companies are trying predictive modeling
Another path that many insurers and agents are pursuing is predictive modeling.
“With predictive modeling and data analysis, you can profile a retail line of business that you’re insuring like commercial auto sales,” Cassel said. “You can derive from data what a commercial auto company is likely to require in terms of insurance. You can also track the sentiment of the customer. Both techniques help agents determine who is happy and who is likely to shop elsewhere for insurance.”
These technologies are likely to make the jobs of those in the insurance industry easier, more predictable, and more precise, which will hopefully translate to more profits for the companies and more savings for the customers.
Original post: https://www.techrepublic.com/article/ai-and-machine-learning-are-making-insurance-more-predictable/
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