What if you could complete underwriting process for insurance or loan applications at five times the usual speed? Well, believe it or not, it is possible! Yet most people haven’t realized how to do it. And it does not require any coding to set up. You don’t have to pick data one point at a time or do calculations manually all the time.
Most of us typically type five times slower than we read. That means you would need even more time to make decisions in the underwriting process and type out the decisions. And since the average insurance company spends a significant portion of its operational time in this process, we should look into making things better.
Fortunately, you can get the underwriting tasks much faster with automation and artificial intelligence. Even if you’re not a programmer, you can use advanced AI technology in conjunction with the insurance company’s policies to determine whether or not to accept the risk posed by the customer.
In Deloitte’s terms, automation and A.I. can help to give rise to an “exponential underwriter”. The first this to do is you need to prepare a detailed workflow implemented in your underwriting process. You can use automation tools such as Gleematic to compile data from multiple sources and interact with multiple systems.
Then, you will need to prepare historical data to ‘train’ the bot for doing predictions of risks. The output of the training will be a ‘model’ to predict future risks and to make decisions.
After you get the ‘mode’, you are ready to use the bot for real work. Within your needs, you can customize several “scripts” (a set of instructions for the bot to follow). While the bot is doing ‘real work’, it will also learn continually and alert you if there’s any outlier data.
See the illustration below to know how machine learning works in the underwriting process.
The bot will ‘read’ documents that contain data of people who applied for insurance coverage. Then, it will take out the data of the person’s attributes (e.g. age, job, salary, housing, smoker/ non-smoker, etc.), check against databases or listings (e.g. Net Reveal, Factiva), then get the outputs and consolidate.
After the bot has analysed, it will propose to the human to review and finalise. Then the underwriting team can give another set of instructions for the bot to prepare the insurance quotations for the bot to send out as an email attachment or enter the data into a system. This capability will also be useful in onboarding customers in insurance, or to help to predict or making suggestions for suitable insurance products for specific customers.
By using this technology, your work can be done up to five times faster than usual. Also, this technology is almost 100% accurate for data collation/ data-entry, preventing human errors. As you only have to monitor the progress, you can spend your time on more important tasks.
By: Elsa Ajarwati