Top 5 Use Cases for RPA in Insurance
From a consumer’s standpoint, the insurance industry is often associated with delays & long turnaround times. It is also an industry that isn’t very quick to adopt new technologies that can resolve some of these issues. But with Robotic Process Automation (RPA), this is about to change. RPA can move insurance companies towards a more efficient future with shorter waiting times for consumers to receive their claims.
The biggest headache facing insurers today is the amount of manual work they need to do. Employees spend most of their time going through data and filling out various parts of the policy form, such as conditions and exclusions, along with various other basic details about the insurance seeker. Some of this could be repetitive, and doing a lot of this will lead to fatigue, which in turn leads to errors, which could be costly on many levels for insurers.
RPA, with the help of AI, aims to automate all these mechanical, repetitive tasks in an effort to eliminate these errors & improve efficiency. Below, we take a look at some use cases that RPA in insurance.
Automated Data Entry:
Let’s take the example of auto insurers or property insurers. When they’re looking to onboard new clients, there is a lot of form-filling the client needs to do. The forms are generally long, and takes time to fill. Here, a mixture of RPA & AI can help. Data can be pre-collected from various data sources and filled automatically.
In the case of a vehicle insurance seeker, the vehicle data is obtained from previous policies that the customer shares with the broker. The data is entered manually into the acord form. This process is time consuming especially while entering the data for vehicles (as there could be over 100 vehicles). Each vehicle has a 17 digit VIN number. An ideal solution would be to fill the sections of the ACORD form pertaining to the Vehicle’s information when the VIN number is entered.
While acquiring a new customer, the current setup requires the customers to go through a lot of paperwork. This comes after they’ve already spent a lot of time reading several documents from multiple insurers, to arrive at the one they feel is best. So having to fill out forms after forms will seem like a very tedious task, especially in the case of organizations.
RPA can help by auto-filling some of these for the customer. Again, models can be set up to acquire data from several sources, and appropriate fields in the form can automatically be filled out. This reduces a lot of hassle for the customer, and reduces the time required for customers to get onboarded. Examples of data that can be auto-filled include company name, industry they serve, key persons of contact, number of locations present and so on. Such data can be gathered easily enough from public listings.
Underwriting & Pricing:
A very key aspect of insurance companies, underwriters need to be very sure of their decisions, since they could be responsible for a lot of money one way or the other. Traditionally, underwriters have precious few sources to go on to make informed decisions about individuals, or the risk possessed by them. It could take upto a month for a particular individual’s risk to be assessed, and many people would simply lose interest in the process midway. This results in a lot of customers lost for insurance companies
RPA and AI can help insurance companies reduce the time this process takes, by automatically gathering certain information from public databases & third-party providers. RPA can also obtain medical histories of insurers to help make better-informed decisions.
In addition to making better decisions, insurance companies can use RPA to roll out new plans or policies, or arrive at optimal price points for existing policies.
Creating smart proposals:
In case of big industries, insurance usually involves covering multiple items. For example, a factory would need to insure its plant, and the vehicles it employs to transport its goods. These need to come from separate vendors who would each specialize in one of these areas. A broker would have to gather proposals from multiple vendors and collate them into a single proposal that gives the customer best value.
Again, this involves a lot of time and energy. And with a lot of proposals coming in, it’s easy to oversee the value of some of them, in which case the consolidated proposal might not be the best one.
RPA can help insurers by automatically going through all the proposals, and selecting a combination that would best serve all sides. This will make it easy for brokers to walk customers through the proposal, and they’d do a better job of this since they wasted next to no time putting this together.
Oftentimes, insurance companies are judged by their dispatch of claims. Failure to do so or being tardy about it can cost a lot more than just lost customers. And just like most aspects of insurance, this too is riddled with problems of excessive manual labor & potential for delays & errors.
With RPA, insurance companies can vastly reduce their claims calculation period and speed up their processing time. Data can be gathered from all the required sources to calculate the final claim amount, which can in turn be quickly dispatched to the concerned party.
RPA in Insurance is only set to take off, since the industry cannot afford to ignore this wave. With so many of their manual work getting automated, along with their errors getting eliminated, this field is one that’ll prove too good to turn down. We could also see a big boom in the range of products and services becoming available in RPA, even for specific aspects within the insurance industry.