Delays in handling insurance claims are a significant cause of customer disappointment. According to a study from Bain & Company, 80% of insurance companies CEOs believe they are offering an excellent experience to their customers. When, in fact, only 8% of their customers think so. This fact alone calls for thorough questioning of the service delivered by insurers. The speed and quality of insurance claim handling impact customer satisfaction. Insurance is an environment where competition to retain customers is fierce. A report from data and analytics firm GlobalData claims that more than 30% of UK consumers said they would acquire some form of insurance commodity from “alternative providers” such as tech mammoths like Google, Amazon, and Facebook.
Today, the claim handler has the responsibility for the whole insurance claim process. The claim handler duties include the FNOL,i.e., the first notice of loss (the moment the client records a claim with the insurer), the policy analysis, and the gathering of the claim circumstances. The review of the insurance claim starts with the study of the documents provided, the loss estimation, and a coverage decision. The conclusion of this process is either an inquiry for fraud detection or resolution decision. As mentioned, the whole process lasts, on average, 46 days in the UK. Most insurance customers are unhappy with this duration. Machine Learning can alleviate, in part, the burden of the claim handlers. As a consequence, they can concentrate on added value tasks such as managing complicated uncommon claims. While simultaneously providing an improved response time for processing standard insurance claims.
Typically, the adequacy between the insurance claim and the insurance policy, the loss estimation, and coverage decision can be automated thanks to Machine Learning. The decision to trigger a fraud inquiry can also benefit from Machine Learning. This way, in a few clicks, the insurance claim is entered and evaluated. There is no interaction with a claims handler. Nor is there any need to phone a contact center to wait an indeterminate amount of time for a response.
TADA, the Small Data Machine Learning solution from MyDataModels, is ideally suited to help claim handlers. TADA can learn from historical insurance data. With only a few hundred or thousand records, TADA models each pertinent stage of the claim handling process. No need to be a Data Scientist either to use TADA. It is a simple tool that anyone can use. Upload your data, and off you go.
MyDataModels brings a self-service solution to insurance professionals who want to save time on insurance claims handling by using their Small Data.
Improving customer satisfaction is a crucial challenge for insurances. It is feasible to implement self-service, computerized smart claims using Machine Learning. Automating insurance claims reduces handling time drastically. And in the process, the customer tastes and needs can be uncovered. This way, insurers have a unique means to provide the claimer unparalleled direct access to replacement goods providers, assistance providers, and repairers. Add to this the ability for the claimant, on any device, to record the insurance claim, get an instant automated decision, and receive replacement or repair choices, with uniquely targeted proposals to keep customers in connection with you – all in a retail-style happening.
“TADA improves insurance customers satisfaction by shortening the insurance claim response time.”
In the end, customer satisfaction improves, hence customer loyalty. To top it all, the ability to sell extra targetted services is a venue for more insurance revenues and profits.
“Creating an ai claims handler”, Marcel Gordon, JULY/AUGUST 2019, Claims Magazine