How Insurers are Using Machine Learning
Last week, we talked about DataRobot automating the machine learning process (from data to deployment), as well as how it is a highly efficient and user-friendly platform that makes curating accurate predictions the most advance it’s ever been.
This article serves to provide an insight on how machine learning is currently used in the insurance industry. The different ways insurers use machine learning include:
- Submission Prioritization – To predict premiums, conversion and losses for the policies that brokers submit. This allows insurers to focus on the most valuable business and provides a competitive advantage.
- Direct Marketing – To profile customers, model propensity to buy product, and measure price elasticity. It helps find the best prospects for a product or offer, the profitability of new products and the best market size for it.
- Conversion – To monitor the conversion rate on customer quotes. This helps insurersunderstand conversion driversand enables them to target price adjustments.
- Optimal Pricing – To build optimal pricing models that are easy for regulators to understand. It helps the company make better pricing decisions.
- Targeting Inspections and Audits – To identify policies with a high risk of misreporting and use the results to target inspections and audits. This procedure helps avoid mispriced risks without disturbing valued customers.
- Fraud Detection – To shift through claims and identify those that warrant deeper investigation.
- Claims Triage – To detect complex claims early in the lifecycle, so they can triage cases accordingly. This feature can save millions of dollars in claim costs through proactivemanagement (i.e fast settlement, targeted investigations and case management).
- Predicting Litigation – To auto-adjudicate claims and triage them accordingly, which significantly reduces claims cost, at First Notice of Loss (FNOL). Legal defense costs drive total claims cost for most commercial insurance products.
- Claims Forecasting – To delivers the high-quality predictions insurers need for smart decisions. Insurers need accurate loss predictions so that they can set reserves appropriately.
- Customer Retention – To identify causes of customer attrition. This helps the insurer take preventive action and target “at risk” customers.
Accelteam has been actively working with customers from various industry segments driving business improvements using machine learning solutions like Datarobot. Be it a customer with or without data scientists in their organisation, Accelteam has the right solution to meet the customer’s expectations in their machine learning and AI quest.
In fact for the past two decades, AccelTeam has been one of the leaders in providing data-driven analytics solution in this region. If you would like toknow learn more about DataRobot and machine learning solutions, please reach out to us at email@example.com