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Claims Management

Posted by Andy Austin on 21 August 2017

Big Data, AI and Machine Learning

Welcome to the final blog post of this series on how Machine Learning can help insurers make faster and more accurate data-driven decisions. In this blog, we will discuss some of the ways that these systems can assist in claims management.

Automated analysis

According to a recent study, over the course of one year, 99% of motor claims, 79% of home claims and 87% of travel claims were successful. The great thing about Machine Learning solutions is that they do not only assist with some aspects of claims handling management but can be useful across a range of different tasks. For instance, significant improvements can be achieved in claims leakage, auditing, subrogation and litigation avoidance with the use of Machine Learning systems. Machine Learning analytics can also be implemented for the analysis of multiple data forms including images, videos and sound.

Let’s suppose that an insurance company is experiencing a lot of issues related to potential miscommunication of its claims process. Staff are spending a lot of time with clients who either don’t understand how to carry out a claim or are attempting to do it in the incorrect way. Instead of spending even more time manually investigating or guessing the root cause of the issue, the insurance company could implement some time-saving Machine Learning tools. Calls can be monitored between customers and staff, for instance, so that any miscommunication is noted and addressed immediately. This will most certainly improve efficiency as well as customer satisfaction and compliance in the long run.

Making sense of Big data

The tools that come with Machine Learning can access very large amounts of raw, unstructured data from a variety of internal and external resources. Users of Machine Learning also have the ability to customise algorithms which suit the company’s business specifications. In the case of insurance, more complex analytical tools can be implemented for complicated claims to streamline settlement processes and bypass inaccuracies, leading to overpayment. 

As comparison to traditional data analytics mechanisms, Machine Learning tools deliver improved accuracy and speed - and not only for sampled data. Claims processing in general can be carried out in several minutes using Machine Learning technology, whereas traditional systems would take weeks or months. With Machine Learning, a lot of the handling process can be automated and some claims can be fast-tracked to reduce cost and handling time. Moreover, the algorithms in these AI systems can identify data patterns that indicate potentially fraudulent claims.

To sum up, Machine Learning is a truly invaluable tool for insurers looking to maximize their efficiency, accuracy and customer satisfaction. When it comes to claims management, AI systems can assist a business across a whole range of essential tasks at unprecedented speeds.