Fighting Insurance Fraud with Machine Learning
25 July, 2017
Posted by Andy Austin
Welcome back to this blog series on how AI and Machine Learning can help insurers make faster and more accurate data-driven decisions with lower overheads. In the previous blog, we discussed the role of AI and Machine Learning platforms in quote conversion. Now, we will have a look at how these systems can be implemented to mitigate fraud and prevent losses.
Recently, insurers uncovered 130,000 fraudulent claims worth £1.32 billion across all insurance products in the UK. Clearly, this is a pressing issue, but implementing various measures to minimise fraud is a costly task for any insurance company. The challenge is to search the marketplace for the most cost-effective solution that provides improved performance through better detection and prevention.
Artificial Intelligence and Machine Learning uses the latest technologies to identify fraud. Models are available that can identify fraudulent activity during any given stage in a claims process through a combination of text-mining, database searches and modelling. Moreover, AI and Machine Learning can utilize insights from historic fraudulent activity to prevent the occurrence of fraud attempts in the future. Although fraudulent activity often goes undetected and is notoriously difficult to track with traditional data modelling mechanisms, the advanced possibilities afforded by Machine Learning intelligence can really expand capacity in that area.
The models that AI and Machine Learning can assist in building to detect and prevent future fraud are detailed and intelligent – much more so than traditional systems. AI and Machine Learning models are able to access data from both structured and unstructured data lakes and determine which data should be modelled to accurately detect fraud. As opposed to traditional models that are much slower and take a long time to build, Machine Learning models can be built, tested and purchased online within a matter of days, meaning that insurers can swiftly address evolving fraud strategies. Machine Learning models yield transparent insights that can optimise the process even more, stemming significant losses that can improve overall bottom line performance.
Let’s imagine that an insurance company is experiencing a large flow of claims, many of which are fraudulent. At present, the insurance company doesn’t have a Machine Learning system in place to assist with fraud detection and mitigation. So, it spends a lot of funds and time to check every application to prevent risk. Another insurance company with an equally copious flow of claims could save immense amounts of time and money by investing in a Machine Learning mechanism that can process huge amounts of data in a short time. This has the capability to reveal connections between factors that the human eye cannot detect, thus independently predicting fraud. So, the profits, time-saving and overall risk patterns of the company that has a Machine Learning system mechanism in place are noticeably improved.
In a market where fraud mechanisms continuously evolve in complexity and where AI systems are simultaneously on the rise, it’s very important for insurance companies to invest in the best possible fraud detection system.
To find out more about how Machine Learning can help your insurance business gain efficiency, improved customer value and risk mitigation.