The insurance industry has collected an uncountable volume of data over recent years. Today, the talk amongst insuretech visionaries is: How do we analyse this data to help us intelligently make decisions that support our strategic objectives?
Nowhere is this question more pertinent than the fight against insurance fraud.
For much of the last decade and before, gathering and connecting data sets has occurred in parallel with a boom in rules-based analytics systems that help investigators and insurers identify patterns of behaviour, uncovering fraud and dishonest claims.
As this activity has matured, the data sets have grown ever more complex and the rules-based systems which interrogate them on our behalf have taken on ever greater importance, sometimes with thousands of human-crafted and curated rule sets operating on a single strategic function.
At times these applications have been accused of slowing down the process of making a claim for honest customers. This has led to organisations varying the ways in which they deploy their fraud checks, always having to calculate the payoff between maintaining rigorous validation processes and the need to give people a smooth journey when they make a claim.
However, today’s insurance company is increasingly able to look beyond the purely rules-based application, to a new generation of machine learning tools which apply a version of artificial intelligence hitherto unavailable.
In the simplest possible terms, machine learning tools apply algorithms that can learn from and make predictions on data, with the output one of simplification for the user and far less manual curation of the previous rules-based data analytics.
10,000 rules can be whittled down to a fraction of that number, with the potential for fraud checks to be carried out at a much deeper level across a portfolio without disruption to claims experience.
If you would like to explore the possibilities of Machine Learning and anti-fraud pattern recognition with EWIS, please contact Ann Lomax, Senior Client Relationship Manager, ExamWorks Investigation Services email@example.com