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Case Study

Motor Liability Decisioning

AIS provide cognitive technology solutions to the UK motor insurance industry and are currently focused on helping insurers and third-party representatives make better accident liability decisions.

The problem

Identifying who was at fault in a motor accident (referred to as a liability decision) is a difficult task and an historical area of weakness in the motor insurance industry. There are a number of problems with the liability process which increase claims costs throughout the industry.

The cost of car insurance now gobbles up 10% of a young driver’s average salary. Correcting issues at the beginning of the claims process can reduce the costs of slow or incorrect liability decisions.

First Notification of Loss (FNOL) is the process where a customer first notifies their insurer. These calls are generally handled by contact centres where employee turnover is high. Contact centre roles are often low paid, employee engagement poor and handler error is common as a result.

A poor liability judgement at FNOL can cause claim delays and result in significantly higher costs being incurred by the insurer later in the claim process. Poor liability judgements can also impact an insurer’s reputation and customer satisfaction levels.

An incorrect liability decision may be costly, but even a slow liability judgement at the outset can affect the insurers ability to retain control of the claim.

Insurance fraud cases are expected to double with the rise of automation. Better liability decisions can spot fraudulent claims earlier in the process

While liability has been a significant area of friction in the industry for some time, Rainbird is the first technology recognised as capable of truly automating this process, providing a model of liability that is capable of delivering comprehensive complex judgements.

The solution

The Rainbird team sat down with the AIS liability experts to encode their best-practise methods into extended knowledge graphs over which Rainbird could reason. Rainbird takes each claim’s data and compares it with the knowledge graph, asking efficient clarifying questions via a simple chat interface when it needs more data. The result is an accurate automated judgement, using a probabilistic method modelled on human expertise. Decision Intelligence powered by explainable AI.

I don’t come from a development background at all – I’ve got no professional IT skills – but I’ve been able to use the platform to code the knowledge that we’ve got
Danny Thomas, Chief Solutions Architect at Automated Insurance Solutions

Rainbird handles any uncertainty and missing data and simply takes that into account when providing feedback.

Existing liability solutions actually require a great deal of understanding from the users in order to get the best from them
Ben Taylor, CEO of Automated Insurance Solutions

Each outcome includes a rationale and a full audit trail backed by evidence, satisfying the client’s obligations to Treating Customers Fairly (TCF) and their other regulatory obligations.

By implementing the solution within a liability process, insurers and others in the motor claims industry see a material improvement in both the accuracy and consistency of front-end liability decision-making.

Based on current industry claim costs, even a reduction of 2% will yield savings of more than £40 per claim.

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