How Covéa plan to save £1 million detecting fraudulent insurance policies
Customer Company Size
Large Corporate
Region
- Europe
Country
- United Kingdom
Product
- Seldon Deploy
Tech Stack
- Python
- Kubeflow
- Artificial Intelligence
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
Covéa Insurance Plc is the UK underwriting business of leading French mutual insurance group Covéa. The company offers commercial, motor, high net worth, property and protection insurance through its Standard & Poor’s A+ stable rating. Covéa Insurance serves two million policyholders and generated over £725.7 million in premiums in 2020. Their goal is to become the most advanced AI factory in the industry and to deliver value to customers and partners through efficiency and personalisation.
The Challenge
Covéa Insurance Plc, the UK underwriting business of leading French mutual insurance group Covéa, serves two million policyholders and generated over £725.7 million in premiums in 2020. The company is facing a significant challenge in the form of insurance fraud, which is costing the industry over £1bn a year. One of the most complex and hard-to-detect types of fraud they face is Ghost broking. This is when a policy is purchased by a middle person for a customer using false or stolen information to reduce the premiums. In the event of a claim, these policies would be legal and Covéa would have to pay out. As Covéa is mainly an underwriter, they often do not deal with the policy holder directly, so they had less data to work with to detect fraud. The call handling team were doing manual searches and checks on over two million new quotes per day. The scale was far too much to deal with in an efficient timeframe.
The Solution
Tom and his data science team at Covéa developed a solution that targets ghost brokered policies, using the capabilities of Artificial Intelligence (AI) with a “human on the loop” system to spot fraudulent activity patterns. They needed to put an ML pipeline into place quickly. They had the Python and Kubeflow skills in-house. They needed to be able to deploy models quickly and, most importantly, they had to be able to explain the decisions their models were making for the high standards of regulatory compliance within Financial Services. After a number of trials and proof of concepts, Tom chose Seldon Deploy as his team’s model serving and explainability tool. The models were put into production by a team of just four people using Seldon Deploy. The models were able to recommend over 1,000 existing policies to be reviewed by the Policy Validation Team.
Operational Impact
Quantitative Benefit
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