Case Studies
- Reduction in claims spend(Personal lines) by accelerating One tough claims settlement.(
Industry: Insurance, Function: claims)
- Redefining data assets for effective reporting and forecasting of key financial measures
with brokers (Industry: insurance, Function: Broker lines)
- Fraud identification/ rating predictive model for early detection of insurance fraud
(commercial and personal) (Industry: Insurance, Function: AML & Fraud)
- Litigation propensity model to reduce litigation costs on MOJ claims (Industry:
Insurance, Function: Legal)
- Pan UAE motor insurance comparison matrix to improve conversion, pricing and retention.
(Industry: Insurance, Function: Sales)
Our Client
- A British Multinational insurance company that caters to more than 35 million customers
worldwide. Dating it’s inception centuries back, it is also one of the largest general
insurers in the UK.
Business Challenge
- A substantial amount of money was recorded as claims leakage on personal lines which
impacted the overall COR.
- Identify the causes of this leakage and leverage BI to reduce the overall claims
spend.
- Enhance capabilities of the existing claims processing systems to foresee the
trajectory of costs.
Solution
- Using several types of data - structured and unstructured and after extensive
scrubbing of them, the key problem to the increased spend was identified to be
directly proportional to the lifecycle of personal injury claims of a certain range
value.
- A proposal of One touch claims was made to the client whereby a predictive model
based on extensive study of individual parameters, would churn out those claims that
can be settled on receipt and with a single touch.
- This drastically reduced the overall lifecycle of claims immensely, reducing the
spend.
