False Claimmants Identified
Reduction in Claims
Client is a manufacturer of spare parts for Commercial Vehicles and Trucks, with a sizeable market share in the country. Thet lose a fair share of Revenue to claims made by Fraud claimants.
The Analytics solution has helped reduce total claims against poor quality products by 50%, and profit margin has increased by $250 million.
The Client is a leading Spare Part Manufacturing Company for Commercial Vehicles and Trucks, with a sizeable market share in the country. The Company earns a sizable sum of annual revenue amounting to $2 Billion but loses a fair share of its Revenue to claims made by Fraud claimants. In the past year, it faced losses totaling to $500 Million to pay out such fraudulent claimants.
Given this situation the Spare Part Manufacturing Company is looking for a Big Data analytics solution that will pull data from the Datalog of the server to determine:
We developed a Big Data analytics platform using Golang , Python, and Hadoop to pull data from Terra Data. The Platform can produce Historical Analytics based on Historical Data, and at the same time, it offers Predictive Analytics based on The Hadoop Data storage. The client has a Data volume turnover of 2PB annually.
This Data is in the form of Structured as well as Unstructured Data which is sourced from Sales, Marketing, Purchase, Claims paid/made, Dealers/Customer Information, GPS information and much more.
Our Analytics solution collects, stores and analyses data collected from this Terra Data by scheduling job to pull relevant data. The job scheduling lasts 15-18 hours.
Deciphering this massive amount of data and previous GPS information the platform can determine:
The predictive/ Big Data Analytics platform thus provides the Client with crucial information. This prediction aids them in taking accurate actions to avoid fraudulent claims.
The cluster for creating this platform includes:
The Spare Part Manufacturing Company has benefited immensely from this solution.
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