With businesses getting an overwhelming response, customer debts started to grow exponentially, and associated recovery was always a challenge at an overall level. The company had been storing a large volume of relationship data points for a long time, but none of this could be put to use to calculate the risk score. This resulted in the collections department’s is being handicapped in the recovery of certain bad debts. The data warehousing team at this big financial services company analysed large sets of customer records collected from sources such as loan management systems and credit bureau data.

 

The data warehouse architecture entailed importing all data into an OLAP database, calculating and speculating on a “Risk Profile” with human intervention, and then loading it into the ultimate master database.

How did Whiteklay overcome the challenges?

Deployed on the Hadoop architecture, the analytical framework brought about a revolution in the development of a customer risk profiling use case. With a mere 15-node distributed computing setup, the solution transformed the process of customer risk profiling.

By harnessing the capabilities of Hadoop, alongside Spark computing and machine learning processing frameworks, the system evolved to automatically profile customers and calculate associated risk scores. This advancement in the big data ecosystem not only improved the efficiency of customer risk assessment but also enhanced data governance practices within the financial sector.

benefits

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