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Industry: Financial Services


Overview: To predict refinance churn to ensure customer retention and reduction in opportunity loss


Frequently fluctuating global and regional economic cycles have made it difficult for banks and other financial services companies to design products and services that hedge the risk of default when economy takes a downturn. Long term loan products with fixed EMIs, for example, assume that the customer will have a constant availability of capital for payback even after owing to changes in economic environment and other personal obligations.


A refinancing solution is prudently utilized by the industry to hedge the risk of default along with retaining and increasing its customer base. Though a powerful tool, such a solution requires data from multiple channels to identify the right customers at the right time. Enterprise data available with the banks is generally outdated and generally completes the puzzle partially.

Enterprise Challenges

  • Outdated and incomplete customers’ demographic and behavioral data
  • Insufficient representation of social and public data in decision making 
  • Data Analytics team working in silos and presenting an incomplete picture about customers’ wants and needs


HyperGraf™ Solution

  • Utilizes enterprise, social, real estate, salary and job related data to zero in on customers’ current debt servicing capability
  • Collects customer related 3rd party data specific to latest salary, job and real estate
  • Calculates probability of attrition to identify risky customers and predict customer churn through customer segmentation and customer 360 profiles
  • Provides ability to segment customers into those having a high chance of refinance, having high customer LTV, having fair to excellent credit score and those who have already applied for Refinance and target these customers accordingly.  
  • Captures insights from multiple customer interaction channels through NLP and SNA



  • Prediction of customer churn for high LTV customers for risk hedging, brand value management, better top and bottom line
  • Better customer service by real time analysis of social and complaint data
Financial Services