Financial Institutes and Banks have been leveraging Statistical Modeling & Data Analytics since last 4-5 decades. The applications of Data Science (or Data Analytics) has evolved and diversified since then. Innovating new products, creating customer experience, improving marketing effectiveness, reducing risk (credit, fraud, and market risks) and improving regulatory compliance are some of the key areas which employ data analytics and insights.

  • Customer Analytics for Financial Services
  • Customer Life Time Value for Credit Card Portfolio
  • Next Best Action in Banking


Increased competitions, reduced customer loyalty, lower differentiation, over regulation and lower trust in agents & brokers are some of the key challenges faced by the Insurance companies. Insurance providers are increasingly using Data and Decision (data analytics and insights) for creating competitive differentiation. The Data and Decision is used across customer life cycle - acquire, engage and service the customers.

  • Claim Fraud Modeling
  • Claim Volume Forecasting
  • Member Switcher Predictive Model


Retailers use Data and Decision Science for improving operational efficiency (e.g. low level of inventory cost, effective supply chain management etc), higher marketing effectiveness (e.g. sales driver identification, optimal budget allocation etc), creating customer experience and increasing customer loyalty (e.g. personalized product targeting, effective pricing)

  • Market Basket Analysis
  • Product Recommendation
  • Category Management


Due to increased digitization and availability of data (patient health records, Prescription information, clinical trial data, claim data, genomics sequences, sensor readings etc ), healthcare industry has been ahead of other industries in employing data analytics and statistical modeling.

  • Preventative Healthcare
  • Healthcare effectiveness
  • Hospital Operational Effectiveness