Financial Services

Analytics For Financial Institutions
Financial Services
Analytics plays an important in the financial services domain. Applications of analytics are plenty – credit scoring, claims processing, hedging, portfolio analysis, and customer analytics to name a few. From improving internal efficiencies (improving yield on debt accounts, selecting the right relationship manager) to increasing bottom lines (identification of price-insensitive customers, profitable brokers & regions, product bundling), companies must practice analytics to survive.

How To Best Leverage Analytics?

Analytics is proven to be extremely effective to increase the lifetime value of a customer. It can help identify cross-sell and up-sell opportunities to improve the campaign response rates, identify customer purchasing patterns and increase revenues & customer loyalty. Analytics can help organizations manage the market risk they face in terms of fluctuations in interest rates, exchange rates, commodity prices etc. Hedging strategies can be improved due to the insights gained from analytics.

Types of Mathematical Models

For over five years, DecisionCraft has built a variety of mathematical models for US and UK based financial organizations. These include:
  • Credit scoring models
  • Portfolio performance analysis
  • Customer profiling and segmentation
  • Price elasticity modeling
  • Loan Pricing
  • Increasing Revenue
  • Increasing the lifetime value of a customer
  • Identifying cross-selling and up-
    selling opportunities
  • Managing Risk
  • Preventing loss due to customer default
  • CART (Classification and Regression Tree)
  • Logistics Regression
  • Linear Programming
  • Nonlinear Programming
  • Generalized Linear Models
  • Association Rules
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