The data analysis and scoring model scorecard provides more objective and fair customer classification criteria, helping customers in all industries to accurately screen customers and suppliers. CRIF's Expert Model Scorecard and Data Driven Model Scorecard help identify decent and problematic customers and enable more accurate decision.
  The CRIF scorecard can be combined with the StartegyOne decision-making engine for higher-order complex calculations. For example, in the case of bank loan business, different scorecard models (such as personal loan, credit card, mortgage, etc.) can be provided according to various product categories of consumer banking and enterprise banking to help banks achieve more objective future risk forecasting. It is a powerful forward-looking risk quantification indicator that can be used in loan origination & maintenance to the collections process, helping banks to make faster and better decisions. Applying a data analysis scoring model ensures consistent decision making, streamlined process efficiency, increased employee productivity, reduced costs, and increased customer satisfaction.
  The types and applications of CRIF scorecards are also divided into "application scorecards" for "new customer development" as well as "behavioral scorecards", "collection scorecards", and "marketing preference scorecards" for "existing customer development", which enables customers to analyze different groups of existing customers and apply the results to make more accurate classification decisions.