Customer Credit Evaluation Training


  • Develop effective credit policies that meet company’s objectives.
  • Use financial and non-financial analysis to assist in making the credit decision.
  • Understanding of Variables associated with Customer Credit Evaluation.
  • Monitoring & Review of Credit Rating based on change in circumstantial .

 Overview of Credit Risk:

  • Credit risks: definitions, components, exposures and events
  • Overview of the creditrating processes – Features & Attributes
  • Quantitative Review of customer available financial information
  • Variables assignment process to different nature ofbusinesses
  • Understanding Global & Regional Economic, Industry & Customer Specific attributes
  • Developing aconstantly performing Risk Review Model (RRM).

Case Study: To be Defined

Customer Data Extraction Techniques

  • Sources of Customer Data
  • Differentiation between value and non value based data
  • Data Extraction Techniques & Methods
  • Verification of reliability and accuracy of data
  • Data Clustering for evaluation
  • How big data can help in Customer Credit Evaluation.

Case Study: To be defined

Qualitative Review

  • Global Trends affecting economy & Industry
  • Benchmarking & Use of Empirical Data for Predictive Analysis
  • PESTEL & SWOT Analysis
  • Customer corporate/business strategy & Business Ambitions
  • Board & Governance Process
  • Extraction of Market Reputation
  • Growth Curves & Business Trends
  • Ethical & Legal Compliance

Financial Review

  • Financial Data Extraction – Sources & Reliability Issues
  • Non Traditional Approach to Financial Data Review & Analysis
  • Extraction of Publically Available Financial Information
  • Financial Statements & Ratio Analysis
  • Customer Business Financial Trends & Variations

Developing a Credit Evaluation Model

  • Defining Objective
  • Creating Agility in Model
  • Testing Quantitative Accuracy & Qualitative Mix Review
  • Flexibility& Multiple Variable Evaluation Capacity in Modelling

Modelling Tools & Techniques

  • Developing Data for Analysis
  • Descriptive Modelling using Statistics
  • Measures of Central Tendency
  • Standard Deviations
  • Hypothesis Testing
  • Regression Correlation
  • Probability Distribution
  • QualitativeInformation Scoring Model
  • Combining Data & Information for Analysis
  • Multiple Analysis based on Changes in Variables
  • Freezing &De freezing Strategies in Modelling
  • Ensuring agility in Modelling

Credit Reporting

  • Reporting Contents
  • Report should Include
  • Sources of Data & Information
  • Cross Referencing
  • Publically Available Information Sources
  • Visual Analytics
  • Financial Analytics
  • Credit Scoring Methodology Used
  • Credit Decision Supporting Information