The Art and Science of Successful Credit Scoring

The Art and Science of winning Credit Scoring

Credit Scoring is simply creditworthiness, while Credit Scoring is the statistical analysis lenders and financial institutions perform to assess the creditworthiness or otherwise. Banks and other financial institutions use Credit Scores to determine whether or not an individual is likely to default on a loan, mortgage, or other debt.

“For most the term ‘Credit Scoring’ is interpreted as ‘application risk scoring’, but few actually understand the complexity of the subject matter beyond determining the probability of default of a new applicant” 

The process involves gathering, measuring and quantifying the many factors, from a multitude of sources, including default history, debt, or purchases on credit. But Credit scoring is not limited to banks. Many organisations, like mobile phone companies, insurance companies, landlords, and government departments employ the same techniques, and many are increasingly using alternative (non-financial) data sources, and Artificial Intelligence to calculate the creditworthiness of borrowers.

This course is designed to reset delegates preconceptions about Credit Scoring and broaden their horizons starting with the principles of Credit Scoring and how it can transform lending operations, improve portfolio quality, reduce costs and provide valuable management information to manage all asset classes.


It's designed to teach delegates how to design and implement the right Credit Scoring model, matching needs, requirements and capabilities with the different types of scoring solutions commonly available, including the potential pitfalls of a poorly implemented strategy with thorough, detailed walk-throughs, case studies and real-world examples from the world of Credit Scoring Principles & Best Practise

The Credit Scoring Principles & Best Practise course will show you

  • The basic requirements for building statistically predictive models and the volumetric data required, and what is required to build and validate the ideal ‘expert model’ over time
  • Options to consider when the individual organisation doesn't have sufficient data to build a bespoke or statistically predictive model
  • How comprehensive monitoring of scorecard performance provides invaluable insights into the risk exposures in your portfolio and how scorecard monitoring provides lenders with a mathematical basis for managing risk rather than judgemental decision-making
  • Combining Accept/Reject models and Policy rules to focus on expensive and valuable assets and  potentially) the most profitable customers
  • The common characteristics that most often demonstrate risk differential and rank ordering in both application scorecards and behavioural models
  • Why Credit Scoring is not a ‘silver bullet’ solution to risk decisioning and why Credit Scoring can never completely nullify the need for human intervention 
  • How credit bureaus develop generic ‘Bureau Scores’ and how these can be applied either in isolation or when combined with other bespoke Credit Scoring models
  • How to insure optimum performance of your Credit Reporting models over time including how to update scoring models through validation and dynamic recalibration
  • How to put together a project management team, the aspects they need to consider, and how best to incorporate the technology into a comprehensive risk management process
  • Implementing Credit Scoring beyond Credit Risk Management; the operational aspects of embedding scoring technology into an organisation, the types of resources required, and implementation strategies, from concept to customer
  • Where is the industry going? What to expect in the coming years and thoughts on the probable direction of Oversight and Regulation?