Scorecard Development Software
GDS Link offers custom Credit Scorecard Model Development, Monitoring and Implementation Services that allow lenders to evaluate credit-worthiness based on conventional demographical, financial, bureau and behavioral data.
What is Credit Risk Scorecard Reporting?
You can tell when a customer or prospect account might pay late if you’ve seen the signs before. At the same time, you know when things look too good to be true. Credit professionals interpret data – many different types of data – to inform this “hunch” and arrive at a logical decision of credit terms and limits. It’s a combination of smart data, sharp instincts, and time spent on due diligence.
Credit Risk scorecards are mathematical models that attempt to provide a quantitative estimate of the probability that a customer will display a defined behavior (e.g. loan default, bankruptcy or a lower level of delinquency) with respect to their current or proposed credit position. Credit Risk scoring uses observations or data from borrowers who defaulted on their loans plus observations on a large number of borrowers who have not defaulted. Statistically, estimation techniques such as logistic regression are used to create estimates of the probability of default based on this historical data. This model can be used to predict the probability of default for new clients using the same observation characteristics (e.g. age, income, house owner). The default probabilities are then scaled to a “credit score.” This score ranks clients by riskiness without explicitly identifying their probability of default.
Scorecards form the back-bone of decision making for many financial institutions. They are used in the account management of key decision areas like collections and authorizations, for example. They can tell us whether to accept or decline a customer for a particular credit-based product or tell us the percentage of a customer’s outstanding balance that will be recovered over a certain period of time.
Types of Credit Risk Scorecard Reporting
There are a number of credit scoring techniques such as hazard rate modeling, reduced form credit models, the weight of evidence models, linear or logistic regression. The primary differences involve the assumptions required about the explanatory variables and the ability to model continuous versus binary outcomes. Some of these techniques are superior to others indirectly estimating the probability of default.
A typical misnomer about credit scoring is that the only trait that matters is whether you have actually made payments on time as well as satisfied your monetary obligations in a prompt way. While a borrower’s payment background is essential, it still just composes just over one-third of the credit rating score.
Whether the borrower is a consumer or a business, we have extensive data management experience to help elevate our customers decisioning methodologies.
Our custom scorecard reporting and documentation provides detailed insight into the development process, sample populations and good vs. bad definitions, along with model descriptions and the relationship between score, population and bad rate. To meet regulatory requirements, the entire process is documented and fully auditable by a third party.
Credit Risk Scorecard Models Help Define:
- Credit limit management
- Risk assessment of credit applications
- Propensity to buy or churn
- Early alert systems
- Cross selling of additional products to the best risks in the portfolio
Benefits of Credit Risk Scorecard Reporting
In its basic form, a credit risk scorecard can be a formula in a spreadsheet. While this can be a manual process that takes time to find and input the necessary data elements, it does provide consistent feedback because the formula is adopted within an organization. Credit teams that have scorecard capabilities within their ERP or risk management solution can automate the function altogether for instant decisions. Implementing a scorecard to open new accounts serves as the foundation for automating the credit function is a key differentiator for modern, proactive credit
The two components of Scorecard Monitoring
Scorecard monitoring reports typically consist of two components: Front-end reports and Back-end reports.
Front-end refers to the filtering function of the scorecard “at the front end” – i.e. guiding the accept/decline decisions.
Back-end refers to how the scorecard performs once the applicant has been on-boarded and is now a customer.
In the case of an application scorecard, the front-end reports could allow one to see if a change in marketing strategies has caused a shift in the behavior of males and females or a certain age group (assuming of course gender and age are variables in the application scorecard) or, in the case of a behavior scorecard, if there has been a shift in the payment patterns of the population.
Scorecard monitoring reports can also be used to check the trend in the accept rate over time and to identify if there have been any significant shifts in the number of applicants that have been accepted or declined, or those that have been accepted but have chosen to decline the product.
This assessment will allow any shifts in the current population to be viewed and is usually the first point of reference in identifying whether or not a significant shift has occurred with regards to the current portfolio.