Collections operations and collections analytics departments live and breathe by a report often called the delinquency movement matrix (DMM). The DMM graphic below illustrates roll-rate behavior, which is the probability of a loan to roll from one delinquency level to another within a specified amount of time.
This DMM report, while simple in nature, helps the collections organization to prioritize efforts and costs. Organizations do not need waste operational expense on segments of the population with a high probability of rolling 30 days delinquency, if the population also has a similarly high probability of self-curing. Collection efforts and expense are best spent where they are most beneficial, to target accounts that will roll delinquent but are unlikely to rehabilitate without intervention from collectors.
Using DMM is especially important when the risk appetite of the organization increases or when the behavior of the delinquent population is not standard or continually changes over time.
DMM is a critical tool not only for collection departments but also useful during the custom scorecard development process. Using DMM, custom scorecards are targeted to predict where the loan population is more likely to charge-off than rehabilitate. Portfolios with very high self-cure rates or high performing collections departments, who increase the natural cure behavior, can benefit greatly from a custom scorecard that matches their delinquency profile. The industry's most trusted analytics providers help lenders create a custom scorecard to optimize lending into lower risk segments that are not easily identified using a generic scorecard.
When defining the performance metric during the development of a custom scorecard, analysts will measure the payment behavior from loan origination through at least 36 months from origination. This analysis is used to identify the percentage of loans that reach varying levels of delinquency and what occurs after the loan is delinquent.
Below, we illustrate an example using the roll-rate analysis from 12 months to 18 months after origination and from 18 months to 24 months after origination.
In this example, loans have a high rate of curing after reaching 60 or 90 days delinquency between months 12 and 18. For this population, 60 to 90 days delinquent within 12 months would not be the optimal behavior for a risk score to be calibrated. For this same population, there is a significantly lower probability of curing from 90 days delinquent between months 18 and 24. A custom origination scorecard for this population may be calibrated to predict the likelihood of going 90 days delinquent in the first 18 months.
While a generic scorecard may predict a consumer’s likelihood to go 60 days delinquent, the roll-rate analysis in this example shows that a consumer who reaches 60 days delinquent has a relatively low-loss likelihood and a high probability of curing. In this example, the generic scorecard could overestimate the risk of applicants.
A custom risk score helps a lender make more profitable decisions. Custom scorecards are tailored to optimally identify the customer profiles with low expected losses and high return. CRIF Achieve can help turn acquisition and performance data into business intelligence for banks, credit unions, and finance companies. Built with proven technology and expertise, CRIF Achieve’s analytic decision management infrastructure enables us to design affordable, customized solutions for our clients that are:
- Multi-dimensional to manage every category of risk financial institutions face today
- Scalable to provide the analytics needed today and to lay the foundation for the analytics of tomorrow
- Expandable across the credit cycle and lending functions
- Agile for rapid return on decision making investments
To learn more about how CRIF Achieve can help any institution leverage analytics for greater lending success, including custom scorecards, please click the button below to request our complimentary eBook.
Photo Credit: Ian Sane