1 AVANTGARD RECEIVABLES Predictive Metrics for Debt Buyers
2 PREDICTIVE METRICS FOR DEBT BUYERS In order to be profitable, debt buyers are faced with two key challenges, first they must accurately bid on a portfolio, and then once obtained, there is an urgent requirement to prioritize collection efforts in order to find those most likely to pay. It is for this reason that many debt buyers turn to statistical modeling. Statistical models do not rely on bureau data which helps to drive down overall costs while also adhering to many regulations such as the Pinto s Ruling.
3 GETTING THE BID RIGHT; AND THE FUNDING TO BACK IT The use of statistical modeling by debt buyers, can help drive profitability, assist in identifying the optimal bid amount and enabling your due diligence process. Overbidding on a portfolio with little potential for recovery can not only result in losses, but also deter funding services from reinvesting while also draining valuable resource time that could have been used on a profitable portfolio. Knowing this information up-front is will help drive profitability. Statistical modeling is very attractive to your funding services which often require a second check as it helps them to understand the value of their investment. Further, the accuracy of your bid and the ability to perform to expectations is vital to future funding. Having a positive track record with high margins will allow you to grow your service more rapidly as you drive down costs and compete effectively in the market. REDUCE SPEND ON BUREAU DATA FOR ONGOING COLLECTIONS Most debt buyers will tell you that to access and leverage meaningful data on a regular basis can be too time consuming; hence they continue to buy bureau data in order to help prioritize collection activities. This is an expensive endeavor that does not always bring the desired results. If fact, when challenged, this data proves to be less predictive than the statistical models. Improper risk grading leads to improper prioritization in the collection process, which can lead to less profitiability. By reducing the costs spent on bureau data, debt buyers can reduce their overall operational costs while improving their results. The models that are specific to your debt type and the age of the debt are more predictive than generic pooled models without such segmentation. External data can be combined with any of the scoring models as required on a custom basis in the case where the industry solutions do not meet your business requirements. The statistical models will help you evaluate the value of the portfolio by determining which written-off accounts are likely to pay and which are likely to stay dormant. The models look at payment behavior specific to the industry debt type and they do not require external bureau data. DEBT BUYERS HAVE SPECIFIC REQUIREMENTS The Predictive Metrics advanced modeling techniques include the ability to produce two scores on all debts traditional probability that a debt will be paid in a given amount of time, while the second is unique in predicting the expected amount that will be paid. The combination of a payer score and expected value offers a more sophisticated ranking methodology to help drive competitiveness in the market by better allocating resources and targeting accounts more effectively. These techniques can be applied to any number of debt types and industries including: STRATEGICALLY TARGET ACCOUNTS FOR LEGAL The statistical models are also used for legal collections in order to optimize liquidations, reduce costs and better allocate resources in their legal strategies by rank ordering based on cost, effort and a validated expected dollar payment amount. Lawsuits are costly and time consuming. For this reason, it is imperative that some level of modeling is performed in order to determine which accounts to send to litigation. Predictive metrics predicts two outcomes providing the most profitable debt collection. The first is the traditional payer score, while the second is unique and advantageous in predicting dollars to be collected. This combination along with the extensive reporting provided, helps organizations to develop collection strategies based on cost, effort and impact, while expending collection resources and personnel more effectively. Credit Card Debt Installment Loans Direct Marketing Healthcare Telecom/Utilities DDA Medical Debt Auto Deficiencies Payday Loans B2B Trade Credit This scoring model is an empirically derived, multivariate statistical model that was developed by using payment recovery behavior specific to charged-off debt collections. The database encompasses more than 10 million observations, which are blended with socio-economic and demographic data before advanced statistical techniques are applied. No bureau data is required to produce these accurate scores. These models do not require bureau data to produce accurate scores, helping organizations minimize risk associated with permissible purpose rules.
4 Understanding how the scoring works The output of the score will help you to determine which accounts are likely to pay in the first 6 months after purchase and their expected value. Predicts two outcomes: Dollar (expected value) and Payer (probability of payment 0.01%-99.9%) versus the typical single collection score ( ) and is validated in a back test on your portfolio. No bureau data or personally identifiable information is required to produce accurate scores, helping you comply with permissible purpose rules and scores masked data. Validated predictive scoring technology is used to improve your decisioning. Advanced reporting provided to help you quantify payment collections and develop optimal collection techniques. Models are designed for different debt ages from fresh, prime, firsts, seconds, tertiary and beyond. COMPLIMENTARY VALIDATION ANALYSIS Proven results, statistical-based scoring for developing collection strategies is that statistical models are evaluated through a validation analysis that documents the model s ability to predict an incidence of payment and payment amount. Historical data is used where the result of the customer s payment activity is already known and the model s predictive ability can be accurately determined through a retrospective analysis comparing predicted with actual results. For a validation analysis, a company usually provides the modelers with 1 to 12 months of aged historical placement data, that has been placed typically six months or more. This is a two pronged approach and two files sent simultaneously will be needed to process your validation. The first file is the original account data file for collection (billing) and the second file is the performance information (i.e. payments, dates of payments, type of payments) during the validation period (i.e. at least six months of transactions, payments, after placement or purchase). The first part of our retro process involves us scoring your purchased/placed accounts file. The second part of the analysis, we attach your performance data (payments / transactions) associated with the accounts over at least a 180 day period and produce a summary showing our predictions versus your actual results. This allows the model s predictive ability to be accurately evaluated. The validation process can also evaluate the output of these models with other scores currently in place. This will offer the ability to apply a Champion Challenger Analysis to evaluate how SunGard s models perform against current solutions. FEATURES Predict two outcomes: - Expected payment amount - Propensity to pay Ability to combine internal data with external variables Allows you to score pre-collects, fresh, firsts, seconds, tertiary and beyond. Specific models for - Medical/Healthcare, Credit Card, Telecom, Utility, B2B, B2C Complimentary validation process BENEFITS Accurately bid on portfolios Improve Funding Increase competitiveness and profitability Improve bid accuracy for debt portfolios Decrease operational costs by strategically allocating resources Ability to conduct simulations of expected liquidations based on costs and collection efforts Reduce legal fees by gaining visibility to the predictive outcome
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