Custom Solutions ⇒ Collections

Collections

Collection Scoring - Collect more by prioritizing early stage collection activities


Risk Based Collection Strategy - Identify and measure superior collection techniques

Collection Scoring Models

With the typical credit department spending 90% of their time managing the day to day relationship with their customers and trying to collect money, the largest growth area for the use of credit scoring is in collections. PMI develops cost effective Collection Scoring Models to help collection departments facilitate the prioritization of collection activities using risk in conjunction with aging as the primary driver of a Risk-Based Collection Strategy. By implementing strategies using collection scoring our clients have seen a dramatic reduction in DSO (Days Sales Outstanding) as well as lower operating costs, reduced write-offs, and have been able to better estimate their bad debt reserves.

The accuracy of our collection models is they are based primarily on your company’s internal A/R and collection performance data, which has proven to be the most predictive data in this type of model. And it’s free and readily available. It has been mathematically proven that a collection model built using internal data only is a much more powerful predictor of future delinquency than a collection model built using only external data.

PMI Collection Scoring Models are empirically derived, multivariate, (based upon many different data elements) statistical models that quickly and accurately evaluate the probability that a customer will become severely delinquent, written-off, sent to a collection agency, or go to loss over the next six months. Our statistical analysis of your internal AR data chooses and optimally weights the most predictive data elements in the model. To ensure model predictiveness, we validate the model on your portfolio using your historical AR data. Leveraging actual portfolio performance provides a true risk assessment of the entire portfolio, as well as on an individual customer level, allowing you to make risk based decisions.

The output of the model is a score that can be used by itself to arrive at a decision or can be combined with other information to determine an existing customer’s collection strategy. A collection score is often supplemented with your collection manager’s policy rules to create a more comprehensive decision system. The model is easily implemented in many different environments including our hosted web based application ScoreMiner as well as through encrypted Internet FTP data transfer.

Benefits of Collection Scoring Models

• Improve prioritization of internal collections activities
• Decrease DSO, delinquencies and write-offs
• Improve efficiency and productivity of credit and collection staff
• Reduce collection time and costs
• Early warning signal

MORE INFORMATION ON COLLECTION SCORING MODELS


BACK TO TOP

Risk Based Collection Strategy

If your company only has one internal collector for every 1,500 customers, how does the collector determine which customers to focus on, what type of treatment should be used for a given customer, and when to apply that treatment? The majority of collectors prioritize collections based on aging, the customer who owes the most money, for the longest period of time, has the highest prioritization in the collection process. However when collectors focus on high dollar accounts under the aging method, many times high risk, low dollar accounts are ignored.

Industry analysis has proven that using only the aging method to prioritize collections activities often causes the wrong treatment to be given to the wrong customer at the wrong time. PredictiveMetrics (PMI) has developed a methodology using a statistically derived credit score based on your internal A/R data, data that is proven to be the most predictive, to determine customer risk. The risk level is used rather than aging as the primary driver of a collection strategy for the on-going management of the customer. PMI groups accounts into risk groups based on their credit score to help you differentiate which customers you need to focus on regardless of the amount owed or the age of the delinquency.

Steps to Developing a Winning Collection Strategy

1. Develop a set of different collection strategies, ranging from mild to very aggressive.
2. Score all the accounts using a statistical-based scoring system and segregate them in to risk class based on their score
3. Select a random sample of accounts within each risk class.
4. Determine a collection statistic that can be used to evaluate the performance of the various collection strategies (i.e., DSO).
5. Divide the sample in each risk class into the same number of groups as the number of collection strategies to be tested.
6. For each group, in each risk class, apply one of the collection strategies.
7. 120 days after beginning the test, re-compute the DSO for each group, in each risk class, and compare it to the DSO computed for that group’s risk class at the beginning of the test.
8. In any risk class where an alternative collection procedure has produced a favorable change in DSO, the collection strategy should be changed.

A Risk Based Collection Strategy ensures ongoing cash flow streams resulting in a reduction of days sales outstanding (DSO), bad debts, and write-off’s. Implementing strategic plans that are based on statistical credit scoring using internal A/R data to more efficiently manage inherent customer risk, internal resources and the performance of the receivable portfolio pays off.

MORE INFORMATION ON RISK BASED COLLECTION STRATEGY


BACK TO TOP