26 September 2016

44975

Retired Member

2,009Posts 6,489,614Views 2,284Comments
Finextra community

Treasury Management

This network brings together treasury and financial professionals who manage treasury functions. Members share a common interest in treasury, cash management, banking, risk management and investments.

Risk-based Collections: Credit Evaluation for New Applicants

27 April 2012  |  3481 views  |  0

In Part I of my Risk-based Collections blog series, I described the differences between Judgmental and Statistical Scoring models.  In Part II, I discussed which scores are best for managing existing credit lines.  In Part III, I will describe which scores are best for new application credit risk evaluation.

One of the best uses of generic scores and credit bureau data is to help you review new applicants for credit. The bureaus have reports that are specifically designed to help you review new applicants. In most cases, you will likely only provide a small line of credit to a new account until you have been doing business with them for a significant period of time and are happy with their payment pattern. Then you’ll have to protect yourself against the possibility of the account’s overall credit deterioration, over time, which often translates to purchasing more credit bureau information in the future.

Essentially, deciding what type of credit evaluation to use with new applicants is a function of the volume of new applicants. The following depicts a set of guidelines often adopted by best practices companies:

  • Low number of applicants – less than 50 a month and low dollars: Use bureau credit reports or scores
  • Low number of applicants – less than 50 a month and high dollars: Use bureau credit reports supplemented by financial statements and trade/bank references
  • Medium number of applicants – between 50 and 500 a month: Use a judgmental rules-based system and financial statement data, if high dollars. Additionally, generic scores and bureau reports can be used to supplement and/or verify your decision
  • High number of applicants – over 500 a month: Use a custom statistical-based credit score enhanced with bureau data that is designed for evaluating new accounts

A custom statistical-based model for new applicants is typically only relevant if you have a significant volume of new business each month, this is estimated at greater than 500 new accounts. You will have to purchase historical credit bureau data for your new applicant model because you don’t have any experience with the new customer, but the statistical-based model can help you set a more applicable credit line for the new customer and, therefore, may account for some additional business when compared to the lower credit line a bureau generic model will support.

Additionally, you’ll save substantial money on future credit bureau data purchases because you will only buy the data that is required to produce the model’s scores. Also, if you are using a statistical-based new account model, you will most likely have a statistical-based model in place for evaluating existing accounts, and once you have been doing business with the new applicant for a few months you’ll be able to use your existing account model to evaluate the customer, and will not need additional credit bureau data. Your own data can be used to evaluate the future risk of that account.

What type of credit evaluation do you use for new applicants? I’d like to hear from you.

TagsRisk & regulation

Comments: (0)

Comment on this story (membership required)

Latest posts from Retired

Modelling fixed income: Why realtime analytics are key

29 July 2016  |  5136 views  |  0 comments | recomends Recommends 0 TagsPost-trade & ops

Who is looking after your cash?

22 June 2016  |  3175 views  |  0 comments | recomends Recommends 0

Content Marketing to promote your App

16 May 2016  |  6378 views  |  0 comments | recomends Recommends 1 TagsMobile & online

Crypto-Finance will transform financial services!

11 May 2016  |  2655 views  |  0 comments | recomends Recommends 4 TagsBlockchainPayments

Retired's profile

job title
location
member since 2014
Summary profile See full profile »

Retired's expertise

What Retired reads
Retired writes about

Who's commenting on Retired's posts

Graham Seel
Ketharaman Swaminathan
Gerard Hergenroeder
Konstantin Rabin
Matt Schofield
Anna Robert
Ian Davis
Steve Patel
Aparty Behera
Karim Maalouf