Probability to Profit: Using Risk-based Collections to Quantify Future Cash Flow

Cash flow forecasting has never been more important. It’s no secret in today’s market that for a company to maintain a healthy cash flow, it needs to be doing business with equally healthy customers. Applying the correct approaches to assessing a customer’s financial health, and the risk associated with their business, has long been at the forefront of everyone’s mind. Whether it be foreign exchange (FX) risk, counterparty risk, interest rate, market or credit risk, assessing all of these measures of risk is of paramount importance. 

However, when it comes to assessing the creditworthiness of a customer, traditional risk management fails to consider cash at risk (CAR), or the risk tied up in one of your biggest assets, the trade receivables portfolio. Outstanding balances from the accounts receivables (A/R) process typically account for the largest source of potential funds available to any company. Hence monitoring the risk of if and when that cash will come in, not just today but in the future, is crucial to accurately quantifying your company’s cash flow and, in turn, your ability to invest and borrow against it.

A study commissioned last year by SunGard found that while 34% of survey participants reported using their receivables as part of their overall capital structure, 81% were not performing any kind of risk analysis on the entire portfolio on a monthly basis. This lack of control and understanding can impact the decisions you and your organisation make on how to most effectively manage this cash.

“We have developed a risk strategy whereby we score our portfolios quarterly, but only about 50% of our locations are doing it at the current time.” says Bill Uhrich, director of corporate credit at Dresser GE Energy.

Risk-based Collections

There is a multitude of ways to apply risk-based collections. Given the vast differences in the customers that have open credit lines with the company, any or all of them may apply to one company’s trade receivable portfolio. The differences in these customers’ ability to pay, their credit rating based on agency data, their industry, their geographic location, the invoice value, age of balance, their remaining credit limit, their ability to use alternative methods of payment, their historical payment behaviour and the commercial risk involved (the risk of losing the customer), must all be analysed to ascertain how to reap the highest return on the investment the company has made by extending payment terms to them.

However, perhaps the single biggest factor in determining how best to use a specific receivable is the probability of payment. Assigning a probability of late payment, or of loss that utilises all the other factors mentioned, provides an organisation with the best information with which to segment and treat receivables. If a company can classify their receivables into groups of customers – those with a high probability of on-time payment versus those that will be delinquent or who have a high probability of loss – they will be able to apply specific treatments to each of those segments of the portfolio that will result in generating the most liquidity for the least cost.

The ability to quantify the probability of delinquency for all receivables makes it possible to develop the necessary financial strategies with which to make informed business decisions. Essentially, companies need to be performing behavioural analysis on their trade receivables to measure the statistical probability of customers paying on time. Statistical modelling allows them to view their receivables and sales forecasts with a critical eye and, by determining the customer’s propensity to pay and when they pay, companies can deliver more accurate cash forecasts.

Technology Lends a Hand

A recent survey conducted by analyst Aite Group confirms that risk analysis forms a key component of accurate cash forecasting and that the role of the treasurer is evolving to become increasingly more strategic. The ability to provide more predictive and intelligent information is an increasing demand and one where sophisticated technological tools may be required. However, the study also showed that if data from the A/R portfolio is indeed being used to help quantify future cash flow, by far the majority still do it on a manual basis.

SunGard’s Predictive Metrics, developed in 1995 to provide automated analytics and predictive credit scoring for business-to-business (B2B) and business-to-consumer (B2C) businesses, uses a company’s own internal A/R data to create statistical portfolio scoring to quantify specific risk probabilities on accounts. This capability distinguishes it from generic credit agency data or in-house judgmental-based scoring, which is formed purely on varying individuals’ experiences and opinions. The scores produced as the product of statistical-based scoring essentially provide a measure of the risk that a given customer will pay their bill on time.

The standard output from a statistical-based scoring system includes not only a credit score, but also the probability that the account will go bad – probability of bad (PBAD) – within a specified period from the scoring date, usually six months, and a cash value estimate of the account that is at risk. These values, when properly applied, assist in allocating collection resources to specific accounts such that the return on investment (ROI) from collection operations will be maximised. In simple terms, the statistical modelling leverages historical payment data and A/R files in order to predict the probability, measured as a percentage, of a specific customer becoming delinquent at some point during the six months after the score date. This information can then be used to drive collection strategies, improve credit decisions and quantify future cash flow.

Leveraging Risk Score to Quantify Future Cash Flow

By knowing and using the probability of the occurrence of specific credit and collection events, it is possible to optimise the allocation of available resources in a given credit and collections environment. From this, strategies can be developed that mitigate the possibility of negative results, while simultaneously increasing the credit lines of low risk accounts and providing the opportunity for additional revenues. For example, where a particular collection strategy based on a more judgmental model may not have been working well, it is extremely difficult to determine which factors and weights need adjustment. However, with a statistical risk-based score it is a straightforward process to determine which variables are causing the problem and the information is used to drive a different approach and adjust the strategy accordingly. As a result of better collections prioritisation, days sales outstanding (DSO) and bad debt is reduced and cash flow is increased.

The prediction and probability of current and future payment behaviour can be used to help identify opportunities to grow the value of the perpetual asset that is the trade receivables portfolio and, in turn, impact future cash flows. Another key element often overlooked is foreign receivables. Often treated differently in a capital structure, foreign receivables represent an increased risk and this should be factored into the cash forecasts. Overlaid with the sales forecast, the detail around trade receivables help in achieving a more accurate view.

“Having moved from a more judgmental approach to portfolio scoring, part for collections and part for credit, we now score a large portion of our receivables,” reports Steve Strong, global credit risk manager for Google. “We have increased the frequency of our credit scoring to almost daily and we have risk scores on the lowest risk customers on demand at any time. We use this for supplier risk to look at their credit and even real-estate and sub leasing deals, and then can incorporate the information into other areas such as cash flow forecasting.”


Performing risk-based statistical scoring should be an integral part of any collections practice and one that should always form a key component of the cash flow forecasting process. Whether it is a need to drive and predict free cash flow, an interest in mitigating bad debt or even a desire to reduce counterparty risk around suppliers, treasurers are increasingly required to take a bigger interest in trade receivables. This is a developing story and the more that treasury becomes involved, the greater the integration between these functions and the more effectively companies will be able to quantify future cash flow, in turn contemplating their receivables for borrowing and securitisation and ultimately improving their overall financial health.



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