Credit Management meets computer automation
A necessary balancing act. A risky business
Shortly after Easter, a newly installed computer system sent out red letters to over 3,000 Derby City residents who had already paid their Council Tax. There were howls of outrage, and an obligatory computer error excuse from the Council. Matters were then compounded as angry residents tried contacting the Council to put the matter right - and found themselves blocked by an automated call system.
Michael Aubrey, from Derby, was threatened with a demand for immediate payment of over £1300 if he did not sort matters out within a week. However, he said: "I rang all the numbers on the form and all you get through to is an automated payment line, where you can't talk to anybody and say I've already paid."
This is the downside of computer automation in one of the most sensitive areas of commercial interaction: credit management. Unless we pay for everything in cash or up-front, it is an issue that will affect us all at some point in our lives.
Like so much else in business, credit management is a balancing act. At one extreme - mostly in the retail sector - are businesses that do not give credit at all. They minimise the possibility of incurring bad debt - but in so doing, they probably fail to maximise store turnover, losing purchases from those who do not have the money to pay right away.
At the other end are those business that exist solely on credit: They provide goods and services before any monies are received. And therefore their success is wholly dependent on their ability to manage the associated credit risk. Many businesses, of course, fall between these two extremes.
According to Neil Monroe, External Affairs Director at UK Credit Referencing, Equifax, best practice relies on implementing a traditional view of the consumer lifecycle. That is, businesses need to balance risk and profitability, while recognising there is no absolutely right answer: What works for one may not work for another.
Best practice also includes segmenting prospects before they become customers - and then managing customers on an individual (or segment) basis throughout their time with an organisation. Thus, some thought needs to be given as to whether an individual is going to be a "good customer" at the point of recruitment - not six months after they have become a customer and owe hundreds of pounds that they are unlikely ever to repay.
The level of credit granted should reflect the same factors, as should the approach to individuals whose accounts have started to pass into delinquency. Some of those who are presently not paying can pay, but won't: Some simply can't, but with a little help and careful management could revert to being model customers in future.
Businesses need to recruit "goods" - individuals who spend a lot and pay on time - and reject "bads." Ultimate success or failure depends on how they manage those who sit between these two extremes - the "greys" - who may spend a lot but be highly risky in terms of payment or pay mostly on time, but spend very little, so ultimately wiping out any profit margin through admin costs.
A major issue that el Reg has encountered is that businesses do not always succeed in applying such an integrated approach. The Marketing Department see their task as bringing in new customers, more orders, no matter what: The Credit Section see their function as safeguarding the bottom line. In essence, they are assessing customers according to two different sets of criteria - and in the worst cases, creating serious systemic problems for their organisation.
In one hi-tech company, this problem was so acute that canny customers eventually worked out that the quickest way to obtain 6 months free subscription was to default on a payment - because marketing would do anything in their power to prevent the customer count from reducing.
The date beneath
For businesses looking for the optimum approach, Equifax is one of the two main UK organisations (the other is Experian) who offer a range of support tools. First and most important is the underlying data. Since legal changes in 2002, organisations may no longer use the full electoral roll and must restrict marketing activity to individuals who have not opted out (currently around 60% of the roll). However, the entire roll is available for credit checking purposes - and it is used, in conjunction with data pooled from a large number of independent organisations in respect of individual payment history, to provide credit screening services against potential new customers.
It is important to distinguish screening services from predictive modelling. The former focus on how well individuals have dealt with credit in the past and takes the (reasonably) rational view that those who have incurred County Court Judgments or failed to pay bills on time might tend to do the same again in future.
Predictive services combine public data with other data held in-house by businesses to come up with a view as to how likely an individual is to be a future credit risk. This difference is at the root of many mass credit card mailings - and the cause of a certain amount of consumer anger. Mailshots go out explaining that an individual has been pre-screened for a particular card: All they have to do is fill out a form to accept. And when they do, their application is then rejected.
What is happening here is that the individual is a good risk on the basis of past data: But once more detailed information is provided, they do not fit the profile that the credit card operator is looking to recruit. This is also the root of a great deal of angst in face-to-face dealings: an individual turned down for credit may have little or no adverse history in their past at all. Rather, a statistical model may simply have said that whilst the overall likelihood of anyone defaulting is 0.1%, the risk relative to that individual is 0.2%. In relative terms, double the risk: In absolute terms, almost certainly a good risk.
Companies such as Equifax offer generic segmentation models, which sub-divide the UK population into groups constructed using a range of variables from the census and other sources: They also offer statistical modelling, to create the predictive scoring outlined above. A range of techniques may be used - although logistic regression and CHAID analysis are two of the most popular.
Matters have been shaken up by the recession. Red lining - the practice of simply refusing to offer certain products on the basis of where people live - has long been outlawed by the Office of Fair Trading (OFT). However, there is an ongoing debate around issues of financial exclusion: The fact that it is often the poorest in our society who pay most for credit. Government - and in particular the Department of Business and Enterprise (BERR) - is concerned with how business grants credit: a Consultation on the implementation of the consumer credit directive is under way. And there has been much focus on what are considered to be bad or irresponsible credit handling habits.
One danger of tightening up credit controls now is that as we emerge from recession, the number of individuals categorised as "greys" by financial and other concerns will be much enlarged. Many individuals will have blotted their credit copybook - and the industry will need some latitude in order to bring these individuals back to the fold.
Philip King, Director General of the Institute of Credit Management, is well aware of this issue. Talking to The Register, he said: "The reality is that with less funds available from banks, the solution for many businesses is to beef up the flow of cash into the business.
"Some businesses are being draconian, reacting - or over-reacting - to present circumstances. Smart businesses are looking at their customer portfolio, working out who pays regularly and well, whilst looking more closely at those who represent a higher degree of risk.
"The best approach may be to reduce payment terms, whilst giving something in return: Simply demanding faster payment, mismanaging the relationship in any way, may store up problems for the future, when the economy picks up again."
At base, he recommends against businesses just getting tougher. Best practice, he observes, is about segmenting and dealing with individuals. He adds: "Automated systems are a good way of driving activity, but they should never overtake thought. There will always be a need for rational judgment."
Additional tips on how to manage cash flow and credit - possibly more relevant to the B2B environment - can be obtained from a series of guides put together by the ICM and BERR.
The automation challenge
All of which brings us full circle to automated systems. It doesn't help when the process - as at Derby Council - is flawed. However, computer automation can impact quite negatively on the customer relationship if the process automated is, itself, ill thought-out.
We spoke to nPower, who are the UK's fourth largest supplier of power and have occasionally found themselves hauled across the coals for what is seen as insensitive treatment of their customers.
Their current approach perfectly illustrates the issues faced by major companies seeking to manage credit on a more intelligent, customer-focused basis. First, they have an issue with customers who are potentially risky - not least those fleeing another supplier with sums still outstanding for electricity and gas supplied. Because they provide a commodity that is seen as a necessity, they are less able than some companies to turn down customers: Providing an individual's pre-existing debt does not exceed £200, they are not allowed to reject them.
However, they are obliged in some circumstances - such as where an individual lives in a property managed by the National Housing Association - to insist that they use a pre-pay meter. This, until recently, opened nPower and other suppliers to accusations that they were penalising their poorest customers. But in the last year, despite the fact that pre-pay costs more to provide, the tariff has been equalised to that for standard quarterly payments.
Customers who pay by direct debit are still better off than those who pay on a regular basis - although the benefit of this approach is not quite as clear-cut as might be presumed. Direct Debits are set to a level that should match overall usage of power during a year: At some points in the year, customers may be in significant debt to nPower, which means that the supplier is effectively subsidising customer tariffs.
At the end of each year, historic usage is compared to what the direct debit is generating and, in some cases, a fairly major adjustment may be necessary. This is because, where adjustment is necessary, there will almost always be two components to that adjustment.
First, there will be an adjustment to the regular payment. And second, an adjustment designed to make up for under-payment in the previous time period. If someone has underpaid by, say, £600 in a year, they will need to adjust their direct debit to generate an additional £1200 in the next year. Combine this with price rises and it is hardly surprising that there is a public perception - adamantly rejected by nPower - that direct debit is used as a means to extract additional value out of customers. nPower may be correct in their assessment of the arithmetic of this issue - but they should guard against leaving such sensitive changes purely to automated systems.
Then there is the reminder notice. Some years back, chasing of unpaid utility bills was a very laid back affair, with first reminders sometimes appearing a month after the due date for payment. Now, a first reminder may be as little as ten working days after the due date for payment, with red letters appearing a week after that.
The tone of such letters can be quite distressing - particularly to pensioners brought up to a gentler regime - and the insult is compounded where the system is in error, or payment has crossed with the reminder. nPower recorded messages inform customers that it can take up to ten days for a payment to clear their system - which begs the question of how much damage they are doing to customer relations by reaching for the threatening letter so quickly.
A spokesman for nPower told us that there are around nine levels of reminder before a serious intervention. He added: "on every occasion nPower would prefer not to intervene and would prefer some customer contact and arrangement rather than action."
nPower also support community initiatives, helping individuals locate benefits that can help them with payments and nudging people towards easier payment schemes.
Whilst the current regime is far from the best practice proposed by the ICM, this should be changing soon. New systems due to go live in the next 12 to 18 months will give nPower the ability to implant more sophisticated credit modelling techniques within their follow-up. In theory, they will be able to identify those likely to be fearful when sent a debt letter and take a gentler approach with them: Also, they should be able to identify those most at risk of defaulting and tailor their action, both message and medium, to the target group.
In the long run, all this may be irrelevant, as experiments are currently under way with smart meters which will make it economical to introduce pay-as-you-go for the majority of their customer base. In theory, the meters will be so smart that, if unusual power usage patterns are detected, they will be able to generate helpful messages to the property owner - like "have you left your lights on?" - via mobile or online.
At the end of the day, credit management remains a very difficult area. It is about finding balance between value and risk in the customer: At the same time, it requires treating customers appropriately, neither so lightly that bad habits set in, nor so harshly that they look for alternatives. In this respect, predictive modelling combined with computer automation can be either blessing or curse. Done well, it deepens relationships and safeguards the bottom line. Done Badly, it will almost certainly achieve the opposite effect. ®