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In developed markets ‘comprehensive’ credit bureaus are common place; that is credit bureaus which store information relating to all of an individual’s past repayments, not just information relating to their defaults. Although there are some exceptions most borrowers, lenders and regulators in these markets believe that a trusted third-party holding a database of good and bad financial history provides borrowers with better products and at a fairer price.

But most markets don’t start at this point. In markets where credit bureaus are new or non-existent it is often difficult for regulators and borrowers to know how to decide between a positive bureau and a negative bureau. In many cases the costs of a positive bureau are relatively well understood – both the physical costs of development and the societal costs in the form of privacy concerns – while its benefits remain underestimated: usually assumed to be only those benefits accruing to lenders through better risk control. As a result, the initial push is often for a ‘negative data only’ bureau.

But a positive bureau also carries significant benefits to borrowers and I will use an applicable, albeit in the inverse, example to explain how and why this can is the case.

In Formula 1 motor-racing points are awarded to the ten best drivers in each race and accumulated over the season to identify an overall winner. The goal of this approach is to identify the ‘best’ driver in a given year and it serves this purpose well – sorting out the very best from the just very good. Since most stakeholders in Formula 1 – team owners, sponsors, drivers and spectators – are concerned almost exclusively with knowing which individual is the ‘best’ this system seldom comes under serious criticism.

But that doesn’t mean that it suits all purposes equally well. Imagine you are placed in charge of a new Formula 1 team that has started with a very limited budget. The team owners realize that this small budget effectively precludes the possibility of winning the title in the short-term but they also understand that if they can survive in the sport for two years they can gain a bigger sponsor and fund a more serious title challenge thereafter. So their goal it to survive for two years and to do that they need to maximize the exposure they provide their advertisers by finishing as many races as possible as far from the back as possible.

What the budget means for you as the team manager is that you can only afford to hire cheap drivers which we’ll assume means drivers who finished in the bottom ten places in the previous season. From that group you’ll still want to get the two best drivers possible but how will you identify the ‘best’ drivers in that group? The table below shows the driver standings at the end of the 2010 season:

As the table above shows, the present system is so focused on segmenting drivers at the top of the table that it struggles to differentiate drivers towards the bottom; in fact the bottom six drivers all finished with zero points. 
Vitantonio Liuzzi might look like a clear choice in more than double the next driver tally but is he really the best option and who would you choose to join him? A new model is needed for your purpose, one that separates the ‘worst’ from the simply ‘bad’.

Negative bureaus have a similarly one-sided focus, a focus that might have fits their initial purpose but that limits their use in other situations. A negative bureau only stores information on customer defaults; helping to separate the highest risk customers from the less high risk customers but struggling to segment low risk customers. Information is only created when a payment is missed – and usually only when it is missed for several months – and so an individual with a long history of timeously repaying multiple debts will be seen as the same risk as a customer who has only paid back one small debt, for example.

Returning now to the earlier scenario: the current Formula 1 model awards points for finishing in one of the top 10 places using a sliding scale of 25; 18; 15; 12; 10; 8; 6; 4; 2; 1. A model better suited for your new purpose should still retain the information relating to good performances but should also seek to create and store information relating to bad performances. The simplest way to do this would be to penalize drivers for finishing in the last ten places using the same scale but in reverse.

Implementing these simple changes across the 2010 season immediately provides more insight into the relative performance of drivers towards the bottom of the standings and in so doing the gap between each driver becomes clearer and useful information is been created. 

Although Vitantonio Liuzzi had previously looked like an obvious pick his good performances – a 6th place in Korea and a 7th place in Australia – were overshadowed by his many more poor performances – last place in Abu Dhabi, Brazil and Singapore and second-last place in Japan and China. When the whole picture is seen together, he is no longer such an attractive prospect. A better bet would be to approach Jaime Alguersuari who, although he never placed better than 9th finished in 13th place or better in 80% of his races and only came last once. Sebastien Buemi also finished last on only one occasion though he spread his remaining results more unevenly with both more top ten and more bottom ten finishes than Jaime.
Both of these drivers would offer theoretically better returns having placed worse on the accepted scale but with more of the sort of results you’re team is looking for.
Of course this isn’t the perfect model and real Formula 1 fans might take exception but it does illustrate how creating a holistic view of the relative performance of all drivers, not just the very good ones, can be value-adding. 

Similarly, a negative-only bureau suits the simple purpose of identifying the very worst of your potential customers but it struggles to identify good customers or to segment the ‘middle’ customers by relative risk; users of the bureau that wish to merely avoid the worst borrowers are well served by this information but lenders who wish to target the best customer segments for low risk/ low margin products or who wish to match pricing to risk are unable to do so. 
The societal costs of a negative only bureau are therefore born by the best performing borrowers in that market who are given the same products at the same price as average risk borrowers. 
A comprehensive positive and negative bureau avoids this societal cost though it usually does so with added build and maintenance costs. 

When deciding which bureau is best for a given market then, borrowers and regulators should focus on the trade-off between the borrowers privacy concerns and the borrowers access to fair products at a fair price while lenders should focus on the trade-off between the cost of a comprehensive bureau – passed onto them in the form of higher bureau fees – and the expected benefits to be achieved through more profitable niche products.

* * * 

The fact that the 2011 season has just finished stands testament to how long this article has sat in draft form, awaiting publishing. However, the big delay does at least afford me add an addendum on the performance of the proposed model.
Of course far too many factors are at play to make a scientific comparison, not least the fact that Vitantonio Liuzzi, the man our model told us not to pick, changed teams but here goes anyway:
Vitantonio Liuzzi didn’t qualify for one race, retired from 5 and ended the season without a top 10 finish and only 7 finishes within the top 20. In all, he didn’t manage to collect a single point and joined seven other drivers in joint last place. 
Both models suggested Sebastien Buemi and he also finished the season placed 15th with 7 top ten finishes against five retirements and no finishes outside of the top 15. While Jaime Alguersuari, our model’s wildcard pick, finished one spot better on the overall standings with 5 top ten places, 3 retirements and only one finish outside of the top 20.
Never shy to identify a trend from two data points, I’d call that a 2-1 win to the comprehensive model

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