Self-assembled monolayers as a way of nanofabrication

According to Barron and Staten (2003:11), ‘broader access to credit markets is widely rec-ognized as the consequence of four simultaneous and interdependent factors’, which relate to access to customer data, better and cheaper data processing, the use of statistical tech-niques for risk assessment, and changes to interest rate ceilings that make risk-based pricing more feasible. These should be restated along two dimensions: (i) decision components—

data, risk assessment, decision-making, and delivery; (ii) change areas—practices, technol-ogy, and regulation. These are illustrated in Table 1.4, where the cells provide key factors or examples.

As can be seen, credit scoring is a practice that has been adopted for automating credit risk assessment. What are not so evident, however, are the interconnections. It is difficult to ascribe the accelerating growth of credit to any one factor. It can, however, probably be safely said that—low interest rates and a benign economy aside—the forces driving credit growth have been those relating to data and automation, with improved risk assessment and an empower-ing legal environment as supportempower-ing factors. The followempower-ing sections do not cover all of the above, but look at:

Data—Increasing amounts of information, resulting from automation, data sharing, and empowering data privacy legislation.

Risk assessment—Use of credit scoring to drive transactional lending, as opposed to the relationship lending of old.

Decision-making—Lenders are no longer limited to the accept/reject decision, but can also use scores to set prices, and value portfolios for securitisation.

Process automation—Evolving technology that has made computers—including processors, data storage, and networks—faster, smaller, and cheaper.

Legislation—Fair-lending legislation and Basel II have promoted the use of credit scoring, while data privacy legislation and practices allow the sharing of data.

Data

A phenomenon that started during the latter half of the twentieth century was the growing power and sophistication of computers, along with improved capabilities for gathering, pro-cessing, storing, analysing, and communicating data. All of this led Varian (1998), to propose a Malthusian view of information.

In 1798, Thomas Malthus anonymously published his work, ‘Essay on the Principle of Population’, in which he proposed that at the then rapid rates of population growth in Europe, available food and other resources would soon be depleted. Although his prophecy was incor-rect, Malthus was the first to highlight the relationship between overpopulation and misery.

Population growth was geometric (doubling every 25 years in Europe at that time), while increases in subsistence (food production) were linear. Resources would be depleted unless population was checked by famine, war, pestilence, or birth control. Malthus’s theories on the political economy were highly influential, and heavily influenced economic theory and social doctrine in the nineteenth century, as well as England’s 1834 Poor Law Amendment Act.

Interestingly, Malthus’s ideas also found their way into Charles Dickens’ portrayal of Ebenezer Scrooge in A Chirstmas Carol, where reference is made to ‘the surplus population’.

Varian proposed that in modern information economies the growth in data is geometric, while the increase in consumption is linear (‘Malthus’s law of information’):

This is ultimately due to the fact that our mental powers and time available to process information is con-strained. This has the uncomfortable consequence that the fraction of the information produced that is actu-ally consumed is asymptoting towards zero.

While Varian does have a point regarding individuals’ ability to process information, he made the same mistake as Malthus. Malthus failed to consider changes to farming techniques that boosted food production, while Varian disregards tools used to get greater value out of data.

This applies particularly to credit scoring, and the computer systems that are used to assemble, assess, decide, and deliver.

Varian (1998) also proposed the equivalent of ‘Gresham’s Law’ for information. Sir Thomas Gresham was an English merchant financier during the Tudor era, who

22 Module A : Setting the scene

Table 1.4. Credit growth drivers

Decision component Change area

Practices Automation Regulation

Data Sharing Collection Privacy

Risk assessment Credit scoring Calculation Fair credit Decision-making Risk-based pricing Decision agents Rate ceilings

Securitisation

Delivery Cross-sales ATM, Internet Distance lending

contended that bad money drives out good money—referring specifically to commodity currencies like those using gold or silver, whose value may be debased by changing the alloy or shaving the rims. In like fashion, Varian proposed that bad information can drive out good information, meaning that information that is both low cost and low qual-ity can force out high-qualqual-ity information—note Microsoft Encarta versus Encyclopaedia Britannica.

Risk assessment

In the early days, credit underwriters reviewed the applicant’s financial and other details man-ually, but credit scoring provided a tool to condense information into a single number. This has been an incredible tool for empowering lenders to make decisions, and has provided them with much greater control over the business. Figure 1.2 is meant to illustrate the development of a scorecard using historical information, both on observed characteristics and subsequent outcomes, and the model’s subsequent use as part of a business process.

The primary goal is to provide a tool for ranking accounts according to the relative odds of them being ‘good’ or ‘bad’ at the end of the period, where good is the desired outcome, and bad is to be avoided. The major assumption is that future will be like the past, or at least sufficient enough for the models to provide value. Unfortunately, credit scoring: (i) is highly backward-looking and not able to provide a forward view; (ii) is unable to assess exogenous data (not provided as part of the system); and (iii) is not well suited for assessing rare but severe events.

As a result, credit scoring is not a law unto itself and cannot be used in isolation. There are two possibilities, based upon the level of automation that can be achieved: (i) high-volume low-value environments—policies can be put in place to cover not only risk, but also statutory, strategy, and other issues, and underwriters may still have the final say when system decisions are disputed by customers; and (ii) other environments—where amounts at risk or potential profits are high, scores may be provided to underwriters as decision aids.

Low

Decision rules

Credit scores provide little value by themselves. They have to be combined with strategies (rule sets) that are used to guide decisions. Initially, the scores focussed almost exclusively on pro-viding an accept/reject decision, and much effort was expended upon choosing an appropriate cut-off. Over time, however, lenders have become more sophisticated, not only in how, but also where the scores are used. Application scores are being used for risk-based pricing.

Behavioural scores are used for limit setting, over-limit management, and pay/no pay deci-sions. Collections scores are used to drive automatic diallers, and decisions on whether to consider the client rehabilitated, or dead (figuratively). Fraud scores are used to identify cases to be referred for further investigation. And propensity scores are used in marketing, to guide who should receive the mail-shot, and who not, or to set the terms offered.

Decisions provided by credit scoring are not always the final word. Lenders’ policies and/or staff can override them, and even then, customers can contest. Care must be taken here, because:

(i) policies may undermine the scorecards; (ii) loan officers are usually limited in their ability to assess large quantities of information; and (iii) customers often have a better understanding of their own circumstances than lenders. In any event, disputes and overrides can provide a vital feed-back mechanism for the people behind the computers, to find out what is actually happening in the field—and there should be a significant investment in override monitoring.

Something that must be stressed here is that these changes have given lenders greater flexibility, and they are becoming more adept at using the tools in an increasing number of ways, especially:

(i) improved account management and collections; (ii) risk-based pricing and securitisation of con-sumer and small-business loan portfolios. At the same time, changes to interest-rate ceilings have made lending to previously underserved markets feasible (sub-prime, MicroFinance).

Lenders must take care when securitising portfolios. According to Allen et al. (2003),

‘Banks are perceived as having superior information concerning the clients to whom they lend. Dahiya et al. (2003) show that the market reacts negatively when a bank sells a loan in its portfolio. The perception is well founded: firms whose loans are sold have a higher probability of bankruptcy than firms that do not. This special relationship is lost as soon as the loans are treated as transactional retail exposures’.

Process automation

Technology has been evolving at a fantastic rate. Everything is becoming faster, smaller, and cheaper. This applies to every decision component, at every stage of the credit risk manage-ment process. It also applies to delivery mechanisms, both for marketing (cross-sales, target marketing) and the channels used to deliver decisions and products (networking, Internet, ATMs, distance lending).

Credit scoring is primarily associated with application processing, where it has been the key driver behind decision automation. Many companies’ first experience—especially during the 1960s and 1970s—was highly manual, with staff members filling in scorecards, tallying the results, and applying the cut-off set by head office. Today the norm is automation, to the max-imum extent allowed by current technology. Relationship lending has given way to transactional

24 Module A : Setting the scene

lending, especially by ever larger banks, which use distance lending to reach customers with increased job and geographical mobility. This does not mean that the underwriter’s role has been done away with: (i) lenders limit automation, where the volumes are low, or the rela-tionship can be used to competitive advantage in a niche market; (ii) customers may dispute system generated decisions; and (iii) there are many instances, where scores are known to be insufficient by themselves.

Regulation

Lenders and consumers have been the main players driving credit growth, but legislators have also been playing an increasing role (see Module G, Regulatory Environment). Changes have been implemented that both facilitate access to credit, and provide guidelines for participants’

practices. In general, there has been an increasing movement towards the use of best practice, good governance, business ethics, and social responsibility. There is also a compliance hier-archy, encompassing statutes, legal precedent, industry codes of practice, company policies and procedures, and unwritten codes used by businesses.

The types of legislation that either affect, or are affected by, credit scoring are: (i) data priv-acy, which sets out limitations relating to manner of collection, data relevance, data quality, its use, information disclosure, subjects’ rights, and data security; (ii) anti-discrimination, to prevent prejudice on the basis of race, colour, language, religion, national origin, gender, etc.;

(iii) fair lending, to guard against predatory and irresponsible lending, and instead promote responsible lending; (iv) capital adequacy, to ensure that banks hold sufficient capital to protect against unforeseen losses; and (v) know your customer, to guard against criminal and terrorist activities, which also helps to protect against fraud.

1.5 Summary

This chapter has provided an overview of the broader theoretical aspects of credit scoring, including what motivates its use, where it fits, how it has affected lending, and how the public benefits. The lending of money is something that requires trust and, as in any instance where two parties contract, there is the potential for adverse selection (making the wrong choice), and moral hazard (change of behaviour once the deal is done). Lenders often rely on collateral, and guarantees, to enhance that trust, but improvements in data and automation have shifted the focus onto information. Information asymmetries (differences in information) that give borrowers an advantage over lenders will always exist, but this advantage can be reduced by accessing data from outside sources. Interest charges will also be reduced, as banks then have less scope to earn information rents (extra benefit that can be obtained by exploiting informa-tion not available to competitors), but this is offset by increased volumes. It also allows bor-rowers greater flexibility to move between lenders, and geographically.

Credit scoring is a tool used to collapse the wealth of available data into something more manageable. It came about as lenders applied predictive statistics to historical data, and derived models of customers’ propensity to behave in certain ways: to repay a loan (risk); to

respond to an offer (response); to move their business elsewhere (retention); and to use the product in a fashion that would be profitable for the lender (revenue). Its greatest benefit has been for assessing credit risk, especially loss probabilities—and more recently loss severity.

When it was first proposed, its primary use was for application processing, but it has become increasingly used throughout the CRMC.

While credit scoring can provide more accurate decisions, that is only one part of the story.

Scorecards often provide answers similar to humans, but lack human foibles, and provide greater speed and consistency. As a result, larger transactional lenders have greater flexibility and reach than they have ever known—especially banks. This does not mean that credit scores are a panacea though. They suffer because of huge data demands, complex systems, skills requirements, and staff and customer acceptance. Many lenders—especially smaller lenders—

still opt to avoid the pain, and stick with traditional relationship lending where they rely upon personal contact.

While lenders have seen significant benefits, so too have the customers that they serve, as competition has meant that most of the benefits are being passed on. Customers have improved:

(i) access to affordable credit; (ii) choice, from a range of products; (iii) mobility, as it has become easier to move relationships and credit histories between lenders; and (iv) convenience, as the task of applying has become easier, and often less embarrassing where the answer is ‘No!’

Once again though, there are also concerns: (i) it is very impersonal; (ii) many lenders do not handle disputes well; (iii) negative public perception about blacklisting; (iv) data privacy issues;

and (v) credit scoring cannot identify causes, but only uses correlations.

In spite of the concerns, there has been a huge amount of credit market growth. Credit scor-ing has played a role, as it has allowed lenders to improve their risk assessment processes, and venture into previously underserved markets. At the same time, the amount and quality of available data has improved substantially, especially as a result of data sharing arrangements via the credit bureaux. Increasing automation has reduced administrative costs, not only for data collection, but also risk assessment, decisioning, and delivery. And finally, changes to leg-islation have empowered lenders, by giving them access to information, and the ability to price for risk.

26 Module A : Setting the scene