The ability of Indian banks to promptly evaluate creditworthiness of small and medium enterprises is critical to ensure better accessibility of credit by the vast number of deserving Indian entrepreneurs.

With diminishing emphasis on priority sector lending and deepening of the market-oriented reforms in the financial sector, if the credit accessibility issue is not addressed adequately then there is a great danger of credit supply to the neediest segment of our business dwindling, leading to horrendous socio-economic consequences.

The lending technologies of a bank can be broadly divided into four categories: financial statement lending, asset-based lending, credit-scoring and relationship lending where the first three can also be clubbed together as transactions-based lending.

Financial statement lending is based on a firm’s audited financial statement and is accessible mainly to large SMEs because of non-availability of such statements with small ones, whereas asset-based lending is available to small firms of any size, but is expensive and requires that the firm have high-quality receivables and inventory to pledge.

Due to the limiting factors of these two lending technologies, lately credit-scoring, long used in consumer lending, has begun gaining popularity as a better tool for SME lending. But relationship lending, a method used by lenders to collect information from informationally opaque small firms about their creditworthiness by developing personal relationship, still remains the technology of choice for lending to small businesses.

Developed on the basis of validated model of performance dynamics, credit-scoring is a statistical technique that combines financial and non-financial characteristics to estimate probabilities of default. By systematically quantifying risk, credit-scoring speeds the decision process while bringing greater cost-efficiency, accuracy and fairness to each decision.

Studies have shown a quantum leap in the ratio of SME loans to total commercial loans among US banks using a credit-scoring technology. By using a credit-scoring tool the average processing cost of a SME loan has been brought down to one-tenth of the processing cost incurred without such tools.

For designing a credit-scoring model with high predictive power, it is essential to understand how performance is related to different factors, both financial (capital cost, labour, etc) and non-financial (strategy, motivation, etc). The explosive growth of international research on small business performance during the last couple of decades has already revealed some interesting aspects of these complex relationships.

Initiatives, like the inter-country project of the World Bank to pool data from banks operating in Brazil, Colombia and Mexico, may further assist the model-building efforts. For creating a robust credit scoring model, the SME Rating Agency (SMERA) in India formed in 2005 must take similar initiatives to pool data of our nationalised and cooperative banks.

At the same time, it is also important to acknowledge that the performance relationships revealed by various studies need to be tested in the context of individual markets for designing a location-specific scoring model. For example, studies have found that small business performance is significantly correlated to environment and firm resources but the nature of these correlations may be very different in two places, say in Gujarat and Nagaland.

Hence, different weights must be used to calculate credit score of loan applications in these two states. According to a World Bank report, in the early to mid-1990s a European firm sold an off-the-shelf SME scoring product in Latin America which had not been reengineered with local data and thus had limited predictive power. This experience soured many bankers in the region on the usefulness of SME scoring solutions.

Notwithstanding the utility of a credit scoring model for improving the efficiency of the evaluation process of loan applications, to ensure better result the credit scoring tool has to be effectively combined with relationship lending, universally popular as the most effective SME lending technology as on date.

Under relationship lending a lender’s decision in substantial part is based on the proprietary information about the firm and its owner (including ‘soft’ data, such as owner’s character and reliability) acquired by the loan officers through a variety of contacts.

A study conducted in the US in 2002 showed that relationship lending was associated with a fundamentally different lending process than transactions-based one and therefore, required a different organisational form.

Some other researchers have found that relationship lending is negatively correlated with bank size and hence cautioned that merger and concentration of banking operations will lead to severe rationing of SME credit since large banks gravitate towards statement-based lending suitable to bigger firms. The continuous pressure to increase the SSI investment limit in India is probably a manifestation of this fact.

However, as consolidation of banks gains further momentum the market might adjust to disappearance of relationship lending with the creation of new smaller banks and other intermediaries, say for example, micro-lending organisations.

It would be interesting to note that considering the negative correlations between relationship lending and physical distance from organisational headquarters of the creditors, the geographical spread of individual micro-lending organisations beyond a feasible limit for managing relationship lending may go against the basic strength of their operational model.

Though relationship lending is a fairly recent topic of academic research, some interesting results that have already emerged need to be examined in the Indian context. An American survey revealed that loan officer turnover was negatively correlated to credit availability for small firms, whereas a Swedish study found differing assessment of loan officers depending on their interactions, exposure to small businesses, age and education.

The vast amount of internal data pertaining to SSI lending already generated by our banks through years of priority sector lending if analysed carefully can reveal important facets of performance dynamics of the Indian small businesses.

This as well as fresh research initiatives to understand the continuously changing performance dynamics will help the financial institutions and policymakers to design fair and efficient credit delivery systems benefiting the 12 million strong SMEs that constitute the backbone of our industry.

This entry was posted on Thursday, July 2nd, 2009 at 2:01 am and is filed under Building Credit. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response or ask a question.