In this context, it is noteworthy that SIDBI has developed software for Credit Appraisal and Rating Tool (CART). With this software, loan proposals can be appraised on any electronic platform. This will also generate the rating of the proposal so as to decide on the pricing.
All proposals for term loan and working capital upto Rs. 100 lakhs can be processed by using CART method. It has also got a maker and checker system besides the ability to generate loan documents once the loan is sanctioned by the competent authority.
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Under the Small Enterprises Financial Centre (SEFC) Scheme of the RBI, SIDBI has given this software free of cost to Moll banks and SFCs. CART is particularly useful for clusters where sanctions to MSMEs can be done very fast by banks and institutions.
a. Banks use two basic methods for determining creditworthiness-or-assessing the bank’s risk level: Judgmental or Credit Scoring Method. On the basis of training & personal experience, the loan officer attempts to predict the likelihood that the applicants will be good or bad credit customers. For “Mass Credit” programs, where volumes are material for profitability & revenues, Credit
Scoring is to objectively distinguish between applicants who are expressed to be good accounts and applicants who are likely to be bad accounts. The scoring method draws profiles from the bank’s lending experience using statistical techniques. Credit scoring again can be used in a manual or an automated environment.
High volume operations typically process and score applications with automated systems. This is part of “Template Lending” wherein, with “benchmark” scores and pre-defined limits (exposure, industry, occupation), etc., credit decisions can be taken swiftly. After defining delinquency cut-off limits (say >60 days) as an example of a definition, data bases are identified for statistical analysis and for creating profiles.
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These data bases include financial and other personal information and information from the original credit bureau reports.
Thus, samples are taken from sets of both rejected and approved applications or charged off accounts. Next, all the financial, personal and credit bureau (bank/NBFC) track records, histories available on every account in the sample analyzed.
The purpose is to determine the predictive value of individual elements of financial information and predictive value of interrelationships among various individual elements. The predictive values are then weighted according to their elements and interrelationships. By statistically expressing the estimated level of risk of each applicant, this score predicts the likelihood of the account becoming good or bad over time.
b. From management’s perspective, the credit-scoring method reduces the likelihood of discrimination in the credit function. It helps the bank to approve a higher percentage of good accounts and decline a higher percentage of bad accounts.
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The strategic focus of the management should be upon rate of new account growth and level of risk taken into the bank credit card/loan portfolio. The ideal would be to find the optimum balance between approval rate and risk level the Perfect Cut-off score. This a worthy but elusive goal.
c. Banks that receive many applications typically process and score them with automated systems, with the help of data entry terminals. A preliminary score may decide whether a Credit Bureau report is required.
Some banks use automated processing for handling applications but use a judgmental method for the final credit decision. Approvals are followed by credit review functions including, credit extension, customer service, security and collections.