Corporate Credit Appraisals and Decision making

Corporate Loans occupy significant size in the asset portfolio of large Banks. Most of the Banks have a well-defined corporate credit department comprising of relationship managers, credit appraisal officers, risk officers, approvers and Department heads. Corporate Loans not only generates interest and fee based income but also build the asset side in the balance sheet of the Bank. Related businesses of corporates like current accounts, forex businesses, salary accounts of staff, financing options for employees of corporates, travel cards for top management/executives, remittance business, cash management are an added attraction for the Bank to pursue corporate banking clients.

Banks follow multiple methods for corporate credit appraisals and assessment. It has evolved from manual processing which involves balance sheet analysis, Credit monitoring and Assessment (CMA), credit worthiness and credit rating checks to partially excel based tools for CMA analysis over a period of time.  The complexity of the processing for corporate credit necessitated the need for development of a software with all the rules/checks/workflows built in, but it took a larger time for the vendor for building the same. However currently most of the Bank uses software for corporate credit appraisals, decision making and the likes.

The software may require to have organization level policy checks, customer specific checks, industry checks, credit worthiness checks and financial /cash flow comparison and checks which will ensure automatic credit decision. There could also be support in the software to overturn the workflow based system decision for credit approvals, by manual intervention based on manual approvals/committee approvals with deviations and exceptions. Depending upon the criticality of exceptions to be overridden, multiple levels of approvals can be enabled. Any automated decision /manual process decision need to be captured in the audit trail/report in the software and stored so that the same can be referenced at a later point of time. Many a times, the decision making is dependent on corporate relationships, reputation of share-holders, past history and precedence which may not reflect totally in the financials or cannot be captured in the credit report /software. However it is important to have a decision from the software and record/capture the reason for overriding the system approvals by a credit officer.

Corporate loans can go bad because of multiple reasons- non- performance or project failures, market risks, diversion of funds, operational failures, bad structuring of finances, people/process issues in the company, management issues etc. However, the Bank and the credit committee is always looked at with suspicion by a larger public when the loan goes bad for reasons beyond the credit assessment by the officer. The transparency which a software can provide with data capture, validations and workflow cannot be equated with a manual process and pinpointing of fingers/blame game can be avoided when the data and measures taken to approve with exceptions and deviations are recorded in the software with details of approval authorities.

To conclude, software should have the capability for workflows for manual/automatic decision making and policy makers of the Bank can decide on the course to be followed which will ensure transparency in the system. With mounting NPAs and bad loans, the bankers are looked upon skeptically by customers, fellow bankers, general public and it is time to come out clean with a mix of manual and automated credit decision skills where each and every action is captured, monitored, tracked and recorded. The staff cannot prevent some of the loans going bad or cannot control the external factors which is contributing to a loan going bad.  Inherent risk control measures need to be inbuilt in the software for monitoring and control. Further, the automated credit monitoring system during the life cycle of the loan has to be in place which should provide early alarms to Banks for taking adequate measures before the loans go bad beyond repair. This can go a long way in reducing the Gross NPA  of a Bank.