Corporate Loans occupy a significant size in the asset portfolio of large banks. Most banks have a well-defined corporate credit department comprising relationship managers, credit appraisal officers, risk officers, approvers and department heads. Corporate loans not only generate interest and fee based income but also build the asset side of a bank’s balance sheet. Related businesses of corporates such as 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 banks to pursue corporate banking clients.
Banks follow multiple methods for corporate credit appraisals and assessment. Credit appraisal and assessment has evolved from manual processing for 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 large time for a vendor to build the same. However currently most banks use software for corporate credit appraisals, decision making, etc.
The software is required to have organization level policy checks, customer specific checks, industry checks, credit worthiness checks and financial / cash flow comparison to 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 needs to be captured in the audit trail / report in the software, and stored so that the same can be referred to at a later point in time. Many a times, 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. It is important to have a decision from the software and record / capture the reason for overriding system approvals by a credit officer. The software can play the role of a watchdog oblivious of customer stakeholders and subjectivity. The software may also open up the hidden nexus between staff and corporate.
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. Needless to say, the quality of credit assessment using software or manual processes cannot be compromised, the credit projects should be in line with prevailing market conditions and the approving authorities should be aware of market trends for the future. The checks and expertise do not prevent a corporate loan from going bad.
However, the bank and the credit committee are always looked at with suspicion by the larger public when a loan goes bad for reasons beyond inaccurate credit assessment by the officer. The transparency which a software can provide with data capture, validations and workflow cannot be equated to a manual process, and the blame game can be avoided if the data and measures taken to approve with exceptions and deviations are recorded in the software with details of approval authorities.
To conclude, the software should have the capability and workflows for manual/automatic decision making, so that the policy makers at a bank can decide on the course to be followed to ensure transparency in the system. With mounting NPAs and bad loans, bankers are looked at with a great degree of skepticism by customers, fellow bankers, and the general public. It is high time banks come up 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 from going bad and cannot control the external factors contributing to bad loans. Inherent risk control measures need to be inbuilt in the software for monitoring and control. Furthermore, automated credit monitoring system during the life cycle of the loan must be put in place to provide an early alarm to banks to taking adequate measures before loans go bad beyond repair. This can go a long way in reducing the gross NPA of a bank.
