Synopsis: An effective credit risk management policy can assist lenders in improving their performance and beating out their competition. We understand what goes into building an effective credit risk management framework below.

The COVID-19 crisis put credit risk management back into the spotlight, not that it was out of the periphery of the storm created by the 2008 global crisis. While the regulators have stipulated various regulations, including Basel-III norms for banks, to curtail credit risk, a prudent credit risk management policy is more than just a compliance exercise.

 A successful credit risk management framework can enable lenders to massively improve their business performance and streamline their cash flows. The manual data collection and verification processes are now being overhauled thanks to automation. 

Below, we highlight the critical components that make for an efficacious credit risk management framework. But, first, let’s understand what credit risk is and its management.

What is Credit Risk?

Credit risk is the probability of the lender losing money on the borrower’s failure to repay his loans or fulfilling contractual obligations. A bond issuer defaulting on payments or a consumer failing to make invoice payments are all examples of credit risk.

Credit risk is typically determined through the 5Cs, which are the character of the borrower, collateral offered, capital, capacity to repay, and conditions/covenants attached to the loan.

What is Credit Risk Management?

Credit risk management is the process of assessing, measuring, evaluating, and mitigating credit risk associated with lending out money. It aims to minimize the probability of incurring losses on non-repayment of loans by borrowers. Credit risk management involves continuous evaluation of credit portfolios and implementation of adequate risk mitigation strategies.

Credit Risk Management: 6 Key Elements

Credit risk management is concerned with continuously evaluating new and existing customers’ creditworthiness to ensure loans are sanctioned to those with the capacity to repay. The six key elements of credit risk management are as follows:

1. Optimized Customer Onboarding 

The most important aspect of credit risk management is conducting due diligence by following proper Know Your Customer (KYC)/Know your Business (KYB) norms. Here, the focus is to assess and verify the genuineness and legitimacy of counterparties to minimize the possibility of money laundering, tax fraud, identity theft, and other financial crimes. For instance, not disbursing loans to people featured on the Fugitive Economic Offenders list.

Additionally, the onboarding process requires the gathering of accurate and updated information to create an extensive profile of the borrower. Certain details, including bank details, investments, repayment history, credit scores, tax details, business registration, and ownership structure, are collected and cross-verified against official/government databases. 

Some regulations may even insist on obtaining the prospective client’s Legal Entity Identifier (LEI) code (if issued by GLEIF) before transacting.

The advent of technologies like AI/ML, Robotic Process Automation (RBA), Big Data, Natural Language Processing (NLP), and analytics have further improved the onboarding process by reducing human errors and speeding up the processing stage.

2. Data-intensive Credit Scoring Model

A credit scoring model is basically a mathematical model which estimates the probability of default, i.e., the likelihood of the borrower triggering a credit event, such as bankruptcy or cross-default events. Based on this probability, the lender assigns a credit score to the customer. 

And yet, there have been instances of lending institutions extending loans based on their impression and the reputation of the client, thus being reckless with their credit decisions (NPA crisis). 

Instead, the key to prudent credit risk management lies in taking decisions guided by credit scores ascertained based on the borrower’s financial statements, transaction history, statutory compliance, litigation data, customer and employee feedback, trade references, social media presence, and transactions with related parties. For instance, frequent consumer complaints on social media can indicate poor business moats.

Machine learning (ML), predictive analysis, binning algorithms, and cumulative accuracy profile (CAP) enable lenders to stay abreast of the changes in their borrower’s profiles caused by the changing macro and microenvironment.

3. Robust Exposure Limits

Another vital element of credit risk management is the instituting of exposure limits on counterparties and sticking to them. These credit limits are usually determined after calibrating the borrowers’ risk scores with the company’s overall risk appetite.

Generally, customers with better risk ratings are assigned higher exposure limits. Additionally, credit limits for business loans differ per the sector or industry’s risk, economic cycles, and geography. The main goal of this exercise is to diversify the lender’s credit risk profile.

4. Credit Approval Protocol

No credit risk management process is complete without a standardized credit management workflow in place. Such workflows automate some of the repetitive tasks associated with gathering and processing data, thus quickening the loan approval rate. This becomes especially crucial with the mushrooming of tech-driven neo-banks and startups, who have been giving traditional lenders a run for their money. 

5. Effective Collection Processes

Irrespective of how assiduously a company computes a counterparty’s probability of default, chances are that some of them will delay or default on their obligations. Thus, what a company does to collect all the overdue payments becomes a crucial aspect of the credit risk management process.

Time management is of huge essence when it comes to debt collection. This is because the cost and energy expended on recovering payments exponentially increase as the repayment is delayed. 

Hence, debt collection can be made effective by leveraging data analytics to better predict a rise in default probability and prioritizing such collections. This will ensure the lender’s cash flows are optimal while building up their reputation as a pragmatically-run business.

6. Review and Monitoring

A single first evaluation of a borrower is no longer sufficient. Instead, a lending institution must actively monitor its counterparties and be on the lookout for any new sources of risk. 

Credit profiles may shift quickly in a volatile environment. As a result of implementing an early warning system (EWS), lenders will be better positioned to spot trouble signals and take corrective action. Statutory compliances, financial filings, and media reports, for example, can give a dynamic, real-time image of a business. 

Similarly, a definite decision must be made as to whether the company’s problems are temporary or long-term. This will assist a lender in determining if it makes economic sense to issue a credit line to the aforementioned firm.

Effective Credit Risk Management for a Strong Bottom Line

Developing a standardized credit risk management system is a critical step in improving a lender’s cash flows. To reduce the likelihood of default, lenders should regularly examine the risk connected with their existing clients. 

Understandably, due to the stiff competition from new-age startup lenders, many traditional banks and lending companies have been on tenterhooks about navigating the ever-evolving credit landscape without losing business. By automating their credit processes, lending businesses can reduce the time spent on peripheral actions, while onboarding clients quickly, computing accurate credit scores, and improving collections.