This blog is written and published by S&P Global Market Intelligence, a division independent of S&P Global Ratings. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence credit scores from the credit ratings issued by S&P Global Ratings.
As companies continue to navigate a sustained period of economic stress the need to find cost and efficiency savings is ever present. In the realm of credit risk, many direct lenders, be they Banks or buy-side firms are increasingly looking at ways to improve productivity and cost base by automating significant parts of their credit workflow. Indeed, research from S&P Global Ratings on the 2023 outlook for banks in France states that “given the need for further productivity gains, banks may be obliged to explore additional avenues of automation…”.
On a more anecdotal note, at a recent S&P Global Market Intelligence event a customer managing one of the largest direct lenders in Europe summarised that providing their borrowers’ needs fell within the defined lending criteria, they could go from the borrowing application right through to a lending decision with little or no manual intervention. In fact, their business margins were so impressive as a partial result of this practice, their main concern was how to maintain their growth trajectory while continuing with the innovation, data utilization, and automation that had made them market leaders.
Depending on the size of companies you are lending to or have exposure to, there will be varying degrees of end-to-end credit workflow automation that you can adopt. We have identified 4 key steps within the credit risk workflow that can be improved with automated processes.
Step 1: Origination/Screening processes
Using APIs or Data Feeds to extract a plethora of relevant information including pre-generated Credit Analytics credit scores, probabilities of default (PD), business credit reports, pre-uploaded financial statements, and firmographic information (country, industry, business size, etc.) can provide a quick way to identify customers that you want to engage with or to screen out companies with inadequate credit strength. The pre-generated credit scores and PD can also be utilized in your final credit assessment and credit limit setting.
Step 2: Detailed Credit and Scenario Analysis
When origination/screening is complete, and you have received customer financials that you want to assess it can be a great time saver to use Data Extraction and Spreading tools such as ProSpread™ via Software as a Service or the desktop user interface. These products will automatically extract financials from a variety of unstructured customer document formats, map each item to a standard chart of accounts, then allow for further spreading of financials, ratio analysis, and storage in a centralized repository.
Once extracted, these customer financials should be automatically pushed into a credit scoring engine to generate a base credit assessment for your customer which can be further customized with Parental & Government or qualitative overlays.
It is also a good practice to incorporate economic scenarios into company risk assessment to understand how changing economic conditions could affect the credit score or PD of your customer, which depending on the scenario likelihood could potentially be included in the final risk grading. Using Credit Analytics Macro-Scenario model with pre-generated stressed scenarios provides an immediate way to understand this impact.
Step 3: Credit Decision and Approval Framework
A recommendation here is to have a rules-based workflow and criteria with exceptions where needed, which would allow you to automate many parts of the approval and decision process. Starting with some simple rules around approvals, which could be based upon multiple criteria, a simplified example could include a specific borrowing threshold, which if not breached would go through an auto approval depending on the stage in the approval chain. In addition, the workflow needs to allow you to assign tasks and share information/documents as appropriate.
Step 4: Risk Monitoring and Surveillance
Once the customer or exposure is onboarded, you need to monitor for risks and early warning signs of credit deterioration. Having auto-generated triggers/alerts on portfolio deterioration on the basis of updated information impacting credit quality helps with the management of these accounts. Using sophisticated Early Warning Systems such as those developed at S&P Global Market Intelligence and scores such as RiskGauge™, in particular, can identify significant changes during the gap between receiving updated financials.
Another important part of liquidity assessment are payment trends such as expected days of payment delays, average days to payment past the due date, payment terms, and so on. The updated information should automatically trigger an alert as soon as there is a change indicating that a customer’s payments could be impacted.
Automation – Transforming the way you monitor and manage risk.
The adoption of automation technology is transforming the way lenders and other financial institutions monitor and manage risk, helping to significantly reduce the amount of manual labor required, while simultaneously creating cost and efficiency savings. Automated credit risk workflows are also critical for monitoring and surveillance and provide early warning signals for potential risks that could otherwise go unnoticed.
To find out how S&P Global Market Intelligence can help you gain up-to-date visibility into the credit risk of your loan portfolios and identify any changes in loan performance quickly and easily visit our website and request a demo.
 “French Banks 2023 Outlook: Withstanding The Slowdown”, January 23, 2023. S&P Global Ratings.
 Credit Analytics is a suite of data, analytics and workflow tools designed to help credit professionals accurately capture the risks and rewards from their credit exposures.