Unlock the Benefits of Automating your Direct Lending Workflow

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.

Direct lending is the largest category in private credit, accounting for 44% of AUM[1]. As the ecosystem evolves, technology is playing an increasingly crucial role in the fundraising, underwriting, and operational processes for private credit investors. Automation and digitization are poised to enhance underwriting decisions and facilitate more efficient portfolio monitoring, especially across large asset pools. Consequently, many direct lenders, including banks and buy-side firms, are focusing on automating key aspects of their credit workflow to improve productivity and reduce costs.

The adoption of automation and digitalization 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 providing early warning signals for potential risks that could otherwise go unnoticed.

At a recent S&P Global Market Intelligence event, a customer managing one of the largest direct lenders in Europe shared an insightful perspective. They explained that as long as their borrowers met the defined lending criteria, they could process a loan application all the way through to a lending decision with minimal to no manual intervention. This approach contributed significantly to their business margins, and their main concern was finding a way to maintain their growth trajectory while continuing to innovate, leverage data, and automate processes 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 and 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 potential borrowers and industries that you want to engage with or to screen them out. The pre-generated credit scores and PD can also be utilized in your initial 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.

A more comprehensive qualitative assessment can also be undertaken with a Credit Assessment Scorecard that can automatically retrieve pre-stored company financials while also allowing for entering of your proprietary customer financials. Leveraging these gold standard credit assessment frameworks will provide PD’s, Letter grade credit scores and loss given default (LGD) for companies across multiple industries with criteria that can stand up to investor scrutiny while reducing the pressure on your internal analyst resources.

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.

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.