Why AI Alone Won't Solve Loan Monitoring

Credit analysts can absolutely get value from tools like Claude, ChatGPT, and other LLMs. Their goal is typically to analyze borrower financials, assess the performance and risk of the loan, and provide management with a clear, supportable view of the credit.

A strong analyst can upload borrower financials, customize prompts, draft commentary, identify variance drivers, and prepare questions for management. Over time, they may even build a personal workflow around the model with reusable prompts, preferred memo formats, and common analysis templates. That is a real productivity gain.

But it is important to understand what is happening behind the scenes. The LLM itself is primarily a prediction engine. Modern AI tools may use it to interpret the request, write and run code, structure the data, perform calculations, and then summarize the results. This can make the experience feel seamless, but it also raises important questions:

• Were the calculations performed correctly?
• Can every number be traced to the underlying financials?
• Will the same process work when borrower formats (pdf, excel, csv, etc.), account structures, or reporting periods change?

Post-close borrower and loan monitoring is therefore not just an individual productivity problem. It is an operating process.

That requires more than an LLM alone. It requires a structured monitoring system around it.

Data Standardization Comes Before Analysis

In credit monitoring, the hardest part is often not writing the memo. It is getting borrower data into a usable format.

Borrowers submit financials in different formats, with different charts of accounts, naming conventions, levels of detail, and reporting packages. Some provide summarized financial statements. Others provide raw, GL-level data that must be mapped before it can support covenant calculations, KPI tracking, trend analysis, or portfolio reporting.

AI can assist throughout this process. In response to a prompt, the system may generate and run code (such as a Python script) to transform borrower data, calculate metrics, validate results, or perform analysis. Much of this can happen behind the scenes, without the analyst seeing or reviewing the underlying code.

The challenge is that the process may not be repeatable or traceable. Even when the same prompt is run again, the system may generate different code, apply different assumptions, or produce a different output. That makes it harder to understand how a result was calculated, reproduce it in the next reporting period, or apply the same methodology consistently across a portfolio.

If analysts rely on one-off prompts to interpret and transform borrower data, the organization remains dependent on individual judgment. The better direction is to build a repeatable data foundation that maps inconsistent raw borrower data into standardized financials (ETL Layer), metrics, covenant calculations, and reporting outputs. Once that foundation exists, AI can produce more reliable analysis because it is working off structured, consistent inputs.

Consistency Matters Across the Organization

A customized LLM workflow may work well for one analyst. But credit teams need consistency across the entire organization.

If every analyst uses different prompts, mapping assumptions, or calculation approaches, the outputs may look polished but still vary meaningfully from borrower to borrower. That inconsistency also limits the lender's ability to build a reliable portfolio-wide view of performance and risk. Creating consistency requires someone to architect, govern, and maintain the prompts, data workflows, calculation logic, and controls used across the credit team.

This becomes even more important when analysts change roles or leave the organization. Today, much of the complexity is buried in analyst-specific Excel files, with each borrower supported by its own unique mappings, formulas, and assumptions. Moving that same complexity into an analyst's personal Claude or AI workspace does not solve the underlying problem: it simply transfers the dependence from one individual tool to another. Borrower history, covenant logic, and reporting context still need to be preserved at the organization level, especially when analysts change roles or leave.

Lenders therefore face a build-versus-buy decision: develop and maintain an internal credit monitoring and AI workflow, or adopt a purpose-built platform. Either way, institutional knowledge and process logic must live at the organization level, providing continuity across analysts, borrowers, and reporting cycles.

Scale Requires a Repeatable and Accurate Monitoring Workflow

As portfolios grow, borrower monitoring becomes harder to manage through spreadsheets, email, and analyst-specific processes.

More borrowers means more reporting packages, more covenant tests, more exceptions, more follow-ups, and more internal reporting demands. At that point, the question is not just, "Can AI draft this faster?" It is, "Can we build a monitoring process that scales?"

AI can generate an assessment of a borrower's credit risk, but producing an answer is not the same as producing a reliable one. Without validation checks, defined business rules, and review controls at each stage, there is no guarantee that the underlying data, calculations, or conclusions are correct.

That is where lenders should be heading: toward a standardized, repeatable workflow where borrower data, covenant logic, KPI tracking, AI-assisted analysis, and reporting outputs are connected.

CovenantIQ is built for this next stage of borrower financial performance and loan covenant monitoring. The platform helps lenders convert raw borrower data, including borrower accounting system data, into standardized financials, metrics, covenant calculations, and credit-ready reporting outputs. The goal is not simply to use AI for one-off analysis, but to embed AI into a structured borrower monitoring process that is more consistent, transparent, and scalable.

In our next article, we will run the same mock deal package, including a quality of earnings report, balance sheet, income statement, and supporting financial data, through several customized LLM workflows. We will compare how each system interprets the data, calculates key metrics, identifies risks, and assesses the credit. Stay tuned for the results.