Self-Improving AI and its Application to Credit Risk Analysis

AI is transforming workflows in credit analysis, risk management, and portfolio monitoring by addressing key inefficiencies. Traditional processes often involve manual data collection, fragmented risk assessments, and delayed decision-making. AI streamlines these workflows by automating document analysis, proactively identifying risk signals, and dynamically refining risk models to enhance accuracy. These improvements reduce human workload, minimize errors, and enable faster, more data-driven lending decisions. Importantly, it is not just about automation, it’s about deploying self-improving AI that refines itself over time, making lenders and analysts faster, more precise, and more strategic. The more lenders use it, the better it gets and the more value they derive.

At EnFi, we’re working to develop these and other approaches to create the world’s best risk analyst assistants, AI-powered tools that eliminate ambiguity, keep workflows moving, and continuously improve.

A Primer on AI in Lending

Understanding AI's role in lending requires a closer look at how these technologies interact with financial risk analysis. Unlike generic AI applications, lending-specific AI is trained to evaluate financial health, detect risk patterns, and enhance decision-making processes.

• LLMs (Large Language Models) in Lending: Think of an LLM as the brain of an AI system—full of general knowledge but requiring specialized training for lender-specific workflows. While it can process financial documents and read legal agreements, its real value comes when it is fine-tuned with private deal-related information, lender policies, and historical lending data. This specialization enables it to make more relevant risk assessments, spot anomalies, and provide actionable insights tailored to financial institutions.
• Prompting for Risk Assessment: Loan officers, credit analysts, and portfolio managers ask AI-driven tools questions about borrower risk, financial stability, or market conditions. Effective prompting ensures AI produces responses that align with lender policies and risk frameworks, avoiding vague or incomplete answers.
• Agentic Systems in Lending: AI-driven agentic systems function as autonomous decision-support tools, capable of managing complex workflows without continuous human oversight. These systems coordinate multiple AI components—such as document analysis, risk scoring, and compliance checks—enabling seamless integration of AI insights into lending operations. By operating independently within predefined guidelines, agentic systems streamline loan processing, risk evaluations, and borrower monitoring, reducing friction in high-volume lending environments.
• AI-Powered Risk Signal Detection: AI can analyze massive datasets to detect early indicators of financial distress before they escalate into defaults. By continuously learning from borrower behavior, economic conditions, and sector performance, AI can provide early warnings on potential risks.
• Automated Documentation Analysis: AI-powered tools can extract key financial clauses, covenants, and risk factors from loan agreements and credit reports. This speeds up due diligence and ensures that lenders do not overlook critical obligations or borrower disclosures.

With these foundations, we can explore how AI is reshaping lending workflows.

The Evolution of AI in Credit and Risk Analysis

The journey of AI in lending started with simple chat-based customer support, evolved into document analysis, and then expanded into financial spreading and risk assessment. These incremental advancements are paving the way for more sophisticated applications, but early tools lacked adaptability and required significant manual intervention.

Traditional AI-driven lending tools provided a step forward in automation but lacked adaptability. Self-improving AI introduces a new paradigm where models learn dynamically, refine their understanding of lender intent, and optimize workflows without requiring constant human intervention. These enhancements are making lending and portfolio monitoring far more efficient and effective.

Understanding Self-Improving AI and Auto-Prompting

Self-improving AI refers to models that continuously refine their performance by learning from user interactions, adjusting their outputs based on past experiences, and optimizing decision-making without requiring constant retraining. Auto-prompting involves AI dynamically modifying and structuring user queries to ensure clearer, more actionable responses from language models.

Here’s how self-improving AI and auto-prompting systems are transforming financial analysis workflows:

1. Prompt Rewriting: AI That Speaks “Lender”

The Problem:

Lenders ask nuanced questions, but vague wording leads to ambiguity. AI models interpret these questions literally, which can lead to incomplete or incorrect results.

The Solution:

AI helps clarify prompts on the fly, using contextual understanding from past interactions, metadata, and lender-specific financial policies to resolve ambiguities before querying the model. By analyzing historical lender queries, borrower profiles, and underwriting guidelines, AI refines its ability to interpret intent, ensuring responses align with institutional risk frameworks and best practices.

? Example: A lender asks, "Show me the latest quarter’s financials." But "latest" could mean:

• Relative to today?
• Relative to the borrower’s latest submission?
• Relative to the fiscal year?

Instead of guessing, AI automatically rewrites the prompt based on document timestamps, borrower disclosures, and reporting cycles.

? More complex example: A lender asks, "Run a downside risk analysis considering interest rate shifts."

• AI expands this request by analyzing historical rate movements, factoring in the current financial environment, and incorporating Fed guidance on potential .25% and .5% shifts.
• The final prompt is optimized for the model, ensuring precise, data-backed insights rather than generic responses.

2. Auto-Responders: Keeping the Workflow Moving

The Problem:

AI models often respond with clarifying questions that require human input, slowing down decision-making and breaking the workflow.

The Solution:

AI acts as an intermediary, responding to the LLM’s follow-ups automatically based on contextual knowledge of the loan, lender policies, and industry standards.

? Example: A lender asks for a borrower’s debt-service coverage ratio (DSCR).

• The LLM might ask, "Should I use trailing 12-month EBITDA or forward-looking projections?"
• Instead of pausing, an auto-responder agent answers intelligently using policy guidelines, ensuring a smooth and continuous flow of analysis.

By eliminating unnecessary delays, AI ensures lenders remain focused on insights rather than manual back-and-forth clarifications.

3. LLM as a Judge: Self-Correcting & Learning Over Time

The Problem:

AI-generated insights aren’t always reliable—hallucinations, omissions, and misinterpretations happen.

The Solution:

AI automatically questions its own output, benchmarking responses against historical data, external sources, and lender policies. It determines credibility by cross-referencing borrower performance trends, assessing citation reliability, and validating financial indicators against known risk models. Additionally, AI applies confidence scoring to highlight potential inconsistencies and flag areas requiring human oversight.

? Example: AI runs a borrower risk assessment and flags three key risks. But instead of accepting its own answer:

• It cross-checks against historical loan performance for similar profiles.
• It benchmarks the response against industry norms and risk models.
• It runs citation validation to catch hallucinations or missing context.

If errors or omissions are found, the AI updates the output, refines future prompts, and continuously improves.

This isn’t just AI answering questions—it’s AI verifying, learning, and evolving its own risk assessment capabilities.

4. AI as a Thought Partner: Guiding Smarter Decisions

The Problem:

Lenders don’t just need answers—they need better ways to think about complex scenarios.

The Solution:

AI suggests logical next steps, highlights blind spots, and guides users to more strategic insights.

? Example: A lender asks, “What’s the borrower’s probability of default?”

Instead of just returning a number, AI suggests:

• "Do you want to factor in macroeconomic shifts?"
• "Would you like to run a stress test with varying interest rate scenarios?"
• "Here’s how similar borrowers performed under similar conditions."

AI doesn’t just provide data—it elevates decision-making. As the AI interacts with the users throughout this process, it is learning from what it is being asked to do and feeding that back into refinements in workflows, and what it suggests as future AI guided analysis.?

5. Autonomous Knowledge Workers: AI That Acts Like a Junior Analyst

The Problem:

Analysts spend hours synthesizing financials, risk models, and borrower profiles across multiple reports.

The Solution:

AI can autonomously compile reports, monitor portfolio risk shifts, and proactively surface insights—reducing human workload. What makes this a key element of self-improving AI is its ability to refine its understanding over time, learning from patterns in financial data and lender interactions to improve future risk assessments and recommendations.

? Example: Instead of manually reviewing all loan data, AI monitors key financial metrics in real-time and alerts the lender only when deviations occur—before risks escalate. As users interact with this data, the AI learns from this feedback. Over time, the AI refines its approach, identifying new risk indicators, improving the financial spreading accuracy, learning from past decisions, and adjusting alert thresholds based on historical accuracy and lender feedback.

What This Means

Lenders and risk professionals aren’t being replaced by AI—they’re being supercharged by it. For example, AI can analyze borrower data at scale, identifying early warning signs of financial distress that a human might miss in a manual review. However, instead of making lending decisions autonomously, AI enhances human expertise by surfacing key insights and suggesting possible risk mitigation strategies. This allows analysts to focus on complex judgment calls while leveraging AI-driven efficiency for data analysis and monitoring. The future of AI in lending isn’t just automation—it’s self-improving intelligence that continuously refines itself, making analysis sharper, faster, and more reliable.

AI’s ability to rewrite prompts, self-correct, guide strategic decisions, and autonomously monitor risk will reshape credit analysis and portfolio management. As these systems become more advanced, they will enable financial institutions to be more proactive, identify emerging risks sooner, and allocate resources more efficiently.

With AI-driven enhancements, lenders will be able to focus more on higher-order strategic thinking rather than manual data retrieval and processing. The key to leveraging AI effectively lies in continuous training, lender-specific optimization, and structured oversight to ensure alignment with risk policies and business objectives.

The Future of AI-Driven Lending

Self-improving AI is pushing financial decision-making to new heights, not by replacing risk analysts but by enhancing their speed, accuracy, and strategic insight. The ability to dynamically refine prompts, automate workflows, self-correct errors, and proactively surface risks marks a new era of lending intelligence.

At EnFi, we are leading this transformation by developing the world’s most advanced risk analyst assistants, helping financial professionals make smarter, more informed decisions.