Implementing CoT Fine-Tuning for Lending

2025-12-17 · Source: Chris Shayan – Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, FinTech & Digital Financial Services · Depth: Intermediate, short

Summary

This tutorial details how to implement Chain-of-Thought (CoT) fine-tuning for the Llama 3.2 (3B) large language model to enhance explainability in high-stakes financial applications like loan approvals and fraud detection. It addresses the challenge of "black box" AI decisions by enabling the model to output its reasoning steps before a final decision, crucial for compliance with regulations such as the EU AI Act. The process involves using Unsloth for efficient 4-bit fine-tuning and constructing a synthetic dataset where assistant responses explicitly separate reasoning from the final decision. The fine-tuned model, trained with `SFTTrainer` and a `max_seq_length` of 2048, demonstrates its ability to provide detailed explanations for decisions like loan rejections based on Debt-to-Income (DTI) ratios or transaction blocks due to fraud rules.

Key takeaway

For AI Engineers building models for regulated financial services, implementing Chain-of-Thought fine-tuning is essential to meet compliance requirements and provide transparent decision-making. You should prepare training data that explicitly delineates reasoning steps from final outputs and leverage tools like Unsloth and `SFTTrainer` to efficiently train smaller, auditable LLMs. This approach mitigates the risks associated with opaque AI systems and fosters trust in automated financial processes.

Key insights

CoT fine-tuning transforms black-box LLMs into auditable reasoning tools for high-stakes financial decisions.

Principles

• Explainability is critical for AI in regulated industries.
• CoT fine-tuning enables auditable AI decision-making.

Method

Construct a dataset with explicit reasoning steps and final decisions, then fine-tune a small LLM like Llama 3.2 (3B) using `SFTTrainer` with a longer `max_seq_length`.

In practice

• Use Unsloth for efficient 4-bit fine-tuning.
• Separate reasoning from decision in training data.
• Set `max_seq_length` to 2048 for CoT responses.

Topics

• Chain-of-Thought
• LLM Fine-tuning
• Llama 3.2
• Financial Services AI
• AI Explainability

Code references

• unslothai/unsloth

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Chris Shayan – Medium.