How LLMs Are Becoming Partners in Compliance and Fraud Detection Investigations

The rapid rise of artificial intelligence (AI) is transforming every sector, but few are experiencing as profound a shift as the field of risk management. Large Language Models (LLMs) are now emerging as key players in fraud detection, B2B compliance, and credit risk assessment, complementing and strengthening traditional machine learning (ML) methods.

With deep expertise in artificial intelligence and data science, our teams observe every day how these technologies are being integrated into decision-making and automated processes. LLMs stand out for their ability to interpret natural language, detect weak signals, and uncover complex patterns often invisible to purely statistical approaches.

However, it would be misleading to view them as a universal solution. Their performance depends on the ecosystem in which they operate: data quality, governance, human oversight, and bias control. Without a rigorous framework, even the most advanced models can lose relevance or, worse, create additional risks.

1. The Distinction Between Traditional ML and LLMs: A Pragmatic Approach

A common misconception is that Large Language Models (LLMs) are destined to replace traditional machine learning (ML) in risk assessment. In reality, the two approaches complement each other far more than they compete.

ML remains the cornerstone of credit risk modeling. It excels at processing structured — mainly tabular — data and producing clear, measurable, and reproducible results. Thanks to vast amounts of historical repayment behavior data, companies can calibrate models capable of generating highly accurate credit scores. For instance, an ML model might leverage over fifty variables — ranging from financial ratios to payment histories — to predict the probability of default with a high degree of reliability.

Fraud detection and KYB (Know Your Business) compliance , on the other hand, operate in a much more uncertain environment. Unlike credit risk — where “ground truth” is represented by repayment records — B2B fraud is often characterized by a lack of labeled, structured, and consistent data.

It is precisely in this area of complexity that LLMs stand out. Their ability to understand and analyze unstructured information — such as invoices, emails, audit reports, news articles, or legal documents — allows them to detect weak signals, inconsistencies, and subtle correlations that would otherwise be difficult, if not impossible, to identify without human intervention.

2. Credit Risk: LLMs as Intelligent Assistants

While machine learning (ML) models remain the foundation of credit decision-making, Large Language Models (LLMs) now play a complementary and strategic role. Their main contribution lies in streamlining internal processes and assisting operational teams. For instance, LLM-powered chatbots can now answer recurring questions from credit analysts, thereby reducing the time spent searching for information and documenting cases.

A particularly promising application is emerging in the Buy Now, Pay Later (BNPL) B2Bsector. In this field, LLMs have proven effective in classifying and interpreting transaction histories — notably distinguishing between recurring patterns and one-off sales. This fine-grained categorization provides a clearer, more dynamic view of transaction flows, improving the quality of internal analyses while feeding ML models with richer and better-structured data.

Key insight: LLMs do not replace traditional machine learning in credit risk management — they enhance it. Their added value lies in contextual understanding and data enrichment, while final decisions remain the responsibility of analytical models and risk experts.

3. Compliance: LLMs Automate Tedious Tasks, Not Judgment

In compliance functions, Large Language Models (LLMs) have proven to be powerful levers for operational efficiency. They have reduced manual workloads by 40 to 80% in time-consuming tasks such as multilingual sanctions screening, watchlist reviews, and analysis of complex regulatory documents.

Their impact is especially visible in Know Your Business (KYB)processes, where they automatically identify inconsistencies in company records or official documents. For instance, an LLM can flag discrepancies between business registries, reported addresses, or ultimate beneficiaries, allowing analysts to focus their efforts on genuinely high-risk cases.

However, human oversight remains essential. In one instance, an LLM associated “XXX Technologies” with a sanctioned entity, “XXX Group” — a lexically relevant alert that turned out to be a false positive after investigator review.

Key takeaway: LLMs excel at reducing informational noise, but they cannot make decisions in regulatory gray areas. The optimal approach is hybrid : automate initial checks while entrusting experts with the final judgment on complex or ambiguous cases.

4. Fraud Detection: LLMs Change the Game

Fraud detection relies on the ability to identify abnormal behavior in a constantly shifting environment, where tactics evolve rapidly. Traditional rule-based systems work well in stable, predictable contexts but struggle to keep up with the ingenuity and adaptability of fraudsters. Large Language Models (LLMs), by contrast, excel precisely in situations where context, ambiguity, and reasoning are central. Their strength lies in a form of “common-sense” reasoning, capable of connecting weak signals that would otherwise go unnoticed.

Recent advances in reasoning-focused models have led to the emergence of true reasoning engines. Popularized by Andrew Ng, this concept emphasizes using LLMs not merely to recall facts, but to reason over ambiguous data and draw coherent inferences. This approach mitigates the risks of hallucinations by anchoring models in logical scenarios rather than purely factual ones. 

At the same time, LLM-powered OCR pipelines OCR pipelines have matured. Initially unreliable — requiring systematic manual checks that nullified efficiency gains — these systems now achieve unprecedented levels of accuracy and data structuring . In the invoice financing sector, they can automatically analyze invoices, transport logs, and contracts, providing a consolidated and actionable view in real time.

5. The Way Forward

The future of AI in risk management now goes beyond the simple “input-output” model of LLMs, moving toward intelligent automation powered by autonomous agents..

Recent advances — including systems capable of multi-step reasoning and deep research — promise to enhance fraud detection , and KYB compliance. OpenAI’s new deep-research agent exemplifies this evolution, demonstrating the potential for AI-driven investigations and analyses.

However, increased automation carries risks. AI agents remain fragile: their performance can fluctuate day to day, and blind trust in their recommendations can be dangerous. The challenge lies in maintaining a delicate balance between autonomy and human oversight. The winning formula is clear: leverage agents for complex, repetitive analyses while requiring human validation for critical decisions, such as freezing an account or reporting potential fraud.

The story of LLMs in risk management is therefore one of bothinnovation and caution. These models offer unprecedented capabilities, enabling finer understanding and faster decision-making, but their effectiveness depends entirely onproper integration, governance, and oversight within a comprehensive risk management framework.

Key Takeaways for Implementation:

• LLMs complement, but do not replace, traditional ML – Use LLMs to process unstructured data and complex contexts, while maintaining structured machine learning models for precise, measurable, and explainable risk assessment.
• Governance is non-negotiable – Even the most advanced AI systems fail without rigorous oversight and governance. Establish clear protocols, escalation procedures, and full traceability of algorithmic decisions.
• Human–AI collaboration is key to success – Automate repetitive, low-value tasks, but entrust high-stakes decisions to human judgment. This balanced partnership maximizes productivity while preserving accountability.
• Continuous adaptation is essential – Fraud evolves, and systems must evolve with it. Implement ongoing improvements through prompt engineering, analysis of false positives, and continuous workflow optimization.

Ultimately, the most successful organizations will be those that recognize LLMs for what they truly are: powerful investigative partners capable of amplifying human capabilities — but never replacing them.