Mitigating AI hallucination risk in banking and credit decision-making

# The challenge

In April 2026, a leading global law firm admitted that AI-generated hallucinations, including fabricated and misquoted legal citations, were submitted in a court filing of a high-profile bankruptcy case, prompting a public apology once the errors were identified by opposing counsel. While attributed to a lapse in human verification, the incident also highlights a fundamental structural weakness: reliance on a single, general-purpose chatbot without purpose-built models, stable source grounding, or auditable controls. In such setups, verification becomes inherently fragile and resource- intensive, as hallucination risk is pervasive and traceability diminishes as models and sources evolve.

While this incident occurred outside the banking sector, it highlights a challenge that banks increasingly face as they embed generative AI across credit analysis, due diligence, compliance, and risk monitoring: AI-only approaches without structured human oversight and a grounding in long-term, high-quality historical data can expose institutions to significant risk.

AI is rapidly becoming a material business conduct risk for financial institutions. The Business Conduct Risk Intelligence Report 2026, drawing on insights from 500+ C-suite leaders, shows that financial services executives expect AI risks to have a greater impact on their business over the next three years. While issues such as corruption and human rights remain relevant, AI is viewed as a fast-evolving exposure alongside climate transition and data integrity, with the potential to directly affect decision-making, accountability, and risk outcomes.

The April 2026 incident demonstrates how even well‑designed AI policies and training frameworks can fail when human oversight is not operationalized consistently, a lesson with direct relevance for banks operating under heightened supervisory and governance expectations. However, the incident also shows that embedding robust guardrails for responsible and trustworthy AI from the outset, including human-labelled training data and auditable data records, can help mitigate hallucination risk and strengthen control over downstream audit and governance risks.

# The solution 

RepRisk helps banks address emerging AI-related risks through its AI with humans in the lead approach that combines advanced AI technologies with expert human validation embedded directly into the risk intelligence lifecycle, rather than applied as a secondary check.

For banking and credit decision‑making, this approach provides:

• AI for scale, humans for accuracy
  RepRisk uses AI to screen millions of documents every day across reputational and business conduct risk topics, while specialist analysts review, curate, and approve every risk incident before publication.
• Governance aligned with banking and supervisory expectations
  Each risk insight follows a transparent, structured process of screening, analysis, quality assurance, and quantification that supports traceability, auditability, and model governance.
• Analyst quality assurance
  Incidents undergo final approval by an experienced analyst, significantly reducing the risk of fabricated, misleading, or mis-contextualized information entering client workflows.
• Human-reinforced AI learning loops
  Analyst feedback continuously improves AI models, allowing RepRisk to scale insight delivery without compromising control or interpretability; a critical requirement in regulated banking environments.

This ensures banks receive decision‑ready, defensible risk intelligence, not unverified AI output.

# The impact 

By applying AI with humans in the lead, RepRisk enables banks to:

• Strengthen credit and counterparty risk assessments
  Banks can rely on validated reputational and business conduct risk signals when assessing borrower quality, large exposures, and project finance transactions.
• Respond to fast-evolving business conduct risks
  Emerging issues such as AI misuse, data integrity failures, and transition-related risks can be identified early, before they crystallize into financial or reputational loss.
• Support regulatory compliance and internal governance
  Proven methodology, human-curated sources, and verified data records deliver relevant, accurate, and timely results – every time, helping banks meet expectations around model risk management, explainability, and accountability.
• Protect institutional reputation and decision integrity
  Particularly in lending, sustainable finance, and emerging-market contexts, validated risk intelligence reduces the likelihood of adverse outcomes driven by flawed data or automation bias.

In contrast to other AI solutions, RepRisk’s approach ensures that human judgment remains embedded at every critical decision-making point.

# Why it matters 

The Business Conduct Risk Intelligence Report 2026 highlights a clear shift in the way that financial services leaders are increasingly viewing AI, data integrity, and climate transition as among the most material non-financial risks shaping the future of banking. These risks may not be immediately visible in traditional financial metrics, yet they can critically impact credit quality, operational resilience, regulatory outcomes, and institutional reputation.

For banks, this underscores several critical implications:

• Financial data alone is not sufficient to protect the business from value erosion driven by non-financial risk-related failures.
• Regulators and stakeholders continue to expect banks to demonstrate accountability for AI-assisted outcomes, reinforcing the need for solutions that provide reproducible, defensible, and auditable data.
• Rapidly evolving business conduct risks require adaptive controls and expert oversight, not static frameworks or fully automated systems.
• AI‑enabled decision‑making can enhance speed and efficiency, but without robust governance it introduces new conduct, model, and reputational risks.

By using AI with humans in the lead, RepRisk provides banks with a proven, scalable way to harness AI’s benefits while maintaining control, transparency, and accountability regulators and stakeholders expect.

Copyright 2026 RepRisk AG. All rights reserved. RepRisk AG owns all intellectual property rights to this case study. This information herein is given in summary form and does not purport to be complete. Any reference to or distribution of this case study must include the entire case study to provide sufficient context. The information provided in this presentation does not constitute an offer or quote for our services or a recommendation regarding any investment or other business decision. Should you wish to obtain a quote for our services, please contact us.