AI Early Warning for Financial Institutions

Intelligent risk analysis for banks and insurance companies based on large language models (LLMs).

External signals transformed into quantified risk features – compliant with EBA, CRR III, MaRisk, and EU Taxonomy.

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Regulatory context

• EBA Guidelines on ESG Risks: consideration of external, qualitative factors
• CRR II/III: expanded data foundation & governance requirements
• MaRisk AT 4.3.3: appropriateness, traceability, documentation
• EU Taxonomy/CSRD: enhanced transparency and data requirements

Output is auditable, documented, and readily integrable.

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How It Works (Feature Generation)

The LLM pipeline converts unstructured text information into verifiable numerical signals.

1. Sources: News, social media, reports, and other open data sources
2. Detection: LLM-powered analysis to identify relevant events and signals
3. Quantification: Assessment via scores, frequency, relevance, and evidence documentation
4. Export: Delivery of structured features for Python / SQL / Databricks / SAS

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Beyond Classical Approaches

Classical Approach

• Keyword-based monitoring
• Limited contextual understanding
• Detects risks only upon explicit mentions

LLM-Based Approach

• Detects implicit risk signals even without explicit reference
• Understands semantics and context across languages
• Quantifies and explains signals with traceable evidence

Challenges & Safeguards

Cost & Latency
LLMs process complex volumes of text – therefore a proper balance between cost, response time, and coverage is critical. Depending on the use case, processing occurs in batch mode or near real-time, combining timeliness with cost-efficiency.

Evaluation & Quality Assurance
Since no predefined labels exist, sample datasets (“Gold Sets”) are utilized to regularly verify relevance and precision.Additionally, manual spot checks and source comparison are employed to ensure consistency over time and detect drift.

Hallucination & Misinterpretation
Every derived statement contains source references and evidence texts, keeping the decision traceable. This allows verification of why a risk was identified – and whether the underlying basis is valid.

Governance & Traceability
Every analysis is versioned, logged, and reproducible. Prompts used, models deployed, and timestamps are archived so that results remain auditable and traceable – compliant with internal control systems and regulatory requirements.

LLM-based systems provide high transparency – but also bring unique challenges that must be actively managed.