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Banks and insurers

AI-powered early warning

Intelligent risk analysis for banks and insurance companies based on large language models (LLMs). External signals are transformed into quantified risk features, compliant with EBA, CRR III, MaRisk and the EU Taxonomy.

The missing puzzle piece: external, qualitative signals completing the internal risk picture.

Regulation

Regulatory context

Output is auditable, documented and readily integrable.

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

Coverage

Risk coverage, model-ready

Reputation

Negative press, litigation, management turnover

ESG and supply chain

Environmental incidents, controversies, Tier-1/2 suppliers

Regulatory and compliance

Sanctions, licence violations, audit reports

Financial and operational

Payment defaults, market losses, operational disruptions

Pipeline

How it works: feature generation

  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 or SAS.

Comparison

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

Safeguards

Challenges and safeguards

Cost and latency. LLMs process complex volumes of text, so 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 and quality assurance. Since no predefined labels exist, sample datasets ("gold sets") are used to regularly verify relevance and precision. Additionally, manual spot checks and source comparison ensure consistency over time and detect drift.

Hallucination and misinterpretation. Every derived statement carries 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 and traceability. Every analysis is versioned, logged and reproducible. Prompts used, models deployed and timestamps are archived so 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.

Live demo with your sectors and counterparties. Quick start via data feed, with optional in-house setup and consulting.

Request a demo

Contact

Let's talk about your data

A short message is enough. We reply within one working day, with a real assessment rather than a sales pitch.

Location
Vienna, Austria