How do you guarantee your customer support bot does not offer a free refund? Building safety checks is critical for public-facing AI deployments.

Automated Hallucination Auditing

We utilize LLM-as-a-judge frameworks (using packages like TruLens or Giskard) to audit responses. These systems automatically test generation against three main metrics:

• Context Adherence: Is the response derived solely from the provided RAG documents?
• Answer Relevance: Does the response address the user’s actual question?
• Grounding: Are there contradictions between the generated text and factual baselines?

Compliance is no longer optional. As the EU AI Act enforcement phases begin, Estonian FinTech firms must audit their AI and ML credit-scoring, risk assessments, and customer operations systems.

Risk Classifications

The Act divides AI systems into four risk tiers. Credit scoring models and automated hiring tools fall under the High-Risk category. Deploying such systems in the EU requires conformity assessments, strict data governance, detailed audit logging, and human oversight mechanisms.

Preparing for Compliance

To avoid heavy administrative fines, FinTechs must establish automatic testing pipelines to check their models for bias, hallucinations, and data leaks. Continuous logging and model telemetry are critical.