Building a risk engine means making decisions from information that is rarely complete, clean or perfectly reliable.

The data can come from APIs, databases, documents, public sources, historical events or unstructured text. Some information may be missing, contradictory, outdated or even intentionally misleading.

Adding AI to this process creates new possibilities, but also new engineering challenges. How much should we trust the model? How do we validate its output? And more importantly, how do we explain why the system reached a particular risk decision?

In this talk, I’ll share the engineering patterns behind building a production-grade AI risk engine.

I’ll cover:

• How to design a pipeline that transforms heterogeneous and unstructured data into usable risk signals

• How to combine deterministic rules, traditional data processing and LLMs without making the LLM the source of truth

• How to handle missing, conflicting and suspicious information

• How to use confidence scoring and validation layers around AI generated signals

• How to aggregate multiple signals into an explainable risk decision

• How to make every decision traceable and auditable

• How to evaluate AI components when false positives and false negatives have real consequences

• How to build observability around the whole pipeline so engineers can understand what happened when a decision goes wrong