Zest AI

Start with one bounded risk workflow for machine-learning credit underwriting platform for banks and credit unions to assess borrower risk and automate lending decisions. Define the decision being supported, the source data, existing rules, review thresholds, and the business cost of both a false positive and a missed fraud or risk event.

Run Zest AI in observation or recommendation mode first. Compare its findings with analyst-reviewed cases and preserve the underlying evidence, timestamps, identities, events, and source-system links. Investigate false positives and missed cases rather than tuning only for attractive dashboard metrics.

Introduce automation gradually. Low-risk enrichment and case creation can be automated earlier; account blocking, endpoint isolation, transaction denial, code changes, and other consequential actions should use policy gates, least-privilege credentials, reversible actions, and a complete audit trail.

After rollout, monitor detection quality, analyst overrides, latency, drift, integration failures, and changes in attacker or fraud behavior. Revalidate controls when adding new models, data sources, business lines, regions, identities, or autonomous actions.