Pagaya

Start with one bounded risk workflow for AI credit and lending platform for underwriting consumer assets, evaluating risk, and connecting financial institutions with capital. 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 Pagaya 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.