Financial AI Workflows
How Syzygy Rosetta applies to key financial use cases — AML monitoring, fraud review, lending decisions, and underwriting.
AML Monitoring
Anti-money laundering (AML) systems generate large volumes of alerts, many of which are false positives. AI models help prioritize and triage these alerts, but regulators require clear documentation of why certain alerts were escalated or closed.
Without proper evidence, institutions cannot demonstrate that their AI-assisted AML system operates within regulatory expectations. False negatives — missed alerts that should have been escalated — become difficult to investigate after the fact.
Rosetta captures every AML alert triage decision: what the model recommended, which risk signals were detected, what policy was applied, and whether human review occurred. Each alert generates an audit receipt that serves as evidence of proper handling.
Fraud Review
Fraud detection systems use AI to score transactions and identify potentially fraudulent activity. When a transaction is flagged, the system must decide whether to block, allow, or hold it for review. Each decision carries significant financial and customer experience implications.
If a fraud decision is later challenged — by a customer, auditor, or regulator — the institution must be able to explain exactly why the decision was made. Without evidence, institutions face regulatory penalties, customer distrust, and financial losses.
Rosetta documents every fraud review decision: the model's fraud score, contributing signals, policy rules applied, and the final disposition. If human review occurred, that is also recorded, creating a complete chain of accountability.
Lending Decisions
AI models increasingly assist in lending decisions — from credit scoring to loan approval to pricing. These decisions are among the most heavily regulated in finance, with strict requirements around fair lending, adverse action notification, and documentation.
Lenders must be able to explain why a loan was approved or denied, including the specific factors that influenced the decision. Without comprehensive evidence, lenders face fair lending challenges, regulatory sanctions, and reputation damage.
Rosetta captures the complete lending decision: credit factors considered, model output, policy rules applied (including fair lending checks), risk assessment, and the final decision. This evidence supports adverse action notifications and regulatory examinations.
Underwriting
Insurance and financial underwriting increasingly leverages AI to assess risk, set premiums, and determine coverage. Underwriting decisions must be consistent, compliant with regulations, and explainable to both customers and regulators.
AI-assisted underwriting introduces risks around model bias, inconsistent application of underwriting guidelines, and inability to explain premium or coverage decisions. Regulators are increasingly focused on AI fairness in underwriting.
Rosetta documents every underwriting decision: risk factors evaluated, model assessments, underwriting guidelines applied, and the final determination. This creates a defensible record for regulatory review and customer inquiries.
Cross-Cutting Requirements
Across all financial AI workflows, several common requirements emerge:
Evidence Documentation
Every AI-assisted decision must be documented with sufficient detail for audit and regulatory review.
Policy Consistency
Policies must be applied consistently across all decisions, regardless of the workflow or model.
Human Oversight
High-risk decisions must be reviewable by humans before finalization.
Risk Proportionality
The level of scrutiny should be proportional to the risk level of the decision.