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Fraud Investigations
Quickly identify and investigate suspicious activities with comprehensive data and AI assistance to determine if transactions are fraudulent.
AML Compliance
Streamline SAR filings with AI-generated narratives and automated evidence collection, reducing your burden while staying compliant.
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Review and assess borderline credit applications with complete context about applicants, enabling more accurate credit decisions.
Account Security
Investigate potential account takeovers or suspicious logins with complete device and behavioral intelligence integrated directly into case cards.
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FAQ
Glad you asked.
What is fraud and AML case management?
Fraud and AML case management is the system and operating process used to turn alerts, referrals, or suspicious events into documented investigations. It brings together relevant entities, transactions, evidence, tasks, collaboration, decisions, escalations, filings, and outcomes. A strong case record helps analysts work consistently and gives managers, auditors, and regulators a traceable account of what was reviewed and why.
How is case management different from alert management?
Alert management determines which signals need attention and whether they can be closed, escalated, or grouped. Case management handles the deeper investigation: linking multiple alerts or entities, gathering evidence, assigning tasks, recording rationale, coordinating approvals, and documenting the outcome. An alert may be resolved during triage or become part of a broader case; the two workflows should remain connected without being treated as identical.
What information should a fraud or AML case bring together for an investigator?
A case should present the relevant customer or business profile, accounts, transactions, KYC or KYB data, device and behavioral signals, rule or model reason codes, prior alerts and cases, linked entities, external enrichment, and communications. Every material fact should retain its source and timestamp. Access should be permissioned, and the interface should emphasize decision-relevant context rather than indiscriminately exposing more data.
When does case management become the bottleneck rather than alert detection?
Case management is the bottleneck when alerts are generated reliably but queue time, evidence gathering, handoffs, duplicate research, or review rework keeps rising. Other signals include aging cases, inconsistent dispositions, manual SAR preparation, and analysts moving between many systems. Measure both touch time and elapsed time: automation helps most when it removes repetitive work while preserving investigative quality and control.
How can AI assist investigations without inventing evidence or conclusions?
AI should work only from approved case data and policies, link material statements to source fields, distinguish facts from inferences and recommendations, and flag missing or conflicting information. Useful tasks include summarizing evidence, surfacing relationships, preparing timelines, suggesting next steps, and drafting narratives. A human should approve consequential decisions, while prompts, outputs, edits, overrides, and actions remain logged for review.
What should risk teams measure besides case-closure speed?
Measure efficiency and decision quality together: queue age by risk tier, analyst touch time, handoffs, evidence-collection time, rework, reopened cases, quality-assurance findings, escalation accuracy, override patterns, and SLA adherence. For AML, include SAR timeliness and narrative quality controls; for fraud, include prevented loss and customer impact where measurable. Faster closure is useful only when decisions remain complete, consistent, and defensible.
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