Fraud analytics toolbox
Practical tools for banking fraud, AI risk, payment scams, and financial-crime analytics.
Use this page as a working library for checklists, signal maps, KPI ideas, dashboards, investigation workflows, and architecture references. Every resource points readers toward a practical next step rather than a chronological article feed.
Resource cards
Choose the tool that matches the fraud problem you are working on.
APP Fraud Risk Signal Checklist
A field-style checklist for sender behavior, recipient and mule account risk, scam story cues, payment journey controls, and case intake questions.
Who it is for: Fraud analysts, payment-risk teams, branch/call-center escalation teams, and scam operations leaders.
Fraud Analytics KPI Checklist
A KPI planning guide for loss, alerts, detection quality, false positives, case workflow, recovery, customer friction, and model governance.
Who it is for: Fraud analytics teams, risk reporting owners, dashboard builders, and managers setting operating metrics.
Money Mule Detection Guide
A practical guide to mule-account indicators across onboarding, dormancy, transaction velocity, recipient networks, rapid funds-out movement, and graph analytics.
Who it is for: Payment fraud teams, AML partners, transaction monitoring analysts, scam operations leaders, and account-risk investigators.
FedNow Fraud Controls Matrix
A control-mapping reference for instant payment risk, including sender friction, receiver intelligence, account thresholds, escalation, and review timing.
Who it is for: Instant-payment teams, fraud strategy groups, bank risk leaders, and operational control owners.
AI-Generated Identity Fraud Guide
A practical identity-risk guide for deepfake IDs, synthetic documents, liveness checks, digital onboarding controls, KYC lifecycle monitoring, and mule-account links.
Who it is for: Digital onboarding teams, KYC owners, fraud strategy groups, identity risk analysts, and financial crime leaders.
AI Fraud Governance Checklist
A governance checklist for explainability, model monitoring, human review, documentation, bias checks, alert quality, and safe AI-assisted investigation.
Who it is for: AI risk teams, fraud model owners, analytics leaders, compliance partners, and investigation workflow designers.
Scam Intake Questions Template
A practical question framework for identifying coercion, urgency, relationship claims, investment narratives, remote-access pressure, and payment journey clues.
Who it is for: Call-center teams, fraud intake specialists, branch teams, investigators, and customer-protection programs.
Account Takeover Signal Checklist
A signal checklist for suspicious login behavior, device shifts, profile changes, credential abuse, payment initiation, and graph-based relationship patterns.
Who it is for: Digital banking fraud teams, cybersecurity partners, graph analytics teams, and account security investigators.
Fraud Analyst SQL Starter Pack
A starter path for common analyst queries: alert volume, case aging, transaction velocity, customer/device pivots, losses, recoveries, and false positives.
Who it is for: Fraud analysts, BI developers, junior analytics hires, and teams standardizing investigation datasets.
Fraud Dashboard KPI Template
A dashboard planning reference for executive, operations, model monitoring, and investigator views of fraud performance.
Who it is for: Fraud reporting owners, analytics managers, BI teams, and risk leaders who need a cleaner metric hierarchy.
Real-Time Fraud Detection Architecture Diagram
A reference architecture path for streaming events, feature pipelines, decisioning, case queues, feedback loops, and human review.
Who it is for: Data engineers, fraud platform owners, architects, analytics leaders, and real-time decisioning teams.
Identity Fraud Guide Set
A reader path for synthetic identity, account takeover, first-party fraud, and graph-based account risk.
Who it is for: Digital banking fraud teams, identity risk analysts, account security teams, and financial-crime analytics groups.
AI Voice Cloning Scam Guide
A guide path for synthetic voice scams, customer pressure tactics, AI-enabled deception, and fraud review escalation.
Who it is for: Scam operations teams, customer-protection groups, AI risk teams, and fraud analysts reviewing social engineering cases.
Reader paths
Need a starting point?
Start with the live checklist if you are working on APP fraud, use the topic hubs if you are researching a broader problem, or subscribe for new templates as the resource library grows.
