Fraud AI practice library32
DesignSTR-01
Fraud AI transformation thesis
Define how AI will improve detection, investigation quality, consistency, investigator effectiveness, and verified loss avoidance while protecting legitimate customers.
PilotSTR-02
Fraud authority and prohibited conduct map
Map investigative authority, fraud statutes, required reporting, permitted methods, prohibited conduct, escalation, and evidence handling by jurisdiction and line.
OperateSTR-03
Use case outcome and boundary
State users, fraud types, populations, decisions supported, exclusions, autonomy, evidence, human authority, measures, and unacceptable outcomes.
ImproveSTR-04
Portfolio sequencing and dependency plan
Sequence initiatives using value, harm, readiness, data, platform, vendor, workforce, jurisdiction, and change dependencies.
DesignDET-01
Fraud signal intake and quality
Ingest claim, policy, billing, application, payment, identity, provider, device, third party, and network signals with quality and permitted use controls.
PilotDET-02
Alert generation and prioritization
Generate explainable alerts using calibrated thresholds, segment specific performance, uncertainty, workload capacity, and monitored exceptions.
OperateDET-03
Entity link and network analysis
Reveal relationships among people, providers, vehicles, properties, devices, accounts, events, and transactions without treating association as proof.
ImproveDET-04
Duplicate anomaly and pattern detection
Detect repeated, unusual, inconsistent, or coordinated activity using validated baselines, time windows, peer groups, and review standards.
DesignINV-01
Evidence provenance and case file integrity
Preserve source, time, collection method, transformations, chain of custody, access, conflicts, and the distinction between fact, inference, and allegation.
PilotINV-02
Lead development and investigation planning
Recommend proportionate investigative steps, evidence needs, contacts, specialists, priorities, and stopping criteria for authorized review.
OperateINV-03
Document image and voice analysis
Analyze documents, images, video, audio, metadata, and generated content within validated scope, authenticity limits, and human review requirements.
ImproveINV-04
External source corroboration
Use external records and databases through approved access, relevance, reliability, privacy, licensing, documentation, and correction controls.
DesignACT-01
Suspicion scoring and uncertainty
Estimate suspicion or referral priority with calibrated uncertainty and supporting factors rather than presenting a score as proof of fraud.
PilotACT-02
Special investigation referral and acceptance
Apply consistent referral criteria, document acceptance or rejection, route urgent matters, and monitor quality, capacity, and disparities.
OperateACT-03
Adverse action safeguard
Prevent an alert or model score from automatically delaying, denying, terminating, pricing, or otherwise adversely affecting a person without authorized review.
ImproveACT-04
Regulatory and law enforcement referral controls
Control reporting decisions, legal review, evidence standards, required approvals, transmission, confidentiality, documentation, and follow up.
DesignHUM-01
Investigator authority and accountability
Define which actions AI may perform, recommend, draft, or never take and which investigator or claim professional remains responsible.
PilotHUM-02
Review override and escalation
Provide accessible evidence, documented override, expert consultation, management review, pause, and escalation with sufficient authority.
OperateHUM-03
Bias and disparity assessment
Test alert, referral, investigation, closure, and adverse outcome differences by relevant segments and investigate plausible proxy or process causes.
ImproveHUM-04
Communication correction and recourse
Provide accurate communications, data correction, human review, complaint, appeal, and other recourse without compromising legitimate investigations.
DesignTEC-01
Fraud data quality and lineage
Trace all source and derived data through alerts, case records, decisions, referrals, outcomes, retention, sharing, and deletion.
PilotTEC-02
Model and agent validation
Test precision, recall, calibration, robustness, fairness, prompts, retrieval, tools, autonomy, generated content, and failure modes by fraud context.
OperateTEC-03
Tool access security and resilience
Control identity, permissions, secrets, prompt injection, data leakage, tool actions, logging, downtime, recovery, rollback, and safe failure.
ImproveTEC-04
Vendor service and evidence controls
Align providers on scope, data, performance, fairness, security, changes, incidents, audit evidence, continuity, subcontractors, and exit.
DesignOPS-01
Production performance monitoring
Monitor precision, recall, calibration, drift, latency, availability, alert volume, tool failures, thresholds, security events, and remediation.
PilotOPS-02
Investigation quality and outcome review
Review evidence quality, investigation proportionality, referral decisions, closures, recoveries, avoidance, fairness, complaints, and customer outcomes.
OperateOPS-03
Workload leakage and behavior monitoring
Measure alert volume, queues, aging, rework, workarounds, overreliance, override quality, investigator capacity, and displacement of work.
ImproveOPS-04
Complaint incident and litigation feedback
Connect defects, complaints, disputes, litigation, regulator inquiries, vendor events, and near misses to root cause, remediation, and change control.
DesignGOV-01
Fraud AI governance and change control
Set ownership, risk tier, approvals, evidence, monitoring, incidents, material change triggers, restrictions, and retirement decisions.
PilotGOV-02
Privacy fairness and jurisdiction review
Evaluate surveillance limits, privacy, unfair discrimination, claims practices, explanation, record, reporting, accessibility, and jurisdiction requirements.
OperateGOV-03
Recovery capacity and workforce realization
Connect AI use to verified recovery or avoidance, investigation quality, customer outcomes, capacity, skills, workload, and total operating cost.
ImproveGOV-04
Closed case learning and continuous improvement
Use closed case review, false positive analysis, complaints, litigation, referrals, recoveries, misses, incidents, and emerging schemes to improve or retire systems.