UC22: Fraud Investigation Case Assembly

The Problem

Banks operate fraud detection systems (card fraud, account takeover, synthetic identity, wire fraud, check fraud) that generate alerts. Fraud investigators assess alerts and build investigation case files: transaction data, customer history, device/IP information, prior fraud alerts, behavior patterns, evidence of fraud. Current process: fraud investigator manually gathers data from multiple systems, creates case file, generates preliminary fraud assessment, initiates investigation. Time per case: 1-2 hours to assemble evidence, 3-5 hours to investigate and conclude. Volume: 100-500 fraud alerts/day requiring investigation. Many alerts are false positives (fraud system has high sensitivity). Backlog of unresolved cases delays investigation and blocks account resolution (customer unable to use account pending fraud clearance).

What the Agent Does

Receives fraud alert with alert reason (card fraud, account takeover, etc.) and trigger (transaction amount, velocity, location, merchant type)
Gathers transaction data: flagged transaction details, recent transaction history (prior 30/60/90 days), transaction corridors, merchants
Gathers customer data: customer profile, account tenure, prior fraud alerts, customer behavior baselines (typical transaction patterns, devices, locations)
Gathers device/IP intelligence: device fingerprint of fraud transaction, prior devices used by customer, geolocation, prior IP addresses associated with customer account
Performs fraud risk assessment: score based on deviation from customer baseline, transaction type risk, device/IP risk, prior fraud history
Generates fraud case file: alert summary, transaction/customer/device data, fraud risk assessment, preliminary recommendation (likely fraud, likely false positive, investigation required)
Routes to fraud investigator with case file for investigation
Tracks case disposition (confirmed fraud, false positive, unable to determine) for fraud detection model tuning

Data Requirements

Data Sources:

Fraud Detection System (Kount, Falcon, Actimize Fraud, or custom) , Fraud alerts, alert reason codes, trigger data (transaction amount, velocity, merchant), fraud score
Core Banking System / Payment Platform (Temenos T24, Finastra) , Customer profile, account tenure, account status, account restrictions
Transaction Database / Data Warehouse , Flagged transaction details, recent transaction history (30/60/90 days), transaction amounts, merchants, corridors, transaction patterns
Device Intelligence System (ThreatMetrix, Kount, or custom) , Device fingerprinting, device ID, IP address, geolocation, prior devices used by customer
Customer Behavioral Database (internal) , Baseline customer transaction patterns, typical merchants, typical geographies, typical transaction amounts, devices used
Fraud Alert History Database , Prior fraud alerts for customer, prior fraud confirmed, fraud loss amounts, fraud patterns
Fraud Investigation System (Actimize, Kount, or custom Case Management) , Case status, investigator queue, prior case dispositions

Data Classification:

Public: None
Internal Structured: Fraud alerts, transaction data, customer profile, device data, behavioral patterns, fraud history, case status
Internal Unstructured: Fraud investigator notes, case narratives, dispute correspondence
External Third-Party: Device intelligence provider data (ThreatMetrix, Kount), third-party fraud signals
Sensitive/Regulated: Customer personal data, transaction details, account information, fraud determination (sensitive reputational impact for customer if incorrectly accused)

Data Quality Requirements:

Fraud alert completeness: 99%+ of fraud alerts captured. Transaction data completeness: 100% for flagged transaction and 30-day history. Customer profile accuracy: 100% (match core banking records). Device data freshness: real-time or T+0 from device intelligence system. Behavioral baseline accuracy: 95%+ (derived from historical transaction analysis). Fraud case data accuracy: 100% (historical fraud confirmation data must be accurate for model training).

Integration Complexity: High , Requires real-time integration with fraud detection system (Kount, Falcon, Actimize) for alert ingestion. Customer data aggregation from multiple systems (core banking, transaction database, behavioral database). Device intelligence system integration (ThreatMetrix, Kount, custom fingerprinting). Historical fraud case data may be scattered across multiple systems/archives. Fraud investigator workflow integration. Sensitive data handling required (customer PII, fraud determinations). Model tuning feedback loop requires tracking case dispositions and updating fraud detection rules.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	2	0.20
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	3	0.15
Data Availability	15%	4	0.60
Process Clarity	15%	3	0.45
Ease of Implementation	10%	2	0.20
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	5	0.50
Composite	100%		3.25

Why It Scores Well

Details to be provided.

Regulatory Alignment

Reg E (Electronic Funds Transfer): Agent assists in fraud investigation and remediation within regulatory timelines
GLBA Safeguards Rule: Agent handles sensitive fraud data with appropriate controls
Wire Fraud Prevention: Agent supports wire fraud investigation and prevention

Sprint Factory Fit

4 build sprints (requires fraud governance and sensitive data handling)

Build Sprints 1-4: Fraud alert system integration, transaction data aggregation, customer data enrichment, device/IP intelligence integration, fraud risk scoring, case file generation, investigator workflow, sensitive data governance

Comparable Implementations

Feedzai: Fraud investigation case assembly; 80% faster case preparation for investigators
Kount: Fraud case intelligence; provides investigator with comprehensive risk and evidence summary

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