Banking & Financial Services
Agentic AI Fraud Detection & Prevention
4 autonomous agents orchestrated end-to-end. 40% faster detection, $12M saved annually.
Agentic AI Workflow
Vijan.AI Forge orchestrates 4 autonomous agents with tool calls & feedback loops
The Challenge
Legacy rule-based systems couldn't keep up with evolving fraud tactics
A leading bank processing 50M+ transactions daily was facing a critical fraud detection crisis. Their rule-based system, built on static thresholds and manual pattern updates, was generating a 70% false positive rate, overwhelming their team of 200 fraud investigators and causing legitimate transactions to be blocked.
Meanwhile, sophisticated fraud attacks were slipping through. Card-not-present fraud had increased 35% year-over-year, and the average detection time was 4.2 hours, by which point funds had already been moved. The bank was losing $20M annually to fraud, with investigators spending 80% of their time on false alarms instead of real threats.
The bank needed a solution that could analyze transactions in real-time, drastically reduce false positives, and automate the entire fraud lifecycle, from detection to resolution.
The Solution
An agentic AI system, not a static pipeline
Vijan.AI deployed a fully autonomous fraud detection system built on agentic AI principles. Vijan.AI Forge, our agent orchestration platform, coordinates 4 specialized agents, each capable of autonomous reasoning, tool calling (ML models, rules engines, graph databases, messaging APIs), and decision routing based on risk scores. A feedback loop from resolved cases continuously retrains detection models. The system gets smarter with every transaction it processes.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Detection
Fraud Detector
Autonomous Transaction Analysis
Autonomously reasons over every transaction in real-time. Calls ML scoring model and rules engine tools to evaluate 200+ features, then independently decides risk level and routes to the orchestrator.
Input
Raw transaction data (amount, merchant, location, device, timestamp)
Output
Risk score (0-100) with confidence level and anomaly flags
- Calls ML Model tool for pattern recognition across 200+ features
- Calls Rules Engine tool for geo-velocity and impossible travel checks
- Autonomous decision: assigns risk score without human input
- Routes high-risk (>75) to orchestrator for Alert Agent dispatch
Step 2 · Alerting
Fraud Alert Agent
Autonomous Triage & Notification
Receives high-risk flags from the orchestrator and decides severity, notification channel, and whether to block the transaction, calling SMS, push notification, and card-block APIs independently.
Input
Risk score, anomaly flags, transaction context
Output
Categorized alert with severity level, assigned investigator, customer notification
- Autonomous decision: classifies severity (Critical/High/Medium/Low)
- Calls SMS/Push API tool for instant customer verification request
- Calls Card Block API tool to freeze card for critical transactions
- Routes categorized alert to Ops Agent via orchestrator
Step 3 · Investigation
Fraud Operations Agent
Autonomous Case Investigation
Builds investigation cases by calling graph database and case management tools. Links related alerts, identifies fraud rings across accounts, and assembles evidence packages, replacing hours of manual analyst work.
Input
Categorized alerts, linked account data, historical patterns
Output
Investigation case with evidence package, recommended actions, timeline
- Calls Graph DB tool for network analysis to identify fraud rings
- Calls Case Management tool to create unified investigation cases
- Autonomous reasoning: prioritizes by financial exposure
- Generates evidence package and routes to Dispute Agent
Step 4 · Resolution
Dispute Resolution Agent
Autonomous Resolution & Recovery
Once fraud is confirmed, autonomously files chargebacks with card networks, generates SAR reports for regulatory compliance, and initiates account recovery. Feeds resolved cases back to the orchestrator to retrain detection models.
Input
Confirmed fraud case, chargeback request, customer account details
Output
Processed chargeback, recovered funds, updated customer account, closure report
- Calls Chargeback API tool to file with Visa/Mastercard networks
- Calls SAR Filing tool for regulatory reporting when thresholds met
- Autonomous decision: selects recovery actions per case type
- Feedback loop: sends resolved cases to retrain ML models
Results
Measurable impact within 90 days of deployment
Fraud Catch Rate
Up from 92% with the legacy rule-based system. Near-perfect detection across all transaction types including card-not-present and account takeover.
Reduction in False Positives
False positive rate dropped from 70% to 28%, freeing investigators to focus on genuine threats and reducing customer friction.
Faster Detection
Average detection time reduced from 4.2 hours to under 15 seconds. Real-time analysis catches fraud before funds leave the account.
Annual Fraud Losses Prevented
Combined savings from prevented fraud ($8M), reduced investigation costs ($2.5M), and fewer chargebacks ($1.5M).
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
Ready to deploy autonomous agents for your use case?
Let's design an agentic AI solution tailored to your organization's workflows.