AI Fraud Detection for Real-Time Bank Transactions
Detect suspicious transactions, reduce false positives, and help fraud teams recheck risky payments with explainable AI.
Live Risk Console
Transaction recheck queue
87
Risk score
24
Open alerts
04m
SLA
TXN-1048
LKR 250,000
New beneficiary + new device
TXN-1082
LKR 12,500
Normal behavior
TXN-1120
LKR 890,000
Velocity + foreign IP
Recommended action
HOLD_FOR_REVIEW
Officer checklist prepared from verified fraud signals.
Built for modern banks, fintechs, and payment teams
Fraud teams are overloaded. Rules alone are not enough.
Too many false positives
Manual review takes too long
New fraud patterns appear quickly
Customers get blocked incorrectly
Fraud decisions are hard to explain
One platform for detection, scoring, and rechecking
FraudShield AI combines rules, machine learning, transaction behavior analysis, and an optional LLM review assistant in one operational workflow.
Real-time fraud scoring
Score each transaction through rules, velocity checks, and model predictions before risky payments move forward.
Explainable decision reasons
Give fraud officers reason codes, risk bands, and recommended actions that are audit-ready.
Officer review assistant
Use optional LLM summaries to prepare checklists and notes without giving the LLM decision authority.
Built for high-volume fraud operations
Real-time transaction scoring
Return risk scores and actions through API-ready decisions.
XGBoost-based fraud prediction
Use model-driven risk prediction alongside transparent controls.
Rule engine with reason codes
Trigger clear explanations for suspicious transaction patterns.
Customer behavior profiling
Compare transactions against recent customer behavior baselines.
Device and beneficiary risk checks
Flag new devices, new payees, and elevated counterparties.
Manual review workflow
Route high-risk transactions to officers with context and notes.
From transaction to decision in milliseconds
Transaction received
Rules and velocity checks run
ML model predicts risk
Risk score and reasons generated
Low-risk auto-approved
High-risk sent to review dashboard
A review dashboard your fraud team can actually use
FraudShield Console
Risk operations dashboard
128K
Total transactions
1,842
High-risk transactions
LKR 91M
Fraud prevented
31%
False positive reduction
Risk distribution
Recent flagged transactions
| Transaction | Amount | Score | Action |
|---|---|---|---|
| TXN-1120 | LKR 890,000 | 94 | BLOCK |
| TXN-1048 | LKR 250,000 | 87 | HOLD_FOR_REVIEW |
| TXN-1194 | LKR 74,200 | 66 | STEP_UP_VERIFY |
| TXN-1202 | LKR 18,800 | 42 | STEP_UP_VERIFY |
Explainable risk scores your fraud team can trust
Every decision returns a score, risk level, reason codes, and recommended action so officers can understand why a transaction moved into review.
{
"riskScore": 87,
"riskLevel": "HIGH",
"reasons": [
"Amount is 7x higher than customer average",
"New beneficiary detected",
"New device used",
"Multiple transactions in 5 minutes"
],
"recommendedAction": "HOLD_FOR_REVIEW"
}LLM-assisted reviews without giving the LLM control
The LLM does not approve or reject transactions. It summarizes verified fraud signals, prepares a review checklist, and helps officers write audit notes.
Officer review summary
Generated only from verified signals
Summary
This transaction is high risk because the customer is sending a large amount to a new beneficiary from a new device.
Checklist
Cover the fraud patterns that slow teams down
Account takeover detection
New beneficiary fraud
Mule account detection
Duplicate transaction detection
Card-not-present fraud
High-value transfer review
Designed for auditability and bank-grade controls
Role-based access control
Full audit logs
Model version tracking
Human-in-the-loop review
No black-box final decisions
Data privacy-first architecture
Built for fraud teams that need clarity
“FraudShield helped our review team prioritize the riskiest transactions first.”
Anika Perera
Head of Digital Risk, fictional regional bank
“The reason codes made internal fraud reviews faster and easier.”
Marcus Silva
Fraud Operations Lead, fictional payments network
“The platform reduced unnecessary manual checks while keeping high-risk cases visible.”
Nadia Wijesinghe
Risk Transformation Manager, fictional fintech
Start with an MVP, scale into real-time protection
MVP Pilot
For pilots and proof-of-concepts
Start PilotGrowth Bank
For teams moving to real-time operations
Start PilotEnterprise
For regulated financial institutions
Contact SalesQuestions fraud and technology teams ask first
01 Can it connect to our existing core banking system?+
Yes. FraudShield AI is designed around API-based transaction scoring and can be integrated with core banking, mobile banking, or payment switch flows.
02 Can we start with CSV data?+
Yes. MVP pilots can begin with CSV transaction history to validate risk signals and review workflows before real-time integration.
03 Is the model explainable?+
The platform returns reason codes, risk levels, model versions, and action recommendations to support audit-ready decisions.
04 Can officers override decisions?+
Yes. Human-in-the-loop review supports officer decisions, notes, and audit trails.
05 Can we deploy on-premise?+
Enterprise deployments can be designed for private cloud or on-premise requirements.
06 What data is needed for an MVP?+
Transaction amount, channel, merchant or transfer category, customer history, device signals, beneficiary signals, velocity counts, and confirmed fraud labels are helpful.
Ready to modernize fraud detection?
Launch a pilot with your transaction data and see risk scores, reason codes, and review workflows in action.