Global Trust Bank - 85% Reduction in Fraud Losses

About Global Trust Bank

Global Trust Bank is a leading international financial institution with $450 billion in assets, serving 28 million customers across 42 countries. The bank processes over 50 million transactions daily across digital, mobile, ATM, and branch channels. As fraud became increasingly sophisticated with criminals using AI and advanced techniques, the bank faced mounting losses and frustrated customers from false positive alerts. Traditional rule-based fraud detection systems could not keep pace with evolving threats, creating an urgent need for AI-powered fraud prevention.

$450B

Assets

28M

Customers

50M+

Daily Transactions

The Challenge

Sophisticated Fraud Schemes

Criminals using AI and advanced techniques to evade traditional rule-based systems

⚠️ $56M in annual fraud losses

High False Positive Rates

Legacy systems flagging legitimate transactions, frustrating customers and overwhelming teams

⚠️ 3.2% of valid transactions blocked

Real-Time Detection Gap

Batch processing meant fraud was often detected hours or days after the damage

⚠️ 18-hour average detection time

Cross-Channel Blind Spots

Fraudsters exploiting gaps between online, mobile, ATM, and branch channels

⚠️ 67% of fraud crossed channels

The AI Solution

Neural Network Fraud Detection

Deep learning models analyze patterns across millions of transactions in real-time

Behavioral biometrics
Anomaly detection
Pattern recognition

Cross-Channel Intelligence

Unified view of customer activity across all banking channels and touchpoints

360-degree customer view
Real-time data fusion
Channel correlation

Adaptive Risk Scoring

Dynamic risk assessment that evolves with emerging fraud patterns

Self-learning algorithms
Threat intelligence integration
Predictive modeling

Explainable AI Dashboard

Transparent fraud decisions with clear explanations for investigators

Decision transparency
Investigation tools
Compliance reporting

Implementation Journey

1. Infrastructure & Integration

4 weeks

Deployed high-performance computing infrastructure
Integrated with core banking systems and data lakes
Established real-time streaming data pipelines
Connected to global fraud intelligence networks

2. Model Training & Validation

6 weeks

Trained models on 5 years of transaction history
Analyzed 2.8 billion historical transactions
Validated against known fraud patterns
Stress-tested with synthetic fraud scenarios

3. Pilot Deployment

8 weeks

Launched pilot with 10% of transaction volume
Parallel run with existing fraud systems
Fine-tuned detection thresholds
Trained fraud investigation teams

4. Global Rollout

6 weeks

Scaled to process 50M+ daily transactions
Deployed across 42 countries
Integrated with mobile and digital channels
Established 24/7 monitoring center

Transformational Results

Metric	Before AI	After AI	Improvement
Card-Not-Present Fraud Real-time behavioral analysis catches anomalies instantly	$18.3M annual losses	$2.1M annual losses	89% reduction
Account Takeover Fraud Biometric and behavioral patterns identify compromised accounts	$15.2M annual losses	$1.8M annual losses	88% reduction
False Positive Rate Customers experience fewer disruptions, satisfaction up 34%	3.2% of transactions	1.2% of transactions	62% reduction
Detection Speed Fraud stopped before money leaves the account	18 hours average	Real-time (< 1 second)	100% improvement

What the Team Says

“Ademero's AI transformed our fraud prevention from reactive to predictive. We're stopping fraud we never would have detected before, and our customers notice the difference - fewer false declines, faster transactions, and complete peace of mind.”

Sarah Chen

Chief Risk Officer

“The explainable AI feature is a game-changer for our investigators. They can see exactly why the system flagged a transaction, making their work more efficient and helping us meet regulatory requirements for model transparency.”

Michael Rodriguez

VP, Fraud Operations

“We went from catching fraud after the fact to preventing it in real-time. The cross-channel intelligence finds patterns that would be impossible for humans to spot across millions of daily transactions.”

Jennifer Park

Director of Digital Banking Security

Key Lessons Learned

💡 Real-Time is Non-Negotiable

Batch fraud detection is obsolete - modern fraud requires real-time AI analysis

💡 Cross-Channel View Essential

Fraudsters exploit channel silos - unified customer view is critical

💡 Explainability Builds Trust

Investigators and regulators need to understand AI decisions - black boxes don't work in banking

💡 Continuous Learning Required

Fraud patterns evolve daily - static models become obsolete quickly

85% Reduction in Fraud Losses