A Singapore-based fintech company specializing in digital payments, online banking security, and risk analytics partnered with Dserve AI to strengthen its fraud prevention systems. The client handled thousands of daily transactions across multiple countries and required a smarter AI model to identify suspicious behavior in real time.
Their existing fraud detection engine relied on static rules and limited datasets, leading to delayed alerts, false positives, and missed fraud patterns.
Project Objective
The client needed a high-quality financial dataset to retrain its AI system using real-world transaction behavior, suspicious activity indicators, and anomaly patterns.
Primary Goals:
- Deliver 90,000 structured transaction records
- Improve fraud detection accuracy
- Reduce false positive alerts
- Detect emerging fraud patterns faster
- Support multi-country transaction behavior
- Ensure privacy-safe data processing
- Enable faster model retraining cycles
Key Challenges
Financial fraud detection requires clean, balanced, and intelligently labeled data. The client faced multiple challenges in preparing reliable training datasets.
| Challenge | Description |
|---|---|
| Imbalanced Data | Genuine transactions heavily outnumbered fraud cases |
| Hidden Fraud Patterns | Sophisticated fraud behavior looked normal initially |
| False Positives | Legitimate users flagged incorrectly |
| Cross-Border Complexity | Different regions showed unique spending behavior |
| Data Privacy Needs | Sensitive financial data required secure handling |
| Rapid Fraud Evolution | Fraud tactics changed frequently |
Our Solution
Dserve AI created a custom fraud-detection training dataset using advanced preprocessing, risk labeling, and anomaly enrichment techniques.
What We Delivered:
- 90,000 curated transaction records
- Fraud / non-fraud classification labels
- Risk scoring attributes
- Time-series transaction behavior mapping
- Merchant category tagging
- Geographic anomaly indicators
- Duplicate and noise removal
- Secure structured delivery format
Workflow Included:
- Data cleansing and normalization
- Pattern-based fraud labeling
- Feature enrichment
- Quality validation checks
- Model-ready export pipeline
Project Impact
After retraining with the new dataset, the client saw measurable improvements in fraud prevention performance.
| Metric | Improvement |
|---|---|
| Fraud Detection Accuracy | +36% |
| False Positive Alerts | -29% |
| Suspicious Pattern Detection Speed | +33% |
| Chargeback Risk Reduction | +24% |
| Model Precision Score | +31% |
The stronger AI model delivered direct operational and financial value to the client.
Results Achieved:
- Reduced fraud-related financial losses
- Better customer trust and satisfaction
- Faster response to suspicious transactions
- Lower manual review workload
- Improved regulatory confidence
- Scalable fraud monitoring for growth markets
Dserve AI delivered a highly organized and accurate transaction dataset tailored to fraud detection needs. Their team understood fintech data complexity and helped us improve model performance quickly.
— Michael Tan, Head of Risk Analytics, Singapore
Why Dserve AI?
Businesses trust Dserve AI for secure, scalable, and accurate AI datasets.
Our Strengths:
- Fintech and banking data expertise
- High-quality annotation workflows
- Secure data handling processes
- Fast delivery timelines
- Custom dataset engineering
- Scalable enterprise operations
- AI model training support
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