Automating Fraud Detection for a FinTech Startup
A FinTech startup providing digital banking services faced challenges in detecting fraudulent transactions in real time, which exposed it to financial risks.
Industry
Financial Services
Services
Machine Learning-powered fraud detection system
Tools We used
Python, Scikit-learn, AWS SageMaker, Apache Kafka, FastAPI
The Problem with Existing System
Increasing Fraudulent Activities
Increasing fraudulent activities resulted in losses and poor customer trust.
Need for a Scalable AI-Powered Fraud Detection System
The startup needed a scalable AI-powered fraud detection system to process real-time transactions.
Limitations of Rule-Based Fraud Detection Methods
Existing fraud detection methods were rule-based and lacked adaptability to emerging fraud patterns.
TechKors Solution
TechKors developed a Machine Learning-powered fraud detection system that could identify suspicious transactions and flag them for review.
Implementation of Anomaly Detection Algorithms
Implemented anomaly detection algorithms using historical transaction data.
Automated Alert System for High-Risk Activities
Built an automated alert system that notified compliance teams of high-risk activities.
Deployment of Real-Time Monitoring System
Deployed a real-time monitoring system to analyze transaction velocity, geolocation, and user behavior.
Results
Improvement in Fraud Detection
Enhancing Customer Experience
Enhanced Compliance, Lower Legal Risks
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We do a discovery and consulting meeting
We prepare a proposal