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

01

Increasing Fraudulent Activities

Increasing fraudulent activities resulted in losses and poor customer trust.

02

Need for a Scalable AI-Powered Fraud Detection System

The startup needed a scalable AI-powered fraud detection system to process real-time transactions.

03

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.

01

Implementation of Anomaly Detection Algorithms

Implemented anomaly detection algorithms using historical transaction data.

02

Automated Alert System for High-Risk Activities

Built an automated alert system that notified compliance teams of high-risk activities.

03

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

Fraudulent transaction detection improved by 92%, reducing financial losses.

Enhancing Customer Experience

False positives decreased by 40%, improving customer experience.

Enhanced Compliance, Lower Legal Risks

Regulatory compliance improved, reducing potential fines and legal risks
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