Lead.AI Fraud Detection XAI Model

The Lead.AI Fraud Detection XAI Model is a prototype machine learning model designed for fraud-risk prediction, explainable AI, and trustworthy decision-support research.

This project is part of Lead.AI Labs, an applied AI initiative focused on building transparent, scalable, and practical AI systems for finance, business automation, and decision intelligence.

Model Purpose

This model demonstrates how machine learning can support fraud-risk scoring while keeping the decision process understandable to users, analysts, and business stakeholders.

Key Features

• Fraud-risk classification: Low, Medium, and High
• Explainable decision logic
• Tabular financial transaction analysis
• Decision-support system design
• Responsible AI and transparency focus
• Research and portfolio demonstration for applied machine learning

Intended Use

This model is intended for:

• Fraud detection research
• Explainable machine learning demonstrations
• AI decision-support prototypes
• Financial technology learning projects
• Lead.AI product development experiments
• Academic and professional portfolio use

Dataset

This model is connected to:

lead-ai-labs/fraud-detection-sample-data

The dataset contains synthetic or sample transaction-style records designed for fraud detection demonstrations. It does not include private banking data, real customer data, or personally identifiable information.

Example Inputs

The model is designed around transaction-style features such as:

• Transaction amount
• Transaction time
• Account age
• Merchant or transaction category
• Previous chargeback activity
• Behavioral risk indicators

Example Output

Prediction: High Fraud Risk

Explanation:
The transaction received a high-risk score because the amount was unusually large, the transaction occurred at an unusual hour, and the account had previous chargeback activity.