---
license: mit
tags:
- fraud-detection
- explainable-ai
- trustworthy-ai
- responsible-ai
- predictive-analytics
- financial-ai
- lead-ai
datasets:
- lead-ai-labs/fraud-detection-sample-data
pipeline_tag: tabular-classification
---

# 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

```text
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.