AI-Based Financial Fraud Detection Framework
Budget: $30 – $250 USD
Proposed Paper Title:
“AI-Driven Fraud Detection in Financial Transactions Using Hybrid Machine Learning and Cloud-Based Data Pipelines”
Abstract Direction:
This paper proposes a hybrid machine learning framework combining deep learning and anomaly detection algorithms to identify fraudulent transactions in real time.
It leverages cloud-based data architectures (AWS, Azure, or GCP) for scalable processing and integrates generative AI models for adaptive fraud pattern recognition.
The research focuses on:
Enhancing the accuracy and speed of fraud detection systems in high-volume banking environments.
Building a cost-efficient, cloud-native fraud monitoring pipeline using technologies such as Python, SQL, Apache Kafka, and AWS Lambda.
Introducing a self-learning feedback mechanism that adapts to new fraud trends without human intervention.
Demonstrating improvements such as a 35–50% reduction in false positives and significant improvement in real-time fraud response.
This framework addresses critical challenges faced by financial institutions and supports U.S. national goals in financial stability, data integrity, and cyber risk mitigation.
Key Methodology Components:
Data Collection & Preprocessing – Simulate or use anonymized bank transaction data (Kaggle, IEEE DataPort, or proprietary datasets).
Feature Engineering – Identify behavioral and transactional anomalies.
Modeling Techniques – Combine:
Random Forest or XGBoost (for structured fraud classification)
Autoencoder or LSTM (for time-series anomaly detection)
Generative AI (for synthetic fraud pattern generation)
System Architecture – Implement a real-time fraud detection pipeline on cloud (AWS or Azure).
Evaluation Metrics – Precision, Recall, F1-score, and AUC-ROC to measure model performance.
“AI-Driven Fraud Detection in Financial Transactions Using Hybrid Machine Learning and Cloud-Based Data Pipelines”
Abstract Direction:
This paper proposes a hybrid machine learning framework combining deep learning and anomaly detection algorithms to identify fraudulent transactions in real time.
It leverages cloud-based data architectures (AWS, Azure, or GCP) for scalable processing and integrates generative AI models for adaptive fraud pattern recognition.
The research focuses on:
Enhancing the accuracy and speed of fraud detection systems in high-volume banking environments.
Building a cost-efficient, cloud-native fraud monitoring pipeline using technologies such as Python, SQL, Apache Kafka, and AWS Lambda.
Introducing a self-learning feedback mechanism that adapts to new fraud trends without human intervention.
Demonstrating improvements such as a 35–50% reduction in false positives and significant improvement in real-time fraud response.
This framework addresses critical challenges faced by financial institutions and supports U.S. national goals in financial stability, data integrity, and cyber risk mitigation.
Key Methodology Components:
Data Collection & Preprocessing – Simulate or use anonymized bank transaction data (Kaggle, IEEE DataPort, or proprietary datasets).
Feature Engineering – Identify behavioral and transactional anomalies.
Modeling Techniques – Combine:
Random Forest or XGBoost (for structured fraud classification)
Autoencoder or LSTM (for time-series anomaly detection)
Generative AI (for synthetic fraud pattern generation)
System Architecture – Implement a real-time fraud detection pipeline on cloud (AWS or Azure).
Evaluation Metrics – Precision, Recall, F1-score, and AUC-ROC to measure model performance.