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Fraud Detection in Banking Data by Machine Learning Technique
Fraud Detection in Banking Data by Machine Learning Technique
Fraud Detection in Banking Data by Machine Learning Technique

Fraud Detection in Banking Data by Machine Learning Technique

₹5,500.00 ₹4,000.00
Product Code: Python - Cybersecurity
Availability: In Stock
Viewed 1988 times

Product Description

Aim:

          The aim is to leverage the power of machine learning to create efficient and accurate fraud detection systems that protect the interests of both financial institutions and their customers.


Abstract:

          As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weight-tuning hyper- parameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyper parameters while preserving practical issues such as unbalanced data. We propose weight tuning as a pre-process for unbalanced data, as well as Cat Boost and XG Boost to improve the performance of the Light GBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyper parameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. Cat Boost, Light GBM, and XG Boost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. Light GBM and XG Boost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results


Proposed System:

        Proposed approach to detecting credit card fraud using  a RFE (Recursive Feature Elimination) this feature selection method used to select the best feature for a dependent variable and also we used oversampling and then using a machine learning algorithm its give a best accuracy.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.