Enhancing Credit Card Fraud Predication: Machine Learning-Deep Learning Ensemble with Fingerprint Authentication

Abstract

The given project will introduce the design and
implementation of an effective credit card fraud detection system
based on the use of the combination of machine learning and deep
learning approaches. This is to find and identify with maximum
precision fraudulent transactions with minimum false positive.
To do this, the ensemble learning methods are utilized, where a
combination of strengths of several machine learning models is
used to create more trustful predictions.
Imbalance between genuine and fake data of transactions is
one of the major difficulties in detecting fraud. In order to
prevent that, data augmentation techniques are used so that
models can learn in a more efficient way the patterns using
the limited amount of fraud cases. The system also incorporates
authentication that is based on biometrics, including facial
recognition, and fingerprint confirmation in order to procure
secure transaction validation. This aspect increases the level of
trust the user has to the application and provides an important
level of security, in that the identity of the user is confirmed
before the transaction is allowed.
It is developed with Python to provide machine learning
algorithms with the help of Django which is a backend frame-
work to process server-side activities. It is achieved through
HTML, CSS, JavaScript, and Bootstrap to build the frontend
interface properly and guarantee user-friendly and responsive
work. The database management system is MySQL which stores
the transaction records and biological data in a safe manner.
The major feature of this project is the fact that the whole
system will work in the offline environment. It makes sure
that the important information such as the biometric data and
financial information is not revealed to the possible online threats.
The given solution can protect users and financial organizations
against credit card fraud because it is a convenient tool that
allows countering the threats on their own.