Credit Card Fraud Detection using OCR & Autoencoders in Keras.

In today’s world the credit card frauds are very vulnerable to each one of us. In a day all around the world millions of transactions get carried out. It is very much possible that the person doesn’t carry out the transaction still the credit is done from his/her account.

There are many ways to detect whether the transaction is a fraud or not.

In our project we detect the transaction on basis of behavior of the transaction.

The detection of fraud transactions can be used by banking companies and other Money transaction applications to inform their customers if a fraud transaction occurs and strict measures can be then taken.

It can be also considered as a safety measure taken to avoid fraud transaction.

If a fraud transaction occurs the company can immediately inform its customers and verify with them.

We are basically going to use 2 different models to optimize our output thereby maintaining our accuracy high.

Image for post

**Architecture **of our System.


OUTLINE

Module 1:

OCR- a font, a font created specifically to aid Optical Character Recognition algorithms. We’ll then devise a computer vision and image processing algorithm that can localize the four groupings of four digits on a credit card, Extract each of these four groupings followed by segmenting each of the sixteen numbers individually, Recognize each of the sixteen credit card digits by using template matching and the OCR- a font.

Image for post

So this is how Module 1 works:

1. Takes a reference image and extracts the digits.

2. Stores the digit templates in a dictionary.

3. Localizes the four credit card number groups, each holding four digits (for a total of 16 digits).

4. Extracts the digits to be “matched”.

5. Performs template matching on each digit, comparing each individual ROI to each of the digit templates 0–9, whilst storing a score for each attempted match.

6. Finds the highest score for each candidate digit, and builds a list called output which contains the credit card number.

7. Outputs the credit card number and credit card type to our terminal and displays the output image to our screen with database details .

8. Comparing with Luhn algorithm it tells whether it is valid or invalid card.

Image for post

Retrieval of Customer details from the database by OCR technique.

#fraud-detection #credit-cards #data-mining #data analysis

What is GEEK

Buddha Community

Credit Card Fraud Detection using OCR & Autoencoders in Keras.

Credit card fraud and technical solutions

Credit card fraud is an increasingly expensive problem. Technology offers solutions to help combat the problem and gain control.
How to prevent fraudulent transactions in credit cards is a common question plaguing the credit card user today. The credit card brings convenience and security to the users, but the same can become a cause of agony if the user is a victim of any credit card fraud. Smart systems are coming to the aid of credit card users and empowering them against cybercriminals. Using fraud detection tools and following some simple precautions, the users can protect themselves against credit card fraud.

#credit card fraud detection #types of credit card fraud #contactless payment systems #technological challenges in credit card fraud #prevent fraudulent transactions in credit cards

Credit Card Fraud Detection using OCR & Autoencoders in Keras.

In today’s world the credit card frauds are very vulnerable to each one of us. In a day all around the world millions of transactions get carried out. It is very much possible that the person doesn’t carry out the transaction still the credit is done from his/her account.

There are many ways to detect whether the transaction is a fraud or not.

In our project we detect the transaction on basis of behavior of the transaction.

The detection of fraud transactions can be used by banking companies and other Money transaction applications to inform their customers if a fraud transaction occurs and strict measures can be then taken.

It can be also considered as a safety measure taken to avoid fraud transaction.

If a fraud transaction occurs the company can immediately inform its customers and verify with them.

We are basically going to use 2 different models to optimize our output thereby maintaining our accuracy high.

Image for post

**Architecture **of our System.


OUTLINE

Module 1:

OCR- a font, a font created specifically to aid Optical Character Recognition algorithms. We’ll then devise a computer vision and image processing algorithm that can localize the four groupings of four digits on a credit card, Extract each of these four groupings followed by segmenting each of the sixteen numbers individually, Recognize each of the sixteen credit card digits by using template matching and the OCR- a font.

Image for post

So this is how Module 1 works:

1. Takes a reference image and extracts the digits.

2. Stores the digit templates in a dictionary.

3. Localizes the four credit card number groups, each holding four digits (for a total of 16 digits).

4. Extracts the digits to be “matched”.

5. Performs template matching on each digit, comparing each individual ROI to each of the digit templates 0–9, whilst storing a score for each attempted match.

6. Finds the highest score for each candidate digit, and builds a list called output which contains the credit card number.

7. Outputs the credit card number and credit card type to our terminal and displays the output image to our screen with database details .

8. Comparing with Luhn algorithm it tells whether it is valid or invalid card.

Image for post

Retrieval of Customer details from the database by OCR technique.

#fraud-detection #credit-cards #data-mining #data analysis

Queenie  Davis

Queenie Davis

1623360840

Credit Card Fraud Detection with Python & Machine Learning

For any bank or financial organization, credit card fraud detection is of utmost importance. We have to spot potential fraud so that consumers can not bill for goods that they haven’t purchased. The aim is, therefore, to create a classifier that indicates whether a requested transaction is a fraud.

About Credit Card Fraud Detection

In this machine learning project, we solve the problem of detecting credit card fraud transactions using machine numpy, scikit learn, and few other python libraries. We overcome the problem by creating a binary classifier and experimenting with various machine learning techniques to see which fits better.

Credit Card Fraud Dataset

The dataset consists of 31 parameters. Due to confidentiality issues, 28 of the features are the result of the PCA transformation. “Time’ and “Amount” are the only aspects that were not modified with PCA.

There are a total of 284,807 transactions with only 492 of them being fraud. So, the label distribution suffers from imbalance issues.

Please download the dataset for credit card fraud detection project: Anonymized Credit Card Transactions for Fraud Detection

Tools and Libraries used

We use the following libraries and frameworks in credit card fraud detection project.

  • Python – 3.x
  • Numpy – 1.19.2
  • Scikit-learn – 0.24.1
  • Matplotlib – 3.3.4
  • Imblearn – 0.8.0
  • Collections, Itertools

Credit Card Fraud Project Code

Please download the source code of the credit card fraud detection project (which is explained below): Credit Card Fraud Detection Machine Learning Code

Steps to Develop Credit Card Fraud Classifier in Machine Learning

Our approach to building the classifier is discussed in the steps:

  1. Perform Exploratory Data Analysis (EDA) on our dataset
  2. Apply different Machine Learning algorithms to our dataset
  3. Train and Evaluate our models on the dataset and pick the best one.
Step 1. Perform Exploratory Data Analysis (EDA)

There are a total of 284,807 transactions with only 492 of them being fraud. Let’s import the necessary modules, load our dataset, and perform EDA on our dataset. Here is a peek at our dataset:

import pandas as pdfrom collections import Counterimport itertools ## Load the csv file dataframe = pd.read_csv ( “./Desktop/DataFlair/credit_card_fraud_detection/creditcard.csv” ) dataframe.head ()

#machine learning tutorials #credit card fraud classification #credit card fraud project

Ismael  Stark

Ismael Stark

1618128600

Credit Card Fraud Detection via Machine Learning: A Case Study

This is the second and last part of my series which focuses on Anomaly Detection using Machine Learning. If you haven’t already, I recommend you read my first article here which will introduce you to Anomaly Detection and its applications in the business world.

In this article, I will take you through a case study focus on Credit Card Fraud Detection. It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. So the main task is to identify fraudulent credit card transactions by using Machine learning. We are going to use a Python library called PyOD which is specifically developed for anomaly detection purposes.

#machine-learning #anomaly-detection #data-anomalies #detecting-data-anomalies #fraud-detection #fraud-detector #data-science #machine-learning-tutorials

Dev Express

1610107146

WHAT IS A TWITTER CARD - TYPES AND USES

This is image title

The Twitter card is a facility provided by Twitter for its user to share their photos, videos, articles, blogs, and media in a more eye-catching way. The Twitter card is something that allows you to share your media beyond the limit of 280 characters, to some extent.

#twitter card #what is twitter card #types of twitter card #summary card #summary card with large image #player card