scholarly journals Predicting Credit Card Approval of Customers Through Customer Profiling using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 552-557
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Credit Card ◽  
Banking Sector ◽  
Current Trend ◽  
Credit Cards ◽  
Credit Score ◽  
Accuracy Ratio ◽  
Customer Profiling ◽  
Learning Machine

In the banking sector, every banking infrastructure contains an enormous dataset for customers’ credit card approval which requires customer profiling. The customer profiling means collection of data related to what customers need. It depends on customers’ basic information like field of work, address proof, credit score, salary details, etc. This process mainly concentrates on predicting approval of credit cards to customers using machine learning. Machine Learning is the scientific study of algorithms and statistical models that computers use to perform specific tasks without any external instructions or interference. In the current trend this process is possible using many algorithms like “K-Mean, Improved K-Mean and Fuzzy C-Means”. This helps banks to have an high profitability to satisfy their customers. However, the currently prevailing system shows an accuracy percentage of about 98.08%. The proposed system aims at improvising the accuracy ratio while using only few algorithms.

scholarly journals DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

InterConf ◽  
2021 ◽  
pp. 393-403
Author(s):  
Olexander Shmatko ◽  
Volodimir Fedorchenko ◽  
Dmytro Prochukhan
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Credit Card ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Credit Cards ◽  
Internet Banking ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

Diffusion and Adoption of Credit Cards in Indian Banking Sector

2018 ◽  
Vol 1 (2) ◽  
pp. 1-20
Author(s):  
Dr. Mandeep Kaur ◽  
Dr.Kamalpreet Kaur
Keyword(s):  
Credit Card ◽  
Banking Sector ◽  
Credit Cards ◽  
Annual Reports ◽  
Banking Services ◽  
Time Period ◽  
Dichotomous Variable ◽  
Reserve Bank ◽  
The Status ◽  
Reserve Bank Of India

The study emphasizes on the identification of factors, which may have influenced the banks to adopt credit cards along with their traditional banking services. Bank specific variables were investigated to deepen the understanding on the diffusion and adoption of credit cards. The data relating to sampled banks’ characteristics have been collected from database of Reserve Bank of India. To know about the status of the bank regarding its adoption of credit card, the websites and annual reports of the banks were explored during different intervals of time period of the study. The study considers the dependent variable i.e. adoption of credit cards as dichotomous variable, whether or not a bank renders the credit card services, denoting 1 if the bank has adopted credit card otherwise 0. The logistic regression has thus been applied to get the valid and reliable results. The empirical findings reveal that, size, non-interest income, non performing assets, profitability, age and market share of the bank are the variables which have contributed significantly in the diffusion and adoption of credit cards.

A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies

2021 ◽  
pp. 969-996
Author(s):  
Addepalli V. N. Krishna ◽  
Shriansh Pandey ◽  
Raghav Sarda
Keyword(s):  
Genetic Algorithm ◽  
Cyber Security ◽  
Credit Card ◽  
Banking Sector ◽  
Predictive Analytics ◽  
Security And Privacy ◽  
Credit Score ◽  
User Data ◽  
Identity Based ◽  
Credit Card Usage

In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.

scholarly journals Do Banks Pass through Credit Expansions to Consumers Who want to Borrow?*

2017 ◽  
Vol 133 (1) ◽  
pp. 129-190 ◽  
Author(s):  
Sumit Agarwal ◽  
Souphala Chomsisengphet ◽  
Neale Mahoney ◽  
Johannes Stroebel
Keyword(s):  
Credit Card ◽  
Credit Cards ◽  
Credit Expansion ◽  
New Approach ◽  
Credit Score ◽  
Credit Card Market ◽  
Marginal Propensity ◽  
Pass Through ◽  
The Impact ◽  
Household Borrowing

Abstract We propose a new approach to studying the pass-through of credit expansion policies that focuses on frictions, such as asymmetric information, that arise in the interaction between banks and borrowers. We decompose the effect of changes in banks’ cost of funds on aggregate borrowing into the product of banks’ marginal propensity to lend (MPL) to borrowers and those borrowers’ marginal propensity to borrow (MPB), aggregated over all borrowers in the economy. We apply our framework by estimating heterogeneous MPBs and MPLs in the U.S. credit card market. Using panel data on 8.5 million credit cards and 743 credit limit regression discontinuities, we find that the MPB is declining in credit score, falling from 59% for consumers with FICO scores below 660 to essentially zero for consumers with FICO scores above 740. We use a simple model of optimal credit limits to show that a bank’s MPL depends on a small number of parameters that can be estimated using our credit limit discontinuities. For the lowest FICO score consumers, higher credit limits sharply reduce profits from lending, limiting banks’ optimal MPL to these consumers. The negative correlation between MPB and MPL reduces the impact of changes in banks’ cost of funds on aggregate household borrowing, and highlights the importance of frictions in bank-borrower interactions for understanding the pass-through of credit expansions.

scholarly journals Credit Card Fraudulent Detection Using Machine Learning Algorithm

2020 ◽  
pp. 445-449
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
High Efficiency ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Optimal Algorithm ◽  
Credit Cards ◽  
Training Model ◽  
Machine Learning Algorithms ◽  
Machine Learning Models

The fraudulent transactions that occur in credit cards end in huge financial crisis. Since the web transactions has grown rapidly, the results of digitalized process hold an enormous sharing of such transactions. So, the financial institutions including banks offers much value to the applications of fraud detection. The Fraudulent transactions can occur in different ways and in various categories. Our work mainly focuses on detecting the illegal transactions effectively. Those transactions are addressed by employing some machine learning models and therefore the efficient method is chosen through an evaluation using some performance metrics. This work also helps to select an optimal algorithm with reference to the machine learning algorithms. We illustrate the evaluation with suitable performance measures. We use those performance metrics to evaluate the algorithm chosen. Within the existing system the algorithms provide less efficiency and makes the training model slow. Hence within the proposed system we used Multilayer Perceptron and Random Forest to supply high efficiency. From these algorithms efficient one is chosen through evaluation.

scholarly journals Credit Risk Assessment of Loan Defaulters in Commercial Banks Using Voting Classifier Ensemble Learner Machine Learning Model

2021 ◽  
Vol 11 (5) ◽  
pp. 565-572
Author(s):  
Shrikant Kokate ◽  
Manna Sheela Rani Chetty
Keyword(s):  
Machine Learning ◽  
Banking Sector ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Feature Selection Technique ◽  
Customer Data ◽  
Credit Score ◽  
Credit Risks ◽  
Machine Learning Model

In banking sector credit score plays a very important factor. It is important to find which customer is valid and which is not valid for loan. Now to classify customer’s credit score is used. Based on this credit score of customers the bank will decide whether to approve loan or not. In banks there are major failures due to credit risks. We can automate this by using various Machine learning algorithms to identify loan defaulters. To classify and predict the customers here various Machine learning techniques like gradient boosting, random forest and Feature Selection technique along with Decision Tree are used. Using these algorithms we accurately classify valid and invalid customers for loan. Designed model can classify their customers into good and bad applicants and train the model for getting the better accuracy of the customer data.

A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies

2020 ◽  
pp. 199-226
Author(s):  
Addepalli V. N. Krishna ◽  
Shriansh Pandey ◽  
Raghav Sarda
Keyword(s):  
Genetic Algorithm ◽  
Cyber Security ◽  
Credit Card ◽  
Banking Sector ◽  
Predictive Analytics ◽  
Security And Privacy ◽  
Credit Score ◽  
User Data ◽  
Identity Based ◽  
Credit Card Usage

In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.

scholarly journals Credit Card Fraud Detection using Machine Learning and Deployment of Model in Public Cloud as a Web Service

2020 ◽  
Vol 9 (2) ◽  
pp. 548-552
Keyword(s):  
Machine Learning ◽  
Web Service ◽  
Credit Card ◽  
Detection System ◽  
Credit Cards ◽  
Fraud Detection ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud ◽  
Detection Model ◽  
The World

In recent times, usage of credit cards has increased exponentially which has given way to an increase in the number of cybercrimes related to transactions using credit cards. In this paper, the aim is to reduce the fraudulent credit card transactions happening around the world. Latest technologies like machine learning algorithms, cloud computing and web service implementation has been used in this paper. The model uses Local outlier factor algorithm and Isolation forest algorithm to develop the credit card fraud detection model using unsupervised learning techniques. The model has been implemented as a Web service to make the solution integratable with other applications and clients across the world. A third party prototype application is developed and integrated to the Fraud Detection Model using Web Services. The complete Fraud Detection System is deployed on the cloud. The Fraud Detection Model shows exceptionally high accuracy when compared to other models already existing.

scholarly journals Credit Card Fraud Detection using Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 124-126
Author(s):  
Gautam Kumar ◽  
Shivanesh Kumar ◽  
A Arul Prakash
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Credit Cards ◽  
Fraud Detection ◽  
Human Being ◽  
Credit Card Fraud ◽  
Made In

Now a days credit card plays a very important role in the lives of the human being. It becomes an important part of the businessman, global activities and many more. Even using credit cards give us a most widely used of benefits when it used with the responsibility and carefully, and very small credit and financial harm is also caused by fraudulent activities or transactions. There are a lot of techniques are given to encounter the scope in credit. In spite of, whatever the methods are used they have the same goal of clog the card fraud and each one has its own advantage, drawback and the characteristics too. The deficiency and the good of the credit card detection-methodologies are description and dissimilarity. Moreover, a taxonomy of reference techniques are classified in two fraud-detection perspective, as misuse (supervised) and absurdity (unsupervised) is given. Again, a taxonomy of methods is presented supported caliber to process the categorical and numerical datasets. Other kind of datasets are made in the literature then mentioned and sorted in real and club into the group of the data and therefore the dominant and customary attributes are removed for prosecute application. Consequently, for the new researches, the issues for credit card fraud-detection are described as per the recommendations.

A Comparative Study on Credit Card Default Risk Predictive Model

2019 ◽  
Vol 16 (8) ◽  
pp. 3591-3595 ◽  
Author(s):  
Ooi Jien Leong ◽  
Manoj Jayabalan
Keyword(s):  
Neural Network ◽  
Default Risk ◽  
Credit Card ◽  
Banking Sector ◽  
Predictive Accuracy ◽  
Predictive Analytics ◽  
Risk Management Strategy ◽  
Credit Score ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The risk analysis of credit card defaulters is a critical procedure in the banking sector to classify the card applicants. Banks perform credit score check to make decisions on applications and to set credit limit accordingly. With the increase in the amount of data and advances in data analytics, the approval process can now be automated for quicker processing of applications. This study aims to provide solutions to improve the risk management strategy among financial institutions using predictive analytics. A real-world dataset obtained from a bank in Taiwan were used to perform the analysis in this project. Four data mining algorithms including Decision Tree, Logistic Regression, Random Forest and Neural Network were constructed with the cleaned dataset. Results revealed that Neural Network is the best performing model with an 82% predictive accuracy of credit card defaulters.

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