Credit Score Prediction Using Machine Learning

2021 ◽  
pp. 69-81
Author(s):  
Debabrata Swain ◽  
Raunak Agrawal ◽  
Ayush Chandak ◽  
Vedant Lapshetwar ◽  
Naman Chandak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Score

scholarly journals Review of Machine Learning models for Credit Scoring Analysis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Madapuri Rudra Kumar ◽  
Vinit Kumar Gunjan
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Profile Analysis ◽  
Credit Scoring ◽  
Process Models ◽  
Machine Learning Techniques ◽  
Process Conditions ◽  
Learning Models ◽  
Credit Score ◽  
Machine Learning Models

Introduction:Increase in computing power and the deeper usage of the robust computing systems in the financial system is propelling the business growth, improving the operational efficiency of the financial institutions, and increasing the effectiveness of the transaction processing solutions used by the organizations. Problem:Despite that the financial institutions are relying on the credit scoring patterns for analyzing the credit worthiness of the clients, still there are many factors that are imminent for improvement in the credit score evaluation patterns.  Objective:Machine learning is offering immense potential in Fintech space and determining a personal credit score. Organizations by applying deep learning and machine learning techniques can tap individuals who are not being serviced by traditional financial institutions. Methodology:One of the major insights into the system is that the traditional models of banking intelligence solutions are predominantly the programmed models that can align with the information and banking systems that are used by the banks. But in the case of the machine-learning models that rely on algorithmic systems require more integral computation which is intrinsic.  Results:The test analysis of the proposed machine learning model indicates effective and enhanced analysis process compared to the non-machine learning solutions. The model in terms of using various classifiers indicate potential ways in which the solution can be significant. Conclusion: If the systems can be developed to align with more pragmatic terms for analysis, it can help in improving the process conditions of customer profile analysis, wherein the process models have to be developed for comprehensive analysis and the ones that can make a sustainable solution for the credit system management. Originality:The proposed solution is effective and the one conceptualized to improve the credit scoring system patterns.  Limitations: The model is tested in isolation and not in comparison to any of the existing credit scoring patterns. 

scholarly journals C-Score: An Alternative Banking Methodology

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2439-2442
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Customer Satisfaction ◽  
Stock Market ◽  
Mathematical Equation ◽  
Random Forest Regression ◽  
The Real ◽  
Credit Score ◽  
A Value

In this day and age, we talk the dialect of assets, stock market investigation and transaction arranged business, or to put a more extensive perspective, money. The current framework focuses on credit score as a default standard for advance application and profiting other banking facilities. The real aspect which is by all accounts missing is adaptability and a connect with the customer. This indirectly prompts a disarray of customer satisfaction and acquisition. The objective of this paper is to build up a superior association with the customers and to create a framework with more pliant aspects, thinking about a more extensive scope of factors for deciding the advance status of a potential applicant. Keeping in mind the end goal to help our speculation, we have contrived a mathematical equation that enables us to perform calculations in light of bigger scope of factors which help decide the applicant's status. This status will appear as a value, which we call the C-Score. This value is utilized to set the level of advantages which can be profited by a customer, accordingly featuring the efficiency of a customer. A calculation is constructed utilizing random forest regression to monitor defaulters and understanding the stream of transactions with respect to advance installments, which is additionally a part of the C-Score. Machine Learning is utilized to play out the calculations at a dynamic stream, the variance being for each customer individually.

Comparative analysis of a deep learning approach with various classification techniques for credit score computation

2020 ◽  
Vol 13 ◽  
Author(s):  
Arvind Pandey ◽  
Shipra Shukla ◽  
Krishna Kumar Mohbey
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Gradient Boosting ◽  
Learning Approach ◽  
Probit Regression ◽  
Credit Score ◽  
Machine Learning Approach

Background: Large financial companies are perpetually creating and updating customer scoring techniques. From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is explored for the case study. Objective: The aim is to audit the comparison between the predictive accuracy of the probability of default with various techniques of statistics and machine learning. Method: In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for machine learning and statistical model evaluation. Results: Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms. Conclusion: Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and statistical approaches.

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.

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 Credit Risk Rating Using State Machines and Machine Learning

2020 ◽  
Vol 11 (6) ◽  
pp. 163-168
Author(s):  
Behnam Sabeti ◽  
◽  
Hossein Abedi Firouzjaee ◽  
Reza Fahmi ◽  
Saeid Safavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Learning Task ◽  
Supervised Machine Learning ◽  
State Machines ◽  
Data Set ◽  
Credit Score ◽  
Novel Approach ◽  
User Data ◽  
Number Of Customers

Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

Credit Score Evaluation of Customer Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Saurabh Agrawal ◽  
Purnima Ahirao ◽  
saurabh kumar ◽  
Pinak Dere
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Credit Score ◽  
Score Evaluation

Advanced credit score calculation using social media and machine learning

2019 ◽  
Vol 36 (3) ◽  
pp. 2373-2380 ◽  
Author(s):  
Swati V. Kulkarni ◽  
Sudhir N. Dhage
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Credit Score

A benchmark of machine learning approaches for credit score prediction

2021 ◽  
Vol 165 ◽  
pp. 113986
Author(s):  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Giancarlo Sperlí
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Credit Score

scholarly journals Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

2020 ◽  
Vol 13 (8) ◽  
pp. 180 ◽  
Author(s):  
Bernard Dushimimana ◽  
Yvonne Wambui ◽  
Timothy Lubega ◽  
Patrick E. McSharry
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Microfinance Institutions ◽  
Credit Score ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Default Rates ◽  
Default Rate ◽  
Score Model

Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers.

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