Loan Analysis Predicting Defaulters
IRJET Journal2022, IRJET
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Abstract
Due to the advancements in the domain of Artificial Intelligence and Data Science, its utilization is becoming more common in every possible domain. Nowadays, the majority of the industries make use of AI and its applications in some or the other way. Taking the advantage of the field of Data Science results in creating effective and modern applications, products irrespective of the domain. One of the industries where the application of AI and Data Science is proving to be effective is the Finance Industry commonly known as the Banking Sector. Banks face severe losses due to the loan defaults made by the client and hence to overcome this problem, there lies a need to create a credit risk scoring model which can analyze and predict the loan defaults. Hence, with the help of Machine Learning, we aim to create a Loan Default Analysis model which could predict the loan defaults and integrate the model into a web application for the user for easy usability.
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Prediction Model for Loan Default Using Machine Learning
The International Journal of Science & Technoledge
Business firms and households sometimes seek for extra-funding to fulfill certain needs. The demand which arises from the need of extra funds is fulfilled by the credit market. Banks and others financial lending institutions are the key players in this market (Gaigaliene and Cesnys, 2018). Loan is one of the most important products of most financial institutions. All financial lenders try to find effective business strategies for persuading customers to apply for loans. However, there are some borrowers who default in loan payments (Begum and Deniz, 2019). During a loan term, default may occur when the borrower fails to make required payments. Therefore, an assessment of a borrower's default risk over time is essential to enable timely risk management. Credit officers determine whether borrowers can fulfill their requirements using manually analysis of borrower's credit history. In the last decade, this trend has changed over time with technological advancement (Rehman, 2017). In recent years, financial lending institutions are using automated loan default models as credit risk scoring tools when granting loans to potential borrowers (Bao et al., 2019). Machine Learning (ML) algorithms have been applied to assess the credit risk of borrowers in financial lending institutions (Djeundj and Crook, 2018). Reliable models for credit risks play an important role in loss control and revenue maximization (Luo and Nie, 2016). Earlier research treated loan default prediction as a binary classification problem, where a loan is classified as either creditworthy or non-creditworthy (Rosenberg and Gleit, 1994). Linear Discriminant Analysis (LDA) and logistic regression (LR) are two most popular tools for constructing credit scoring models (Wiginton, 1980). Subsequently, other classification algorithm such as, Artificial neural networks (ANN) Gulsoy and Kulluk (2019) support vector machines (SVM) Alaka et al. (2018), decision trees (DT) Liu et al. (2015), and Bayesian classifier (BC) Carta et al. (2020), have been used to estimate borrowers' probability of default. Recently, time-to-default modeling has attracted increasing research interest (Dirick et al., 2017). Time-to-default data fall into the category of lifetime data in general, which is commonly analyzed by survival analysis (SA) (Malekipirbazari and Aksakalli, 2015). In loan prediction, two types of errors inevitably lead to inefficiency in prediction
Loan Default Prediction Using Machine Learning Techniques
IRJET, 2023
Loan business is one of the major income sources for bank. Loan default problem is a major issue for loan business. Loans, specifically whether borrowers repay the loan or default on it, have a significant impact on a bank's profitability. By anticipating loan defaulters, the bank is able to reduce its non-performing assets. Three primary predictive analytics techniques-I Data Collection, II Data Cleaning, and III Performance Assessment-are used to research the prediction of loan defaulters. Experimental investigations reveal that when it comes to loan forecasting, the KNN model performs better than the Decision tree model.
Machine Learning-Based Credit Risk Assessment for Predicting Loan Defaulters in Ethiopian Banking industry
Research Square (Research Square), 2023
Machine Learning is an AI technique, empowers organizations globally to gain insights from their data and achieve success. This study focuses on improving risk management of banking in Ethiopia by employing machine learning techniques to detect and predict loan defaulters, ultimately mitigating bad loans. The research aims to distinguish borrowers who repay loans promptly from those who don't, forecast potential defaults, and assess the creditworthiness of prospective customers without overhauling existing systems and data. To showcase the study's results, classi cation models are constructed to reveal signi cant patterns among customer attributes. The primary objective is to apply cutting-edge machine learning algorithms to classify bank loan defaulters. Several machine learning algorithms, including Random Forest, Decision Tree, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP), are employed in the process. Various data split ratios, such as 70/30, 60/40, and 90/10, are explored, alongside the 80/20 data split, to build classi cation models for each classi er algorithm. Based on the experiment's outcomes, the 70/30 split is selected for its superior accuracy. The study evaluates the performance of these algorithms using metrics like accuracy, precision, recall, and F1score. The results indicate that the Gradient Boosting classi cation technique surpasses other algorithms, achieving a training accuracy of 98.7% and a testing accuracy of 97.8%.
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Loan Prediction Using Machine Learning Algorithms
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Loans are a major prerequisite in the present-day world. By this, as it were, Banks get a major portion of the entire benefit. It is advantageous for understudies to oversee their instruction and living costs, and for individuals to purchase any kind of extravagance like houses, cars, etc. But when it comes to choosing whether the applicant's profile is pertinent to be allowed a credit or not. Banks must see many aspects. Giving credit is the most commerce of banks. A noteworthy parcel of the bank's income comes straightforwardly from benefits from credits. Indeed, on the off chance that the bank favors the advance after the confirmation and certification relapses prepare, it cannot be beyond any doubt that the chosen candidate is the genuine candidate. Doing this handle physically requires unused time. Able to anticipate whether. a specific candidate is secure, and the whole confirmation handle is automatized with the machine. fashion. After the presentation of innovation t...
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Credit Risk Analysis using Machine Learning
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