Loan Default Prediction Using Machine Learning

Loan Default Prediction Using Machine Learning Thesis Topics

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1. Explainable prediction of loan default based on machine learning models

Keywords:

Explainable prediction, Machine learning, Loan default, Local interpretable model-agnostic explanations

            Our paper predicts the loan default by utilizing ML methods like LR, DT, XGBoost and LightGBM model. Our prediction outcome displays that LightGBM and XGBoost performs better than logistic regression and decision tree models to predict the ability. We work the local interpretable model-doubting clarification methods to assume reasonable analysis of predictions.

2. 2. A Novel Predictive Model for Housing Loan Default using Feature Generation and Explainable AI

Keywords:

Home Loan default, LIME, SHAP, Random Forest, Gradient Boosting

            We examine some machine learning methods to find the loan default before give the loan to the candiatate. This can be learned widely and utilize the predictive analytics identify the connection among attributes and target variable. Predictive analysis allows to give optimal set of features to ML models. We have to fit five ML methods to datasets and the champion model came up with roc score. LIME and SHAP were given to the champion model with dataset for global and local understandable.

3. Comparison of Extreme Logistic Regression Algorithm and Random Forest Algorithm for Efficient Prediction of Car Loan Default with Improved Accuracy, Precision, and Recall on Personal Loan Dataset

Keywords:

Prediction algorithms, Classification algorithms, Automobiles, Logistics

            To predict the car loan is the goal of our paper by utilizing Extreme Logistic Regression with Novel Association rule and compare with RF method. ML is the developing field for prediction and we consider two groups namely Extreme LR method with Novel Association Rule and RF method. Our result displays that the Extreme LR with a Novel Association Rule performs well as Random Forest method. 

4. An Optimized Extreme Learning Machine for Predicting Loan Default in Peer-to-peer Lending Based on an Enhanced Honey Badger Algorithm

Keywords:

peer-to-peer lending, loan default prediction, optimized extreme learning machine, meta-heuristic algorithm

            Our paper offers a novel hybrid intelligence method for loan default prediction in p2p lending based on Extreme Learning Machine (ELM) and an Enhanced Honey Badger Algorithm (EHBA). To increase nature-inspired meta-heuristic method we tune the parameters of ELM to increase predictive performance. The proposed method can enhance the loan default prediction by comparing KNN, ANN, RF, SVM, KSVM, ELM, GA-ELM, PSO-ELM, GWO-ELM, AOS-ELM, MPA-ELM and HBA-ELM.

5. Loan Default Prediction Using Machine Learning Techniques

Keywords:

Loan prediction, Banking, Credit risk management, Predictor, Classifiers, Python

            We suggest a best methodology by utilizing machine learning methods like KNN, Decision Tree, SVM and Logistic Regression to predict defaulters. The accuracy of our methods can also be tested by utilizing the metrics like log loss, Jaccard similarity coefficient and F1 score. Our metrics can be contrast to define the accuracy of prediction. This can help bank to protect manpower and to reduce the number of steps to verify if they are eligible or not for loan.

6. Bank Loan Default Prediction Using Ensemble Machine Learning Algorithm

Keywords:

Ensemble Machine Learning, Decision Tree, Classification

            Recently banks use some methods to predict the chance of loan repayment from the borrower. Our paper aims to produce a similar model but by utilizing ensemble machine learning method of Random Forest classification and can perform a comparison with the method (Decision Tree Classification) can now in use. After we finished the execution all the models was decided that Random forest classifier gives the best performance than DT classification.

7. An Effective Approach for the Prediction of Car Loan Default Based-on Accuracy, Precision, Recall Using Extreme Logistic Regression Algorithm and K-Nearest Neighbors Algorithm on Financial Institution Loan Dataset

Keywords:

Car Loan Forecasting, Extreme Logistic Regression Algorithm, K-Nearest Neighbors Algorithm, Novel Credal Sets

            The goal of our paper is to predict car loan by utilizing an extreme Logistic Regression method with novel credal sets and KNN methods. We can used ML methods is a developing field for prediction so our paper consider two groups like Extreme Logistic Regression with Novel credal sets and KNN methods. Our Extreme Logistic Regression method performs better than KNN.

8. Development of Loan Default Prediction Model for Finance Companies in Sri Lanka – A Case Study

Keywords:

Finance companies

            We have an effort to improve the machine learning based loan default prediction method to increase credit decisions. We used some traditional machine learning methods that are selected, trained and evaluated by using real world dataset that are similar to vehicles from one of the top FCs in Sri Lanka. Our model SVM and RF gives the most accurate outcome. 

9. Prediction of loan default based on multi-model fusion

Keywords:

Credit crisis, multi-model fusion

            Our paper utilizes loan default dataset from lending club. We implement the method ADASYN (Adaptive synthetic sampling approach) to handle class imbalance issue of the dataset. We have to increase the prediction accuracy by utilizing the Blending method to combine three methods namely LR, RF and CatBoost that can efficiently predict the possibility of customer loan default over the training of the dataset and to decrease the risk by online loan platform.

10. Optimization of Machine Learning Models for Prediction of Personal Loan Default Rate

Keywords:

LightGBM, Credit Default Prediction

            Our paper built two personal credit loan default risk assessment models like RF, Light Gradient Boosting Machine (LightGBM) by utilizing accurate rate (ACC) and Area under the ROC curve (AUC) as metrics. The most significant factors that can affect the loan defaults are ‘debt_loan_ratio’ and ‘known_outstanding_loan’. LightGBM gives the best performance.