Machine Learning and Deep Learning Approaches for Fake News Detection

— Digital banking and financial technology have grown so quickly to change the lending ecosystem a lot. Increasingly, banks and other financial institutions are turning to data-driven methods to assess the creditworthiness of borrowers and reduce the risk associated with loan approvals. One of the biggest problems for banks and other lenders is determining whether a borrower will pay back a loan or not. The non-repayment of loans is detrimental to the profits of banks and other financial institutions and increases the number of non-performing assets (NPAs), leading to instability of the entire financial system. To solve this problem, predictive analytics that use Machine Learning (ML) and Deep Learning (DL) techniques have become a good choice.This work focuses on developing an advanced loan prediction framework using ensemble learning techniques to enhance the accuracy and reliability of prediction. Ensemble methods combine predictions from more than one machine learning algorithm to produce a prediction that is better than any one of the individual models. This study considers three main ensemble algorithms: Random Forest, Gradient Boosting, and Extreme Gradient Boosting (XGBoost). These models are recognised to be able to work with structured financial data that has complex, nonlinear relationships while reducing overfitting and variance.Random Forest creates many decision trees during training and outputs the class that is the mode of the classes of the individual trees. It makes things more stable and reduces the variations between single decision trees. Gradient Boosting, on the other hand, builds models sequentially, with each model attempting to correct the errors of its predecessors. XGBoost is an enhanced version of gradient boosting that adds regularisation techniques, optimised tree pruning and parallel processing. This makes it very fast and capable of handling large financial datasets.A big problem when trying to predict loans is class imbalance. In most financial data, the number of cases where there is no default greatly exceeds the number of cases where there is a default. This imbalance can result in the development of biased models that are good at predicting the majority class but not at identifying high risk borrowers. To overcome this limitation, the study utilises advanced data balancing techniques such as SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling). These methods create synthetic samples of the minority class to balance the dataset. That makes it easier for the model to find people who might default.The Financial Behaviour Simulator is a new component added to traditional predictive modelling on this project. The simulator is designed to generate fictitious financial behaviour data for potential borrowers. It simulates the evolution of various factors over time, such as income fluctuations, spending patterns, saving habits, credit utilisation, and repayment behaviours. The simulator helps to circumvent the problems associated with small or incomplete datasets by generating realistic scenarios of financial behaviour. It also allows you to train and test predictive models in different simulated economic conditions.Simulated financial behaviour data coupled with ensemble machine learning methods makes a strong and flexible system for predicting loan risk. This approach not only makes the model more general but also aids in risk assessment based on scenarios. The system allows banks and other financial institutions to gain a better understanding of borrowers by looking at their historical financial records and simulated behaviour patterns. This enables lenders to make better decisions about loan approvals, interest rate changes, and credit limits.The proposed system also lays lot of emphasis on performance evaluation using key classification metrics like accuracy, precision, recall, F1-score and ROC-AUC score. These evaluation metrics ensure that the model not only has a high overall accuracy but also identifies the applicants with high risk. The aim of the framework is to reduce false negatives, which are particularly costly in lending scenarios, by placing more emphasis on recall and precision for the minority (default) class..