The study proposes a machine learning framework that optimizes the approval processes of Neo Banks loan applications and overcomes the limitations of traditional credit scoring models. Using feature engineering, ensemble methods, and hyperparameter optimization on customer demographics, financials, and transaction details to determine loan acceptance. The results show that the improved models have the accuracy of 97.87% and 88.59% recall and are far better compared to traditional methods (logistic regression: 63.76% recall). Significant predictors are education level (importance: 0.35), income (0.30), and family size, with the probability of approval among high-income, high-education customers being 45% higher. The framework reduces the false negative rate, allowing the Neo Banks to focus on the cream of the crop applicants but avoiding risks. Examples of practical strategies are customized marketing and dynamic pricing. This work talks about limitations such as data imbalance, and future research suggests integrating real-time behavioral data and fairness-aware modeling. It fills a gap between tech innovation and operational requirements, providing a scalable method for updating credit risk evaluation in digital banking.

This paper aims to discuss the dynamics of gold pricing through two key approaches: the Error Correction Model (ECM) and the Gold Supply and Demand Balance Valuation Model. These models provide insights into how short-term fluctuations and long-term trends in gold prices are shaped. The ECM is particularly helpful for analyzing non- stationary data like gold prices, while the valuation model focuses on the interaction between supply and demand across different economic conditions. By combining these methods, we can better understand the key drivers of gold prices and offer practical tools for investors, policymakers, and businesses. This study aims to bridge the gap between theoretical models and real-world market behavior, providing a comprehensive framework for understanding and forecasting gold prices in both stable and volatile markets.