Machine Learning in Investment and Credit Risk Modeling

Abstract

The financial sector has undergone many changes with the advancement of technology. Artificial intelligence algorithms are used to analyze large datasets and support making trades at high speed based on market patterns. Machine learning algorithms discover trends and patterns from data fed to examine creditworthiness more precisely than conventional methods. By incorporating ML techniques, FI can enhance risk assessment and make well-informed lending judgments. The most commonly embraced applications of machine learning in investment include risk management, fraud detection, customer support, and process automation. The financial industry is mostly driven by customer expectations and preferences. The usage of machine learning in investment is developing and focusing on moving in the direction of autonomous finance. Hence, the present study examines the development and influence of machine learning in investment and credit risk modeling. ML in financial institutions improves security, precise prediction, and efficiency.ML positively influences investment and credit risk modeling as it offers many benefits. The present study analyses the impact of machine learning-based decisions in preventing financial losses. The ML majorly enhances the process of decision-making by predicting opportunities, risks, and threats with fed data from the investor.

Furthermore, the current study deliberated on machine learning-based credit risk modeling and investment challenges. The main challenge is the overfitting, under-fitting, insufficient, and poor quality of training data, such as missing and abnormal values. Therefore, the study also recommends the use of machine learning-based decisions for the investment.