Harnessing AI and machine learning for enhanced credit risk analysis: A comprehensive exploration of computational techniques in the financial realm

Abstract

Within the confluence of the banking and financial sectors, the integration of machine learning in credit risk analysis signifies a paradigm shift towards data-centric decision-making. Historically, methodologies for credit risk were limited in predictive accuracy and computational efficiency. The advent of expansive language models, exemplified by Ant Group's AntFinGLM, offers a solution. These models, underpinned by deep learning, amalgamate financial texts and transactional data, facilitating the discernment of intricate financial paradigms and market nuances. This paper conducts a rigorous exploration of machine learning methodologies, from Bayesian classifiers to k-means clustering, offering an analytical perspective on their advantages and challenges. As the industry inclines towards innovations like AntFinGLM, the imperatives of professionalism, precision, and data sanctity gain significance. Upholding standards that encompass five dimensions and 28 categories, AntFinGLM epitomises these benchmarks, championing enhanced functionalities while fostering collaborative initiatives with financial entities. Addressing challenges, particularly around data security and professional integrity, becomes crucial. Techniques encompassing intent recognition, fact verification, and robust data protection mechanisms are indispensable. In summation, the endeavours of entities like AntFinGLM underscore the transformative prowess of expansive language models, ushering the financial sector into an epoch characterised by astute, efficient, and safeguarded decision-making paradigms.