This paper explores the dual impact of digital banks and alternative lenders on financial inclusion and the regulatory challenges posed by their business models. It discusses the integration of digital platforms, machine learning (ML), and Large Language Models (LLMs) in enhancing financial services accessibility for underserved populations. Through a detailed analysis of operational frameworks and technological infrastructures, this research identifies key mechanisms that facilitate broader financial access and mitigate traditional barriers. Additionally, the paper addresses significant regulatory concerns involving data privacy, algorithmic bias, financial stability, and consumer protection. Employing a mixed-methods approach, which combines quantitative financial data analysis with qualitative insights from industry experts, this paper elucidates the complexities of leveraging digital technology to foster financial inclusivity. The findings underscore the necessity of evolving regulatory frameworks that harmonize innovation with comprehensive risk management. This paper concludes with policy recommendations for regulators, financial institutions, and technology providers, aiming to cultivate a more inclusive and stable financial ecosystem through prudent digital technology integration.

This report considers the financial stability implications of the growing use of artificial intelligence (AI) and machine learning in financial services. Financial institutions are increasingly using AI and machine learning in a range of applications across the financial system including to assess credit quality, to price and market insurance contracts and to automate client interaction. Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions. Meanwhile, hedge funds, broker-dealers and other firms are using it to find signals for higher uncorrelated returns and to optimise trade execution. Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment and fraud detection.

The rapid use of artificial intelligence in banking could trigger financial stability risks and some unexpected surprises unless proper testing and training is put in place, the Financial Stability Board has warned. Banks, insurers and asset managers are rushing to swap humans with computer systems able to do the same jobs, with'smart' robots able to crunch data, automate client interaction, spot fraud or price insurance contracts. But the race to replace people with machines "has the potential to amplify financial shocks" and could be used by cybercriminals to manipulate market prices, the FSB said, adding that firms were in an'arms race' to adopt AI because their competitors are. While the FSB acknowledged that the use of AI shows "substantial promise" and could make the financial system more efficient, it urged the industry to monitor usage closely as a number of risks were on the horizon. Institutions could become dependent on the technology giants making the robots, for example, opening them up to risks created by third-party providers which fall outside the remit of financial regulators.

Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the global Financial Stability Board (FSB) said. The FSB, which coordinates financial regulation across the Group of 20 Economies (G20), said in its first report on artificial intelligence (AI) and machine learning that the risks they pose need monitoring. AI and machine learning refer to technology that is replacing traditional methods to assess the creditworthiness of customers, to crunch data, price insurance contracts and spot profitable trades across markets. Replacing bank and insurance workers with machines risks creating a dependency on outside technology companies beyond the reach of regulators, the Financial Stability Board (FSB) said. There are no international regulatory standards for AI and machine learning, but the FSB left open whether new rules are needed.