Unleashing the Power of AI: Revolutionizing Financial Inclusion

Can artificial intelligence (AI) be the disruptor that finally makes financial services more accessible? In this comprehensive exploration, we delve into the transformative potential of AI in improving access to credit, drawing insights from leading academic journals and historical perspectives. We examine whether AI is poised to level the playing field in financial inclusion and analyze how past technology disruptions have shaped the landscape of credit access. Finally, we uncover the challenges and opportunities that lie ahead as the financial industry embraces the Generative AI Era.

Unlocking Financial Inclusion: AI's Transformative Potential

The Soft Information Era: Relationship-Driven Lending

In the early 20th century and prior, loan decisions were predominantly manual, relying heavily on human judgments and qualitative assessments. Computers were not yet mainstream, and the available technology lacked the sophistication to automate these processes. Lending decisions were heavily influenced by personal relationships and the borrower's character, with local bankers often knowing their clients intimately and assessing their reputations and integrity. The borrower's social standing and community ties were crucial factors in evaluating the likelihood of loan repayment. Extensive interviews and physical collateral inspections were standard practices to understand the borrower's needs and repayment ability, while references from respected community members provided additional assurances of the borrower's reliability.

The Hard Information Era: The Rise of Quantitative Lending

As businesses grew more complex in the mid-20th century, financial statements became increasingly significant in lending decisions. Bankers manually analyzed balance sheets, income statements, and cash-flow statements, calculating fundamental financial ratios such as debt-to-income (DTI) and loan-to-value (LTV) to evaluate repayment ability. The establishment of credit bureaus allowed the collection and sharing of credit information, with lenders accessing borrowers' credit histories, including loans, repayments, and defaults. However, these credit reports were less comprehensive than modern credit scores, and the soft information collected through bankers' interactions remained essential to facilitating credit.

The Financial Technology Era: Fintech's Promise of Inclusion

During the Financial Technology Era, between the mid-2000s and the mid-2010s, the disruption of financial technology (fintech) lenders, characterized by their intensive use of technologies to provide financial services, was seen as the solution to the credit-access gap. Fintech's promise of introducing the latest advances in information processing, cutting-edge machine-learning (ML) models, access to big data, and innovative data-driven culture suggested that the boundaries between hard and soft data might disappear. The question then arose: Would the advanced technologies introduced by fintech companies be sufficient to guarantee access to credit for all solvent borrowers while charging fair interest rates?

The Generative AI Era: Blurring the Lines of Lending

The advent of advanced generative AI (GenAI) technologies has the potential to be the ultimate tool that blurs the traditional boundaries between hard- and soft-information analyses. These technologies, embedded in new models for computer vision, natural language processing (NLP), large language models (LLMs), and big-data capabilities, have been widely implemented across the lender spectrum, even among incumbent, mainstream banks. The financial-economics community is now focused on analyzing the influences of these novel technologies, but the historical perspective of the Generative AI Era still needs to be fully explored.

The Promises and Perils of AI-Driven Lending

The use of GenAI across the industry can transform access to finance, but it is still uncertain whether it will lead to better inclusivity and efficiency than previous technologies. While fintech lenders have demonstrated the ability to process mortgage applications faster and provide credit more elastically in response to demand shocks, the evidence on their impact on financial inclusion is mixed. Some studies have shown that fintech lenders tend to target the most profitable borrowers, while others have found that they may initially increase the inclusion of higher-risk borrowers not typically served by incumbents.Moreover, the rational goal of profitability embedded in AI models can lead to undesirable outcomes for financial inclusion. The recent findings on AI algorithms autonomously learning to collude and charge supra-competitive prices raise concerns about the potential for AI to exacerbate inequalities in access to credit.

The Future of AI in Financial Inclusion: Challenges and Opportunities

As the financial industry embraces the Generative AI Era, the boundaries between the typical soft-information-based relationship lending and the mostly hard-information-based model may disappear. In an optimistic scenario, AI has the potential to significantly enhance financial inclusion by improving access to credit, reducing transaction costs, and providing personalized financial education. However, in a pessimistic scenario, the advent of general intelligence AI could completely transform financial intermediation as we know it, with the majority of human labor being replaced.Addressing the challenges and risks associated with AI is essential to ensuring its benefits are equitably distributed. Continued research and careful implementation of AI in financial services, along with robust supervision, will be vital to achieving inclusive economic growth. The financial-economics community must remain vigilant in analyzing the influences of these novel technologies and their implications for financial inclusion, as the future of the industry hangs in the balance.