Key Takeaways
- Technological advancements, particularly AI, are significantly reshaping the financial landscape, including lending, by enabling more efficient credit assessment and fostering financial inclusion.
- While fintech offers benefits like improved loan screening and personalized services, it also raises concerns regarding financial stability, data privacy, and potential discrimination.
- Empirical evidence on fintech’s impact on loan pricing, default rates, and financial inclusion remains mixed, with different studies reporting varying outcomes.
- An analytical framework highlights that fintech’s influence on competition and welfare depends on factors like differentiation between intermediaries, competition intensity, and price discrimination capabilities.
- The specific type of fintech advancement plays a crucial role, with innovations reducing the distance friction between lenders and borrowers having a more pronounced impact on market dynamics.
The Evolving Financial Sector: Fintech’s Disruption
Technology, and specifically artificial intelligence (AI), is revolutionizing the financial world, with lending being a prime example of this transformation. Machine learning algorithms and vast datasets are now effectively employed for sophisticated credit assessment. The rise of fintech has spurred significant efficiency gains throughout the loan lifecycle, from enhanced screening and monitoring to streamlined processing, while also extending financial services to previously underserved populations and developing economies.
However, these technological strides are not without their complexities. Concerns have emerged regarding financial stability, user privacy, and the potential for discriminatory practices. Digital technologies empower financial institutions to segment customers with greater precision, which, while facilitating personalized services, also enables more nuanced price discrimination.
The empirical data reflecting fintech’s impact on loan pricing, the interplay between fintech and traditional bank credit (whether they substitute or complement each other), loan default rates, and data sharing practices presents a varied picture.
Mixed Results in Fintech Lending Studies
Empirical investigations have yielded differing conclusions on whether fintech-originated loans exhibit higher or lower default or delinquency rates compared to those from traditional banks. Some research indicates elevated default rates (Di Maggio and Yao 2021), while others report lower rates (Fuster et al. 2019), and a segment of studies finds no statistically significant difference (Buchak et al. 2018).
Similarly, the impact of open banking initiatives on small and medium-sized enterprises (SMEs) shows mixed results. While these initiatives can increase the likelihood of SMEs establishing new lending relationships with non-bank lenders and potentially reduce their interest payments, they do not consistently improve financial inclusion (Babina et al. 2024).
Conversely, in specific markets like Germany (Nam 2023) and India (Alok et al. 2024), open banking has demonstrably improved credit access on both extensive and intensive margins without escalating risk. In the United States, California’s Consumer Privacy Act has, in fact, bolstered fintechs’ screening capabilities relative to banks, leading to more personalized mortgage pricing, ultimately lowering loan rates and enhancing financial inclusion (Doerr et al. 2023).
An Analytical Framework for Understanding Fintech and Banking
A recent analytical framework developed by Vives and Ye (2025a, 2025b) addresses the key distinctions between fintech firms and incumbent banks. This framework aims to reconcile the divergent empirical findings in existing literature and offers insights for welfare analysis. It introduces a taxonomy categorizing how fintech influences frictions within the lending market.
The study posits that fintech’s effect on market competition and overall welfare is largely determined by its influence on the differentiation among financial intermediaries and the efficiency disparities between them. Crucial elements shaping market performance include the existing level of bank concentration, the intensity of competition among fintech providers, the scope for price discrimination, the size of the unbanked population, and the convenience factor offered by fintech services.
The model utilizes a spatial oligopolistic competition paradigm where lenders, comprising both banks and fintechs, vie to offer loans to entrepreneurs. This framework captures fundamental differences between these entities. For instance, banks typically possess more extensive financial data and soft information (gained through relationship lending) in comparison to fintechs. However, fintech firms often boast superior information-processing technology and are more adept at converting qualitative information into quantifiable metrics (leveraging digital footprints). They also tend to have lower distance friction with borrowers, where distance can be physical or relate to expertise; a greater distance typically increases monitoring or screening costs.
Furthermore, banks generally benefit from lower funding costs, while fintechs often provide greater convenience to customers. Fintechs also exhibit higher price flexibility due to technological and regulatory factors, granting them a competitive edge. In a simplified view, banks are differentiated by expertise (akin to location), whereas fintechs are not universally differentiated in this manner. Critically, fintechs can engage in price discrimination, a capability often absent in traditional banks.
In this model, entrepreneurs endogenously decide to participate at various locations, with their projects requiring monitoring (screening) to enhance returns (Vives and Ye 2025b) or to mitigate potential moral hazard issues (Vives and Ye 2025a).
The Critical Role of Fintech Advancement Type
A significant insight from Vives and Ye (2025a) emphasizes the importance of distinguishing between general fintech advancements that reduce the lender-borrower distance and those that do not. Broad improvements in data collection and processing, such as enhanced data storage, increased computing power, or sophisticated desktop software, do not inherently lessen distance friction.
Conversely, technologies that effectively shrink the distance between lenders and borrowers include advancements in internet connectivity, video conferencing, remote collaboration tools, AI, and sophisticated search engines. These innovations enable lenders to expand their domain expertise and more effectively serve borrowers located further away. Big data analytics, coupled with machine learning, can bolster both types of capabilities mentioned.
⚡ If fintech innovations successfully reduce distance friction, the differentiation among lenders diminishes, leading to increased competition intensity and potentially reduced lender profits and monitoring incentives. This effect is amplified when entrepreneurs face more severe moral hazard problems. The impact on entrepreneurs’ investment decisions and overall welfare follows a hump-shaped pattern. Notably, these effects are absent when fintech progress does not alter the distance between lenders and borrowers.
Competition Dynamics Between Banks and Fintechs
In their work (Vives and Ye 2025b), the authors model a scenario where banks are differentiated by expertise (akin to being located at different points on a circle), while fintechs are not inherently differentiated and are positioned centrally (in the virtual middle). Their findings indicate that:
- Fintech entry can be prevented (blockaded), remain a latent threat, or become a market reality, contingent on the fintech’s monitoring efficiency.
- Fintech lending may either substitute or complement bank lending, depending on whether banks were already in a competitive pre-entry environment.
- Fintech entry and the volume of loans it facilitates tend to be higher in markets with greater pre-existing bank concentration.
Furthermore, if banks are restricted from price discrimination, a fintech firm without superior monitoring efficiency or funding cost advantages can still enter the lending market. Should banks and fintechs possess similar funding costs, banks may consequently offer higher loan rates and engage in more rigorous monitoring compared to fintechs for similarly situated entrepreneurs, making fintech borrowers more prone to default. This dynamic shifts if fintechs have substantially higher funding costs than banks.
📍 If fintechs offer a significant convenience advantage, they are likely to command higher prices, while banks may engage in more in-depth monitoring. Therefore, disparities in funding costs, the value of convenience, and the ability to price discriminate can account for the varied empirical outcomes observed in loan default rates between banks and fintechs.
The entry of fintechs might suppress entrepreneurs’ investment levels if competition among fintech firms is not sufficiently vigorous. An intermediate level of competitive intensity within the fintech sector is necessary to ensure that fintech entry positively impacts overall welfare, by appropriately balancing the incentives of both borrowers and lenders.
📍 However, if banks are also permitted to engage in price discrimination, fintechs require an advantage in monitoring (or, less likely, in funding costs) to successfully penetrate the market. Finally, the mere threat of fintech entry, or its actual occurrence, can prompt banks to exit or restructure. This could potentially reduce the intensity of lending competition and investment, yet it may also generate a welfare-enhancing option value effect.
Policy Implications for the Financial Sector
The analysis offers several important policy considerations. It is recognized that price discrimination can serve as a competitive tool, but its welfare optimality is not guaranteed unless it successfully expands the market, a point echoed in the modeling.
Socially optimal loan rates should achieve a balance between incentivizing entrepreneurs and intermediaries to exert effort (thereby mitigating moral hazard), encouraging broader market participation by entrepreneurs, and enhancing lenders’ monitoring or screening efforts. This equilibrium, however, is often not achievable through unmitigated lender competition with location-based price discrimination.
For instance, with endogenous entrepreneur participation across all potential locations, a bank should, from a welfare standpoint, charge higher rates for more distant locations due to increased monitoring costs and lower surplus generated. In contrast, in a competitive equilibrium, price-discriminating banks might adopt the opposite strategy to remain competitive. Nevertheless, permitting banks to price discriminate when fintechs already do so can improve welfare, particularly when inter-fintech competition is limited.
📊 Regarding data sharing policies, such as open banking, the research suggests that initiatives beneficial to fintechs must be paired with an adequate level of inter-fintech competition. Without this, such policies could be counterproductive, potentially allowing a dominant fintech firm to secure a monopoly in certain market segments. Differences in the degree of competition across markets may help explain the varying empirical results concerning the impact of open banking initiatives.
In conclusion, policies aimed at leveling the playing field—ensuring parity in lenders’ ability to price discriminate and access information—are valuable for fostering a degree of competition that promotes a fair distribution of rents. This, in turn, helps to align the incentives of various market participants, ultimately maximizing welfare. It is crucial that this competitive environment is sufficiently robust to prevent monopolistic dominance in specific market segments, while also ensuring that both lenders and borrowers maintain a meaningful stake in the market’s success.