Motivated by its rapid growth, this paper investigates how FinTech activities influence risk-taking by financial intermediaries (FIs). In this context, the paper revisits an ongoing debate on the impact of competition on financial stability: on one side, it is argued that greater competition encourages greater risk-taking (competition-fragility hypothesis), while the other side asserts that more competition can increase financial stability (competition-stability hypothesis). Using a curated database covering over 10,000 FIs and global FinTech activities, we find a robust relationship whereby greater FinTech presence is associated with heightened risk-taking by FIs, offering support for the competition-fragility hypothesis. However, the inclusion of bank-, industry, and country-specific characteristics can alter this relationship. Importantly, there is suggestive evidence indicating that in certain cases, greater FinTech presence may be associated with less FI risk-taking amid stronger domestic institutions. Notwithstanding the relevance for policy, this paper presents a novel framework that may help reconcile some of the conflicting results in the literature, which have found supportive evidence for each of the two competing hypotheses

Small business lending (SBL) plays an important role in funding productive investment and fostering local economic growth. Recently, nonbank lenders have gained market share in the SBL market in the United States, especially relative to community banks. Among nonbanks, fintech lenders have become particularly active, leveraging alternative data and complex modeling for their own internal credit scoring. We use proprietary loan-level data from two fintech SBL platforms (Funding Circle and LendingClub) to explore the characteristics of loans originated pre-pandemic (20162019). Our results show that these fintech SBL platforms lent relatively more in zip codes with higher unemployment rates and higher business bankruptcy filings. Moreover, fintech platforms’ internal credit scores were able to predict future loan performance more accurately than traditional credit scores, particularly in areas with high unemployment. Using Y-14 M loan-level bank data, we compare fintech SBL with traditional bank business cards in terms of credit access and interest rates. Overall, while not all fintech firms follow the same approach, we find that fintech lenders could help close the credit gap, allowing small businesses that were less likely to receive credit through traditional lenders to access credit and potentially at lower cost

Central banks around the world are exploring the possibility of Central Bank Digital Currencies (CBDCs) for retail and wholesale use. While no major economy is yet to fully introduced a CBDC, some countries have begun pilot programs. The purpose of this paper is to highlight the potential benefits and risks associated with CBDCs, including challenges and opportunities associated with proposed CBDC regulation in the United States and the European Union. The paper also discusses the CBDC landscape in Asia. It highlights some of the key findings of the research presented in this special issue on FinTech and CBDCs. Lastly, the paper offers thoughts for potential future research in areas such as the actual designs of CBDCs and their uses, ‘DeFi’ versus ‘CeFi’, their interoperability and stability, and concerns over cybercrime

This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. This result reflects a higher capacity of non-traditional data to capture relevant borrower characteristics and of machine learning techniques to better mine the non-linear relationship between variables in a period of stress

Compared with the previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. On the basis of panel data from Chinese commercial banks spanning 2010–2021, this paper investigates the impact of digital transformation on bank soundness and its potential mechanisms. The empirical findings demonstrate a positive association between digital transformation and bank soundness, driven primarily by strategic and management digitization. Mechanistic analysis indicates that digital transformation improves bank soundness by mitigating risk-taking behavior and promoting diversification. The positive effect of digital transformation is more pronounced in state-owned and joint-stock banks, banks with higher liquidity mismatch and in the subsamples with greater levels of external FinTech development and economic policy uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively lenient monetary and macroprudential policies, highlighting the harmonization and complementarity between internal innovation from digital transformation and external regulatory policies in maintaining banking stability. Overall, this paper contributes to the literature on bank FinTech, which focuses on the factors influencing bank stability. This study also provides a novel explanation for the relationship between financial innovation and financial stability

We study the effects of technological change on financial intermediation, distinguishing between innovations in information (data collection and processing) and communication (relationships and distribution). Both follow historical trends towards an increased use of hard information and less in-person interaction, which are accelerating rapidly. We evaluate more recent innovations, such as the combination of data abundance and artificial intelligence, and the rise of digital platforms. We argue that the rise of new communication channels can lead to the vertical and horizontal disintegration of the traditional bank business model. Specialized providers of financial services can chip away activities that do not rely on access to balance sheets, while platforms can interject themselves between banks and customers. We discuss limitations to these challenges to the traditional bank business model, and the resulting policy implications

This study analyzes the evolution of climate-risk discourse in banking using 4,887 news articles (2008–2024) collected from ProQuest. We apply Natural Language Processing and add two novel layers: (i) an event-alignment analysis that links coverage dynamics to dated policy and supervisory milestones, and (ii) a discourse-network analysis connecting banks and regulators. We document a marked post-2020 shift, with ESG emerging as the dominant framing (7,860 mentions) alongside persistent geographic asymmetries (U.S.-led coverage) and uneven sectoral engagement (Risk Management highest salience; Fintech lowest). Sentiment skews positive (≈4,000 positive vs. ≈1,500 negative), and topic modeling identifies eight stable thematic clusters spanning operations, ratings, ESG assessment, disclosures, and market instruments. Event alignment shows media attention is typically anticipatory (median peak two months before an anchor), with COP26 producing a sustained level shift (+100% within a ±6-month window) and the Bank of England’s CBES results generating the largest single spike (210 articles), whereas some 2022 rule-making announcements (e.g., SEC climate-disclosure proposal) exhibit sharper but less durable attention. The discourse network centers on two regulatory hubs (the Federal Reserve and the ECB) with key banks (e.g., Citigroup, JPMorgan, UBS) bridging into supervisory narratives. Collectively, the findings show climate risk becoming embedded in core banking practice while revealing structural, regional, and functional asymmetries that matter for policy design and implementation. • Provides the first longitudinal NLP-based analysis of climate risk discourse in the banking sector • Reveals how climate risk integration in banking has evolved across regulatory, operational, and market dimensions • Identifies distinct thematic domains shaping climate risk narratives in banking over time • Shows that climate risk discourse is predominantly anticipatory around major policy and supervisory milestones • Maps the institutional structure of climate risk governance by linking banks and regulators within a discourse network