Abstract:  Artificial intelligence (AI) is transforming productivity and market structure, yet the roots of firm dominance in the modern economy remain unclear. Is market power driven by AI capabilities, access to data, or the interaction between them? We develop a dynamic model in which firms learn from data using AI, but face informational entropy: without sufficient AI, raw data has diminishing or even negative returns. The model predicts two key dynamics: (1) improvements in AI disproportionately benefit data-rich firms, reinforcing concentration; and (2) access to processed data substitutes for compute, allowing low-AI firms to compete and reducing concentration. We test these predictions using novel data from 2000–2023 and two exogenous shocks—the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. The results confirm both mechanisms: compute access enhances the advantage of data-intensive firms, while access to processed data closes the performance gap between AI leaders and laggards. Our findings suggest that regulating data usability—not just AI models—is essential to preserving competition in the modern economy. 

Abstract: Payment fintechs, acting as lenders, possess a potential solution to weak debt enforcement because of their ability to deduct a part of a merchant’s digital sales towards loan repayment. Analyzing payments processed by an Indian fintech company offering sales-linked loans, we find that some borrowers discontinuously reduce sales flowing through the company immediately after the loan disbursal to circumvent repayment and strategically default. Using credit bureau scores sourced independently and the spatial and temporal heterogeneity in cash availability generated by a cash-crunch episode, we find that competition from other lenders and cash limits the effectiveness of this enforcement technology 

Abstract:  Does AI make firms vulnerable or resilient to cyber risk? To answer this, we develop a novel measure identifying AI-intensive U.S. public firms using publicly available patents and business-description data. While cyber threats typically suppress innovation, AI-intensive firms neutralize this effect. This protective effect strengthens with greater AI experience. Moreover, firms combining AI innovation and implementation exhibit a stronger buffer protecting their innovation and financial outcomes under cyber stress, whereas firms merely implementing AI without internal innovation gain no such resilience. Our results emphasize internal AI innovation as fundamental in enabling firms to effectively withstand cyber threats. 

Abstract: I examine the value of interoperable payment data in lending, central to Open Banking initiatives. Using a unique dataset linking borrowers’ payment histories with their non-bank loans, I show that payment data improves loan screening and monitoring beyond traditional sources including credit bureaus. These information sources are complementary: through subsample analysis and a natural experiment—where borrower discretion is reduced—I find that payment histories signal real-time repayment ability, while credit bureau captures willingness to repay. Shifting to payment-based screening benefits most borrowers but disadvantages those with poor credit and limited payment histories, highlighting key trade-offs in Open Banking implementation. 

Abstract:  Data security is often viewed as a defensive cost. But in today’s data-driven economy, can it also create value? We propose a novel measure of ''data innovation complementarity'', capturing the extent to which firms have structurally integrated data-security expertise into their broader innovation processes. We show that such integration unlocks cross-domain knowledge spillovers, fueling firm-wide innovation and growth. Exploiting U.S. Data Breach Notification Laws as a quasi-exogenous increase in the perceived importance of data security, we find that only high-complementarity firms realize these gains. A calibrated real-options model shows that value creation arises only above a complementarity threshold, turning defense into opportunity. 

Abstract: A bank faces entrepreneurs with different degrees of innovativeness, which is their private information. More innovative entrepreneurs pursue high-riskhigh- return technologies and possess higher reservation utility. Within such a scenario, the bank’s contract menu, which exclusively incorporates collateral as a selection mechanism, proves inadequate for distinguishing entrepreneur types. However, through the introduction of a distinctive contractual element offering an unconditional private benefit to the entrepreneur set against a high repayment obligation, the bank is able to reestablish its capacity to segregate entrepreneurs. This novel contractual addition bears resemblance to contemporary venture capital agreements.

Abstract: In a setting of credit market with asymmetric information, we present a case when collateral fails as a screening device. This rules out any separating equilibrium. Pooling equilibrium exists and is characterized with socially inefficient lending, as some socially productive firms are denied credit due to high interest rate. These results hold for any arbitrary distribution of types.

Abstract: In contrast to the benign neglect of the financial system in traditional monetary models, there has been growing evidence in recent years that the size and the structure of financial intermediation play a critical role in the transmission of monetary policy. This paper reviews the implications of three key post-2008 crisis developments in financial intermediation - the role of banks, the globalisation of debt markets and the sustained decline in global long-term interest rates - for various transmission channels of monetary policy in EMEs. The paper argues that the globalisation of debt markets means that monetary policy can no longer be conducted through the short-term interest rate alone. This raises questions about the appropriate instruments to be used for economic stabilisation in this new environment.