AI-driven fintech boosts long-term bank stability but raises short-term risks

AI-driven systems rely heavily on large volumes of data, automated decision-making, and algorithmic models that can amplify errors if governance structures are weak. When these systems are introduced rapidly, banks may struggle to align them with existing risk controls, leading to volatility in performance. The study shows that this adjustment phase is especially sensitive in developing and emerging economies, where regulatory frameworks for AI and Fintech are still evolving.

 A large cross-country study focusing on Islamic and conventional banks shows that while AI-powered financial technology can strengthen banking systems over time, it may also introduce short-term instability if deployed without adequate regulatory and institutional safeguards.

The study, titled AI and Fintech Synergy: Strengthening Financial Stability in Islamic and Conventional Banks, was published in the Journal of Risk and Financial Management. The research examines how artificial intelligence mediates the relationship between Fintech adoption and financial stability across 78 banks in 25 countries that are members of the Organization of Islamic Cooperation .

The findings challenge the assumption that faster technological adoption always improves stability, showing instead that outcomes differ sharply between Islamic and conventional banking systems and vary over time.

AI as a double-edged force in banking stability

The research finds that artificial intelligence plays a decisive but complex role in shaping financial stability. In the short term, higher levels of AI readiness are associated with lower stability in both Islamic and conventional banks. This negative effect reflects the risks banks face during early stages of AI integration, including operational disruptions, data quality problems, cybersecurity exposure, and gaps in regulatory oversight.

AI-driven systems rely heavily on large volumes of data, automated decision-making, and algorithmic models that can amplify errors if governance structures are weak. When these systems are introduced rapidly, banks may struggle to align them with existing risk controls, leading to volatility in performance. The study shows that this adjustment phase is especially sensitive in developing and emerging economies, where regulatory frameworks for AI and Fintech are still evolving.

Over the long run, however, the relationship reverses. As AI systems mature and become embedded in banking operations, they strengthen the positive impact of Fintech on financial stability. AI improves credit assessment, fraud detection, liquidity management, and strategic decision-making, allowing banks to better absorb shocks and manage risk. The research identifies AI not as a standalone driver of stability but as a powerful mediator that determines whether Fintech adoption ultimately reduces or amplifies financial risk.

This time-dependent effect is one of the study’s central contributions. It shows that policy debates framing AI as either a threat or a solution to financial stability miss the core issue. What matters is not whether banks adopt AI, but how they do so and under what regulatory conditions.

Why Islamic and conventional banks respond differently

One of the key findings is the divergence between Islamic and conventional banks in their response to AI-driven Fintech. While both systems experience short-term instability linked to AI adoption, the magnitude and long-term outcomes differ.

Conventional banks, which operate under interest-based models and fewer religious constraints, tend to adopt AI and Fintech more aggressively. Their larger balance sheets and broader access to capital allow them to invest heavily in digital infrastructure, data systems, and advanced analytics. As a result, they show stronger long-term gains from AI-enabled Fintech, even though they also experience greater volatility during the transition phase.

Islamic banks, on the other hand, follow Sharia-based principles that prohibit interest, excessive uncertainty, and speculative behavior. These ethical and legal constraints shape how technology is adopted. The study shows that Islamic banks integrate AI more cautiously, prioritizing compliance, transparency, and risk avoidance. This slower pace reduces exposure to short-term instability but also limits the speed at which long-term efficiency gains materialize.

The authors find that Islamic banks generally exhibit lower average financial stability scores than conventional banks, but with less variability. This suggests a more conservative risk profile that dampens extreme outcomes, both positive and negative. Liquidity management plays a particularly strong stabilizing role in Islamic banks, reflecting their emphasis on asset-backed financing and stricter balance-sheet discipline.

These structural differences mean that a one-size-fits-all approach to AI and Fintech regulation is unlikely to work. Policies designed for conventional banking systems may not align with the governance models and ethical requirements of Islamic finance. The study argues that regulatory frameworks must account for these institutional distinctions if AI-driven innovation is to support stability across diverse banking systems.

Policy lessons from AI-driven Fintech adoption

Beyond its comparative insights, the research carries clear implications for regulators and policymakers. The findings suggest that unmanaged or poorly sequenced AI adoption can weaken financial stability, even when Fintech penetration is high. This is particularly relevant for countries pursuing rapid digitalization as part of broader financial inclusion and economic development strategies.

The study highlights the importance of AI readiness, not just in technical terms but also in governance, ethics, and institutional capacity. Banks operating in jurisdictions with clearer regulatory guidance, stronger data protection rules, and more developed digital infrastructure are better positioned to convert AI-driven Fintech into long-term stability gains.

Macroeconomic conditions also matter. Inflation and unemployment are shown to undermine financial stability in both banking systems, while economic growth supports it. These factors interact with AI adoption, meaning that technology cannot compensate for weak economic fundamentals. Instead, AI amplifies existing strengths and vulnerabilities within financial systems.

Policymakers should focus on phased adoption strategies, robust supervisory frameworks, and cross-border coordination. Given the global nature of AI development and Fintech platforms, regulatory gaps in one country can create spillover risks for others. This is especially relevant in the OIC region, where financial systems vary widely in size, maturity, and regulatory capacity.

The study also points to the need for greater attention to ethical governance. AI systems can introduce new forms of bias, opacity, and systemic risk if their decision-making processes are poorly understood or inadequately monitored. For Islamic banks, aligning AI with Sharia principles adds an additional layer of complexity that requires specialized oversight and expertise.