The primary purpose of this paper is to bridge the technology gap between Blockchain and Fintech applications. Blockchain technology is already being explored in a wide number of Fintech sectors. After creating a unique taxonomy for Fintech ecosystems, this paper outlines a number of implementation scenarios. For each of the industries in which blockchain is already in use and has established itself as a complementary technology to traditional systems, we give a taxonomy of use cases. In this procedure, we cover both public and private blockchains. Because it is still believed to be in its infancy, especially when it comes to financial use cases, blockchain has both positive and negative aspects. As a result, it is critical to be aware of all of the open research issues in this field. Our goal is to compile a list of open research challenges related to various aspects of the blockchain’s protocol and application layers. Finally, we will provide a clear understanding of the applications for which blockchain can be valuable, as well as the risks associated with its use in parallel.

This article uses reinforcement learning (RL) to approximate the policy rules of banks participating in a high-value payment system (HVPS). The objective of the RL agents is to learn a policy function for the choice of amount of liquidity provided to the system at the beginning of the day and the rate at which to pay intraday payments. Individual choices have complex strategic effects precluding a closed form solution of the optimal policy, except in simple cases. We show that, in a stylized two-agent setting, RL agents learn the optimal policy that minimizes the cost of processing their individual payments—without complete knowledge of the environment. We further demonstrate that, in more complex settings, both agents learn to reduce the cost of processing their payments and effectively respond to liquidity-delay tradeoff. Our results show the potential of RL to solve liquidity management problems in HVPS and provide new tools to assist policymakers in their mandates of ensuring safety and improving the efficiency of payment systems.

Smart contracts power decentralized financial (DeFi) services but are vulnerable to security exploits that can lead to significant financial losses. Existing security measures often fail to adequately protect these contracts due to the composability of DeFi protocols and the increasing sophistication of attacks. Through a large-scale empirical study of historical transactions from the 37 hacked DeFi protocols, we discovered that while benign transactions typically exhibit a limited number of unique control flows, in stark contrast, attack transactions consistently introduce novel, previously unobserved control flows. Building on these insights, we developed CrossGuard, a novel framework that enforces control flow integrity onchain to secure smart contracts. Crucially, CrossGuard does not require prior knowledge of specific hacks. Instead, configured only once at deployment, it enforces control flow whitelisting policies and applies simplification heuristics at runtime. This approach monitors and prevents potential attacks by reverting all transactions that do not adhere to the established control flow whitelisting rules. Our evaluation demonstrates that CrossGuard effectively blocks 35 of the 37 analyzed attacks when configured only once at contract deployment, maintaining a low false positive rate of \(0.26\%\) and minimal additional gas costs. These results underscore the efficacy of applying control flow integrity to smart contracts, significantly enhancing security beyond traditional methods and addressing the evolving threat landscape in the DeFi ecosystem.