搜索结果：Fintech

The advancement of information technology has propelled payment systems from conventional methods to technology-based solutions, such as e-wallets and Fintech. Fintech, a fusion of technology and financial services, has evolved into an online business model enabling fast and remote transactions. This research discusses the progress of information technology influencing payment systems, particularly in the realm of Fintech. The primary focus is on the Fintech application OVO and its impact on tenants at the International Plaza Mall in Palembang. This study employs the System Usability Scale or SUS to evaluate the Usability of the OVO application, emphasizing aspects like effectiveness, efficiency, and user satisfaction. The research is descriptive and quantitative, with a sample of 50 respondents from Mall IP tenants. Data is collected through SUS questionnaires and analyzed using SPSS. The evaluation indicates that the OVO application has high Usability, with an SUS score of 87.05 or Grade A, signifying an Excellent rating. It suggests that the OVO application provides a comfortable user experience, particularly in electronic financial transactions.

This study measures the impact of COVID-19 outbreaks on financial technology (FinTech) lending in Indonesia. Using monthly FinTech data published by Financial Services Authority (OJK) over the period 2018M02-2021M04, the article examines the impact of COVID-19 started on March 2020 on FinTech by adopting an interrupted time series (ITS) experiment. The estimation shows that the COVID-19 outbreaks negatively affect changes in FinTech lending level in Indonesia, but the changes in the trend are positive. Moreover, the COVID-19 has been found to have a negative and statistically significant effect on the 90-day success loan settlement rate level. However, COVID-19 has positive and statistically significant effects on the 90-day default rate of loan repayment level. These estimation results recommend that the financial services authority of Indonesia should intensively promote various innovative financial technology (FinTech) lending post-COVID-19 to increase digital financial inclusion by providing peer to peer lending (P2P) to unbanked populations.

This paper investigates the impact of financial technology (FinTech) on the financial sustainability (FS) of commercial banks. We employ a three-stage network DEA-Malmquist model to evaluate the FS performance of 104 Chinese commercial banks from 2015 to 2023. A two-way fixed effects model is utilized to examine the effects of FinTech on FS, revealing a significant negative relationship. Further mechanistic analysis indicates that FinTech primarily undermines FS by eroding banks' loan efficiency and profitability. Notably, banks with more patents or listed status demonstrate greater resilience to FinTech disruptions. These findings help banks identify external risks stemming from FinTech development, and by elucidating the mechanisms underlying FS, enhance their capacity to monitor and manage FS in the era of rapid FinTech advancement.

Financial systems have a growing reliance on computer-based and distributed systems, making FinTech systems vulnerable to advanced and quickly emerging cyber-criminal threats. Traditional security systems and fixed machine learning systems cannot identify more intricate fraud schemes whilst also addressing real-time performance and trust demands. This paper presented an Adaptive Neuro-Fuzzy Blockchain-AI Framework (ANFB-AI) to achieve security in FinTech transactions by detecting threats using intelligent and decentralized algorithms. The framework combines both an immutable, transparent and tamper resistant layer of a permissioned blockchain to maintain the immutability, transparency and resistance to tampering of transactions, and an adaptive neuro-fuzzy learning model to learn the presence of uncertainty and behavioural drift in fraud activities. An explicit mathematical model is created to explain the transaction integrity, adaptive threat classification, and unified risk based decision-making. The proposed framework uses Proof-of-Authority consensus to overcome low-latency validation of transactions and scalable real-time financial services. Massive simulations are performed i

Employing a comprehensive survey of micro and small enterprises (MSEs) and the Digital Financial Inclusion Index in China, this study investigates the influence of fintech on MSE innovation empirically. Our findings indicate that fintech advancement substantially enhances the likelihood of MSEs engaging in innovative endeavors and boosts both the investment and outcomes of their innovation processes. The underlying mechanisms are attributed to fintech's role in fostering long-term strategic incentives and investment in human capital. This includes the use of promotions and stock options as rewards, rather than traditional perks like gifts or trips, the attraction of a greater number of university graduates, and the increase in both training expenses and the remuneration of technical staff. Our heterogeneity analysis reveals that fintech exerts a more pronounced effect on MSEs situated in economically developed areas, those that are five years old or younger, and businesses with limited assets and workforce. Additionally, we uncover that fintech stimulates the innovation of MSEs' independent research and development (R\&D) efforts. This paper contributes to the understanding of

This paper explores the impact of banking fintech on reducing financial risks in the agricultural supply chain, focusing on the secondary allocation of commercial credit. The study constructs a three-player evolutionary game model involving banks, core enterprises, and SMEs to analyze how fintech innovations, such as big data credit assessment, blockchain, and AI-driven risk evaluation, influence financial risks and access to credit. The findings reveal that banking fintech reduces financing costs and mitigates financial risks by improving transaction reliability, enhancing risk identification, and minimizing information asymmetry. By optimizing cooperation between banks, core enterprises, and SMEs, fintech solutions enhance the stability of the agricultural supply chain, contributing to rural revitalization goals and sustainable agricultural development. The study provides new theoretical insights and practical recommendations for improving agricultural finance systems and reducing financial risks. Keywords: banking fintech, agricultural supply chain, financial risk, commercial credit, SMEs, evolutionary game model, big data, blockchain, AI-driven risk evaluation.

Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology, and acronyms complicate effective retrieval and synthesis. This paper introduces an agentic RAG architecture designed to address these challenges through a modular pipeline of specialized agents. The proposed system supports intelligent query reformulation, iterative sub-query decomposition guided by keyphrase extraction, contextual acronym resolution, and cross-encoder-based context re-ranking. We evaluate our approach against a standard RAG baseline using a curated dataset of 85 question--answer--reference triples derived from an enterprise fintech knowledge base. Experimental results demonstrate that the agentic RAG system outperforms the baseline in retrieval precision and relevance, albeit with increased latency. These findings suggest that structured, multi-agent methodologies offer a promising direction for enhancing retrieval robustness in complex, domain-specific settings.

The growing use of machine learning in cloud environments raises critical concerns about data security and privacy, especially in finance. Fully Homomorphic Encryption (FHE) offers a solution by enabling computations on encrypted data, but its high computational cost limits practicality. In this paper, we propose PP-FinTech, a privacy-preserving scheme for financial applications that employs a CKKS-based encrypted soft-margin SVM, enhanced with a hybrid kernel for modeling non-linear patterns and an adaptive thresholding mechanism for robust encrypted classification. Experiments on the Credit Card Approval dataset demonstrate comparable performance to the plaintext models, highlighting PP-FinTech's ability to balance privacy, and efficiency in secure financial ML systems.

Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capital allocation strategies, offering new perspectives on how companies are assessed and financed. However, for a new financial architecture based on blockchain and advancements in technology -- what is commonly referred to as Fintech -- to replace, in whole or in part, traditional finance, it will need to overcome significant challenges such as regulation, environmental sustainability, its association with illegal activities, and achieving greater efficiency in cryptocurrency markets. For this reason, the future of Fintech is likely to be more conventional -- yet also more transparent, efficient, and regulated -- ultimately evolving to resemble the traditional finance we know.

Fintech provides technological services to increase operational efficiency in financial institutions, but traditional perimeter-based defense mechanisms are insufficient against evolving cyber threats like insider attacks, malware intrusions, and Advanced Persistent Threats (APTs). These vulnerabilities expose Fintech organizations to significant risks, including financial losses and data breaches. To address these challenges, this paper proposes a blockchain-integrated Zero Trust framework, adhering to the principle of "Never Trust, Always Verify." The framework uses Ethereum smart contracts to enforce Multi Factor Authentication (MFA), Role-Based Access Control (RBAC), and Just-In-Time (JIT) access privileges, effectively mitigating credential theft and insider threats, the effect of malware and APT attacks. The proposed solution transforms blockchain into a Policy Engine (PE) and Policy Enforcement Point (PEP), and policy storage, ensuring immutable access control and micro-segmentation. A decentralized application (DApp) prototype was developed and tested using STRIDE threat modeling, demonstrating resilience against spoofing, tampering, and privilege escalation. Comparative an

The emergence of distributed systems has revolutionized the financial technology (Fintech) landscape, offering unprecedented opportunities for enhancing security, scalability, and efficiency in financial operations. This paper explores the role of distributed systems in Fintech, analyzing their architecture, benefits, challenges, and applications. It examines key distributed technologies such as blockchain, decentralized finance (DeFi), and distributed ledger technology (DLT), and their impact on various aspects of the financial industry, and future directions for distributed systems in Fintech.

While Large Language Models (LLMs) excel at tool calling, deploying these capabilities in regulated enterprise environments such as fintech presents unique challenges due to on-premises constraints, regulatory compliance requirements, and the need to disambiguate large, functionally overlapping toolsets. In this paper, we present a comprehensive study of tool retrieval methods for enterprise environments through the development and deployment of ScaleCall, a prototype tool-calling framework within Mastercard designed for orchestrating internal APIs and automating data engineering workflows. We systematically evaluate embedding-based retrieval, prompt-based listwise ranking, and hybrid approaches, revealing that method effectiveness depends heavily on domain-specific factors rather than inherent algorithmic superiority. Through empirical investigation on enterprise-derived benchmarks, we find that embedding-based methods offer superior latency for large tool repositories, while listwise ranking provides better disambiguation for overlapping functionalities, with hybrid approaches showing promise in specific contexts. We integrate our findings into ScaleCall's flexible architecture a

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI and (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech

Algorithmic lending has transformed the consumer credit landscape, with machine learning models commonly facilitating underwriting decisions. To comply with fair lending laws, these algorithms exclude legally protected characteristics, such as race and gender. Yet algorithmic underwriting can still inadvertently favor certain groups, prompting concerns about whether lending algorithms exhibit discriminatory behavior. Using proprietary loan-level data from a major U.S. fintech platform, we audit lending decisions across approximately 80,000 personal loans. We find that loans made to men and Black borrowers yielded lower profits than loans to other groups, suggesting that men and Black borrowers benefited from relatively favorable pricing. We trace these disparities to miscalibration in the platform's underwriting model, which overestimates risk for women and underestimates risk for Black borrowers. We then show that one could correct this miscalibration -- and the corresponding disparities -- by including race and gender in underwriting models, illustrating a tension between competing notions of fairness.

The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework (CNTMF), a proposed framework designed to address the risks in these ecosystems, such as oracle manipulation and cross-chain exploits. CNTMF represents a proposed extension of established methodologies like STRIDE, OWASP Top 10, NIST frameworks, LINDDUN, and PASTA, while incorporating tailored components including Hybrid Layer Analysis, the CRYPTOQ mnemonic for cryptocurrency-specific risks, and an AI-Augmented Feedback Loop. Drawing on real-world data from 2025 incidents, CNTMF supports data-driven mitigation to reduce losses, which totalled approximately $2.47 billion in the first half of 2025 across 344 security events (CertiK via GlobeNewswire, 2025; Infosecurity Magazine, 2025). Its phases guide asset mapping, risk profiling, prioritisation, mitigation, and iterative feedback. This supports security against evolving risks like state-sponsored attacks.

Gamification has the potential to make significant contributions to financial product delivery, Fintech services, and inclusive growth. The integration of gamification into FinTech applications has shown a positive correlation with the social impact theory. Utilizing gamification in a sustainable and effective manner can be crucial for long-term prospects in the FinTech industry. Therefore, it is essential to develop efficient and modern financial software that improves the customer experience. The current literature aims to contribute to this area by highlighting the relationship between interrelated theories and the key factors to consider when designing a gamified element. This study aims to explore the effects of gamification on altering user intention and its significant influence on customer value propositions.

Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory study on a correlation between reinforcement learning and Fintech to highlight the prediction accuracy, complexity, scalability, risks, profitability and performance. Major uses of reinforcement learning in finance or Fintech include portfolio optimization, credit risk reduction, investment capital management, profit maximization, effective recommendation systems, and better price setting strategies. Several studies have addressed the actual contribution of reinforcement learning to the performance of financial institutions. The latest studies included in this survey are publications from 2018 onward. The survey is conducted using PRISMA technique which focuses on the reporting of reviews and is based on a checklist and four-phase flow diagram. The conducted survey indicates that the performance of RL-based strategies in Fintech fields proves to perform considerably

The integration of Large Language Models (LLMs) into financial technology (FinTech) has revolutionized the analysis and processing of complex financial data, driving advancements in real-time decision-making and analytics. With the growing trend of deploying AI models on edge devices for financial applications, ensuring the privacy of sensitive financial data has become a significant challenge. To address this, we propose DPFinLLM, a privacy-enhanced, lightweight LLM specifically designed for on-device financial applications. DPFinLLM combines a robust differential privacy mechanism with a streamlined architecture inspired by state-of-the-art models, enabling secure and efficient processing of financial data. This proposed DPFinLLM can not only safeguard user data from privacy breaches but also ensure high performance across diverse financial tasks. Extensive experiments on multiple financial sentiment datasets validate the effectiveness of DPFinLLM, demonstrating its ability to achieve performance comparable to fully fine-tuned models, even under strict privacy constraints.

India's linguistic diversity presents both opportunities and challenges for fintech platforms. While the country has 31 major languages and over 100 minor ones, only 10\% of the population understands English, creating barriers to financial inclusion. We present a multilingual conversational AI system for a financial assistance use case that supports code-mixed languages like Hinglish, enabling natural interactions for India's diverse user base. Our system employs a multi-agent architecture with language classification, function management, and multilingual response generation. Through comparative analysis of multiple language models and real-world deployment, we demonstrate significant improvements in user engagement while maintaining low latency overhead (4-8\%). This work contributes to bridging the language gap in digital financial services for emerging markets.