Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management

Abstract

Research background: Fintech development shapes corporate investment efficiency and economic growth with innovative tools, and can decrease financing constraints of enterprises, enabling direct and indirect financing and furthering inter-bank competition. Crowdfunding- and blockchain-based fintech operations harness deep and maching learning algorithms, augmented and virtual reality technologies, and big data analytics in mobile payment transactions. Purpose of the article: We show that fintechs have reconfigured financial service delivery by harnessing AI-based data-driven algorithms and cloud and blockchain technologies. Fintech optimizes financial organization and services, economic structures and growth, data analysis, and digital banking performance. Machine learning algorithms can streamline payment operation capabilities and process promptness, ensuring smooth operational flows, assessing risks, and detecting frauds and money laundering by historical data and customer behavior analysis across instant payment networks and infrastructures. Methods: Quality tools: AXIS, Eppi-Reviewer, PICO Portal, and SRDR. Search period: July 2023. Search terms: “fintech” + “artificial intelligence algorithms”, “cloud computing technologies”, and “blockchain technologies”. Selected sources: 40 out of 195. Published research inspected: 2023. Data visualization tools: Dimensions and VOSviewer. Reporting quality assessment tool: PRISMA. Findings & value added: Fintech development enables organizational innovation by mitigating information asymmetry and financing limitations while providing financial assistance and tax incentives in relation to products and services. The fintech growth has influenced the dynamic intermediary function of financial institutions in terms of sustainability and economic development. Fintech and natural resources negatively influence, while green innovations and financial development further, environmental sustainability.

Navigating Uncertainty: Enhancing Markowitz Asset Allocation Strategies through Out-of-Sample Analysis

“…The optimal weight (ω p ) in the risky portfolio is established by considering the return (r p ) and standard deviation (σ p ) of the risky portfolio, along with the risk-free rate (r f ). The investor aims to maximize the expected utility by solving the optimization problem shown in Equation (10).…”

“…The application of advanced statistical methods and technological innovations enables more precise risk assessments. Researchers have explored Bayesian methods and utilized machine learning [4][5][6][7][8][9][10][11][12][13][14][15][16][17] to enhance parameter estimations. Additionally, an increasing emphasis on behavioral finance has yielded insights into investor psychology, influencing decisions on asset allocation.…”

Financial big data management and intelligence based on computer intelligent algorithm

“…Among them, Li analyzed the job of monetary specialist organizations in surveying SME store network credit and how they can assist SMEs with acquiring store network supporting through computerized stages utilizing enormous information examination 7 . Lăzăroiu states that machine learning algorithms can streamline the payment operations capabilities and the timeliness of the process, ensure the smooth operation process, assess the risk, and detect fraud and money laundering through historical data and customer behavior instantly payment network and infrastructure analysis 8 . Nguyen uses a multidimensional descriptive analysis to familiarize people with the role of Big Data, Artificial Intelligence, and Machine Learning technologies in the financial technology roadmap and stated that AI and machine learning technologies are relevant to the future challenges of AI ethics, regulatory techniques and smart data utilization 9 .…”

Credit risk assessment of small and micro enterprise based on machine learning

“…[ 48 ], demonstrating its supremacy in prediction accuracy, notably outpacing traditional methodologies like discriminant analysis and logistic regression. With the deepening application of machine learning models, a growing body of scholarly work is turning attention towards integrative research that combines these models with cutting-edge technologies such as the Internet of Things (IoT) and cloud computing [ 49 , 50 ]. This trend benefits innovative research in credit assessment for small and micro enterprises.…”

“…Additionally, the continuous drive to bolster the precision of assessment models remains salient. Previous researches [ [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] ] have crafted predictive models through various methodologies, including expert analysis, statistical techniques, and machine learning. Even though these efforts have achieved commendable success in predictive fidelity, there is ample room for further enhancements and pioneering breakthroughs in the field.…”