Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management
Generative artificial intelligence algorithms in Internet of Things blockchain-based fintech management

Summary/Abstract: Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithmscan provide real-time personalized financial service access, strengthen risk management, andmanage, monitor, and mitigate transaction operational risks by operational credit risk man-agement, suspicious financial transaction abnormal pattern detection, and synthetic financialdata-based fraud simulation. Blockchain technologies, automated financial planning andinvestment advice services, and risk scoring and fraud detection tools can be leveraged infinancial trading forecasting and planning, cryptocurrency transactions, and financial work-flow automation and fraud detection. Algorithmic trading and fraud detection tools, distrib-uted ledger and cryptocurrency technologies, and ensemble learning and support vectormachine algorithms are pivotal in predictive analytics-based risk mitigation, customer behav-ior and preference-based financial product and service personalization, and financial transac-tion and fraud detection automation. Credit scoring and risk management tools can offerfinancial personalized recommendations based on customer data, behavior, and preferences,in addition to transaction history, by generative adversarial and deep learning recurrent neu-ral networks.Purpose of the article: We show that blockchain and edge computing technologies, generativeartificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools canbe harnessed in financial decision-making processes and loan default rate mitigation fortransaction, payment, and credit process efficiency. Generative and predictive artificial intelli-gence (AI) algorithmic trading systems can drive coherent customer service operations, pro-vide tailored financial and investment advice, and influence financial decision processing,while performing real-time risk assessment and financial and trading risk scenario simulationacross fluctuating market conditions. Fraud and money laundering prevention tools, block-chain and financial transaction technologies, and federated and decentralized machine learn-ing algorithms can articulate algorithmic profiling-based transaction data patterns and struc-tures, credit assessment, loan repaying likelihood prediction, and interest rate and creditlending risk management by real-time financial pattern and economic forecast-based creditanalysis across investment payment and transaction record infrastructures.Methods: Research published between 2023 and 2024 was identified and analyzed acrossProQuest, Scopus, and the Web of Science databases by use of screening and quality assess-ment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, Distill-erSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+.Findings & value added: The main value added derived from the systematic literature reviewis that generative AI-based operational risk management, fraud detection, and transactionmonitoring tools can provide personalized financial support and services and clarify financialand credit decisions and operations by financial decision-making process automation in dy-namic business environments based on fraud detection capabilities and transaction data anal-ysis and assessment. The benefits for theory and current state of the art are that credit risk andfinancial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algo-rithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and moneylaundering and fraudulent payment detection. Policy implications reveal that investmentmanagement and algorithmic credit scoring tools can streamline financial activity operationalefficiency, design financial planning analysis and forecasting, and carry out financial serviceand transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation andimproving personalized experiences.

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