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Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk man-agement, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial 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 vector machine 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 offer financial 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, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, 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 simulation across 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 credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, 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+.