A Review of Blockchain Applications For Secure and Transparent Financial Services with Big Data Analysis
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P111Keywords:
Big Data Analytics, Blockchain, Financial Services, Accounting Information, Financial Forecasting, Real-Time ReportingAbstract
Big data is changing the financial sector and could greatly affect the way financial research is done in the future. The implementation of new digital technologies has radically transformed financial services, particularly through big data analytics and blockchain technology. Big data enables companies to work with large, diverse, and moving data to enhance decision-making, forecasting, risk management, and cost optimization in accounting and financial operations. Hadoop and Apache Spark can be used to store, process, and perform real-time analytics on a significant volume of data, supporting more accurate financial reporting and fraud detection. Besides such developments, blockchain enhances transaction security and data integrity by providing decentralised, transparent, and tamper-resistant data structures. The enabling technologies (through cryptographic algorithms, distributed ledger, timestamping, and peer-to-peer networks) provide a high level of confidentiality, traceability, and efficiency. Blockchain has been used in financial services for fraud detection, cross-border payments, smart contracts, credit ratings, and recordkeeping, enabling quicker settlements and reduced intermediary costs. Together, these innovations make accounting information systems more reliable, secure, and efficient, transform conventional financial processes, and contribute to more transparent digital ecosystems.
References
[1] S. B. Shah, “Evaluating the Effectiveness of Machine Learning in Forecasting Financial Market Trends: A Fintech Perspective,” in 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), 2025, pp. 1–6. doi: 10.1109/ICICACS65178.2025.10968297.
[2] A. R. Bilipelli, “Forecasting the Evolution of Cyber Attacks in FinTech Using Transformer-Based Time Series Models,” Int. J. Res. Anal. Rev., vol. 12, no. 3, pp. 1–7, 2023.
[3] H. P. Cyril, “Serialization of Telecom Provisioning Transactions in Distributed Systems,” Int. J. Curr. Eng. Technol., vol. 15, no. 6, pp. 526–533, 2025.
[4] A. Parupalli, “The Evolution of Financial Decision Support Systems : From BI Dashboards to Predictive Analytics,” KOS J. Bus. Manag., vol. 1, no. 1, pp. 1–8, 2023.
[5] T. Shah, “The Role of Customer Data Platforms (CDPs) in Driving Hyper-Personalization in FinTech,” Int. Res. J. Eng. Technol., vol. 12, no. 04, pp. 1–10, 2025.
[6] V. Verma, “Deep Learning-Based Fraud Detection in Financial Transactions: A Case Study Using Real-Time Data Streams,” ESP J. Eng. Technol. Adv., vol. 3, no. 4, pp. 149–157, 2023, doi: 10.56472/25832646/JETA-V3I8P117.
[7] S. G. Kishore Subramanya Hebbar, Harsh Parnerkar, “AI-Driven Real-Time Fraud Detection Using Kafka Streams In Fintech,” Int. J. Appl. Math., vol. 38, no. 6, pp. 770–782, 2025.
[8] A. Subrahmanyam, “Borsa _ Istanbul Review Big data in finance : Evidence and challenges,” Borsa istanbul Rev., vol. 19, no. 4, pp. 283–287, 2019, doi: 10.1016/j.bir.2019.07.007.
[9] V. Prajapati, “Blockchain-Based Decentralized Identity Systems: A Survey of Security, Privacy, and Interoperability,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 3, pp. 1011–1020, Mar. 2025, doi: 10.38124/ijisrt/25mar1062.
[10] Vibhor Pal and Satyadhar Kumar Chintagunta, “Transformer-Based Graph Neural Networks for RealTime Fraud Detection in Blockchain Networks,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1401–1411, Jul. 2023, doi: 10.48175/IJARSCT-11978Y.
[11] G. Sarraf, “Privacy-Preserving Blockchain for Healthcare : Addressing Security Challenges Through Decentralized Architecture,” Int. Res. J., vol. 10, no. 11, pp. 111–117, 2023.
[12] V. Prajapati, “Enhancing Threat Intelligence and Cyber Defense through Big Data Analytics: A Review Study,” J. Glob. Res. Math. Arch., vol. 12, no. 4, pp. 1–10, 2025.
[13] N. Prajapati, “The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and Applications,” ESP J. Eng. Technol. Adv., vol. 5, no. 2, pp. 16–22, 2025, doi: 10.56472/25832646/JETA-V5I2P103.
[14] P. Foroudi, T. C. Melewar, and S. Gupta, “Corporate Logo: History, Definition, and Components,” Int. Stud. Manag. Organ., vol. 47, no. 2, pp. 176–196, 2017, doi: 10.1080/00208825.2017.1256166.
[15] V. Verma, “Optimizing Database Performance For Big Data Analytics And Business Intelligence,” Int. J. Eng. Sci. Math., vol. 13, no. 11, pp. 56–75, 2024.
[16] A. A. Vărzaru, “Assessing Digital Transformation of Cost Accounting Tools in Healthcare,” Int. J. Environ. Res. Public Health, vol. 19, no. 23, 2022, doi: 10.3390/ijerph192315572.
[17] M. Emir and J. Henry, “Empowering Businesses: The Impact of Digital Accounting Tools,” EasyChair Prepr., 2024.
[18] G. L. Thanasas, “The role of Big Data Analytics in Financial Decision-Making and Strategic Accounting,” Tech. Bus. Manag., vol. 10, pp. 17–33, 2024.
[19] S. B. Shah, “Advancing Financial Security with Scalable AI: Explainable Machine Learning Models for Transaction Fraud Detection,” in 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2025, pp. 1–7. doi: 10.1109/ICDCECE65353.2025.11034838.
[20] A. Karras et al., “FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE,” Electronics, vol. 12, no. 22, 2023, doi: 10.3390/electronics12224633.
[21] S. Wawge, “A Survey on the Identification of Credit Card Fraud Using Machine Learning with Precision, Performance, and Challenges,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 4, pp. 1–8, 2025.
[22] H. Antonopoulou, V. Mamalougou, and L. Theodorakopoulos, “The Role of Economic Policy Uncertainty in Predicting Stock Return Volatility in the Banking Industry: A Big Data Analysis,” EmergingScience J., vol. 6, no. 3, pp. 569–577, 2022.
[23] C. Karras, L. Theodorakopoulos, A. Karras, and G. A. Krimpas, “Efficient Algorithms for Range Mode Queries in the Big Data Era,” Information, vol. 15, no. 8, 2024, doi: 10.3390/info15080450.
[24] S. Garg, “AI/ML Driven Proactive Performance Monitoring, Resource Allocation And Effective Cost Management In Saas Operations,” Int. J. Core Eng. Manag., vol. 6, no. 06, pp. 1–11, 2019.
[25] S. Mireslami, L. Rakai, B. H. Far, and M. Wang, “Simultaneous Cost and QoS Optimization for Cloud Resource Allocation,” IEEE Trans. Netw. Serv. Manag., vol. 14, no. 3, pp. 676–689, Sep. 2017, doi: 10.1109/TNSM.2017.2738026.
[26] D. Patel, “Enhancing Banking Security: A Blockchain and Machine Learning- Based Fraud Prevention Model,” Int. J. Curr. Eng. Technol., vol. 13, no. 06, Dec. 2023, doi: 10.14741/ijcet/v.13.6.10.
[27] D. Patel, “Leveraging Blockchain and AI Framework for Enhancing Intrusion Prevention and Detection in Cybersecurity,” Tech. Int. J. Eng. Res., vol. 10, no. 6, pp. 853–858, 2023, doi: 10.56975/tijer.v10i6.158517.
[28] Dhruv Patel and Ritesh Tandon, “Cryptographic Trust Models and Zero-Knowledge Proofs for Secure Cloud Access Control and Authentication,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 2, no. 1, pp. 749–758, Dec. 2022, doi: 10.48175/IJARSCT-7744D.
[29] S. Gupta, K. Gupta, and B. Chandavarkar, “The Role of Cryptography in Cryptocurrency,” 2021, pp. 273–278. doi: 10.1109/ICSCCC51823.2021.9478099.
[30] A. S. Rajasekaran, M. Azees, and F. Al-Turjman, “A comprehensive survey on blockchain technology,” Sustain. Energy Technol. Assessments, vol. 52, p. 102039, 2022, doi: https://doi.org/10.1016/j.seta.2022.102039.
[31] S. Dong, K. Abbas, M. Li, and J. Kamruzzaman, “Blockchain technology and application : an overview,” PeerJ Comput. Sci., pp. 1–50, 2023, doi: 10.7717/peerj-cs.1705.
[32] L. M. Ambrosie, “Myths of tourism institutionalization and Cancún,” Ann. Tour. Res., vol. 54, pp. 65–83, 2015, doi: https://doi.org/10.1016/j.annals.2015.06.002.
[33] J. G. Zwaid, A. H. Mhawesh, and A. H. Hussein, “Confidentiality, integrity and availability of accounting information reflected in enhancing the quality of financial inspections by using hotels as a case study,” African J. Hosp. Tour. Leis., vol. 9, no. 2, pp. 1–17, 2020.
[34] M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Khan, “A review of Blockchain Technology applications for financial services,” 2022. doi: 10.1016/j.tbench.2022.100073.
[35] M. Kherbouche, G. Pisoni, and B. Molnár, “Model to Program and Blockchain Approaches for Business Processes and Workflows in Finance,” Appl. Syst. Innov., vol. 5, no. 1, 2022, doi: 10.3390/asi5010010.
[36] L. Liu, C. Zhang, and C.-C. Fang, “Effects of health-promoting leadership, employee health on employee engagement: employability as moderating variable,” Int. J. Work. Heal. Manag., vol. 15, no. 1, pp. 1–18, 2021, doi: https://doi.org/10.1108/IJWHM-07-2020-0122.
[37] A. Haleem, M. Javaid, R. P. Singh, S. Rab, and R. Suman, “Hyperautomation for the enhancement of automation in industries,” Sensors Int., vol. 2, p. 100124, 2021, doi: 10.1016/j.sintl.2021.100124.
[38] R. Owen, C. an Bhaird, J. Hussain, and T. Botelho, “Blockchain and other innovations in entrepreneurial finance: Implications for future policy,” Strateg. Chang., vol. 28, no. 1, pp. 5–8, 2019, doi: https://doi.org/10.1002/jsc.2250.
[39] R. Yaksick, “Overcoming Supply Chain Finance Challenges Via Blockchain Technology,” in Disruptive Innovation in Business and Finance in the Digital World, Emerald Publishing Limited, 2019. doi: 10.1108/S1569-376720190000020012.
[40] S. Zhang, D. Zhang, J. Zheng, and W. Aerts, “Does policy uncertainty of the blockchain dampen ICO markets?,” Account. Financ., vol. 61, no. S1, pp. 1625–1637, 2021, doi: https://doi.org/10.1111/acfi.12639.
[41] X. Zhang and Y. Han, “Research on the Energy Distribution and Tendency of Blockchain in the E-commerce,” Int. J. Front. Eng. Technol., vol. 1, no. 1, pp. 88–97, 2019, doi: 10.25236/IJFET.2019.010109.
[42] J. Ke and Z. Tang, “Innovation of Supply Chain Finance Model Based on Blockchain Technology,” in International Conference on Cognitive based Information Processing and Applications (CIPA 2021), B. J. Jansen, H. Liang, and J. Ye, Eds., Singapore: Springer Singapore, 2022, pp. 680–686.
[43] S. Alam et al., “Blockchain-based Initiatives: Current state and challenges,” Comput. Networks, vol. 198, p. 108395, 2021, doi: https://doi.org/10.1016/j.comnet.2021.108395.
[44] S. Chandra, S. Muniraj, S. Y. Reddy, V. Raju, B. R. Kumar, and S. koteswararao Katta, “Blockchain and AI Integration in Fintech: Securing Financial Transactions and Ensuring Transparency,” in 2025 IEEE 4th World Conference on Applied Intelligence and Computing (AIC), 2025, pp. 257–261. doi: 10.1109/AIC66080.2025.11212004.
[45] P. Chidambaranathan and V. MuthuPriya, “Risk Prediction in Financial Transactions Using IoT Big Data Analytics,” in 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), 2024, pp. 328–332. doi: 10.1109/ICESC60852.2024.10689762.
[46] Y. Chen and Z. Wen, “Computer Software Application in Blockchain and Big Data Technology,” in 2024 5th International Conference on Information Science and Education (ICISE-IE), 2024, pp. 656–659. doi: 10.1109/ICISE-IE64355.2024.11025479.
[47] T. Saba, K. Haseeb, A. Rehman, and G. Jeon, “Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction,” IEEE Trans. Comput. Soc. Syst., vol. 11, no. 2, pp. 1667–1674, 2024, doi: 10.1109/TCSS.2023.3268592.
[48] K. Balaji, “Blockchain Based Banking Transactions Enabled by Big Data,” in 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE), 2023, pp. 1–6. doi: 10.1109/ICEPE57949.2023.10201613.
[49] M. Akram and A. Sen, “A case study Evaluation of Blockchain for digital identity verification and management in BFSI using Zero-Knowledge Proof,” in 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, pp. 1295–1299. doi: 10.1109/DASA54658.2022.9765121.
