eISSN: 3087-4289 editor@aimjournals.com
All Journals
Submit your article

International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

AI-Driven Hyper-Automation for Financial Workflows

DOI
PDF
Dr. Aarav Sharma 1 ,
4 Department of Artificial Intelligence Institute of Advanced Computing and Intelligent Systems New Delhi, India

Abstract

The increasing complexity of financial ecosystems, coupled with stringent regulatory requirements and rapidly evolving customer expectations, has accelerated the adoption of artificial intelligence (AI)-driven automation technologies across banking, insurance, capital markets, and financial technology organizations. Traditional robotic process automation (RPA) has demonstrated significant improvements in operational efficiency by automating repetitive rule-based activities; however, its limitations in handling unstructured information, dynamic decision-making, and adaptive process optimization have motivated the emergence of AI-driven hyper-automation. Hyper-automation integrates generative artificial intelligence, machine learning, process mining, intelligent document processing, predictive analytics, cloud computing, and workflow orchestration into a unified ecosystem capable of continuously optimizing financial operations. Recent studies demonstrate that combining generative AI with process mining enables intelligent process discovery, automated decision support, and adaptive workflow optimization across enterprise financial environments (Krishnan & Bhat, 2025). Furthermore, advances in agentic AI, secure cloud infrastructures, enterprise workflow automation, behavioral biometrics, fraud detection, and AI governance have expanded the capabilities of hyper-automation beyond operational efficiency toward intelligent enterprise transformation.

This research-review paper presents a comprehensive analytical framework for AI-driven hyper-automation in financial workflows by synthesizing recent literature on generative AI, enterprise automation, intelligent financial systems, cloud-native computing, cybersecurity, fraud detection, workflow orchestration, and governance. The study develops an integrated conceptual architecture illustrating how intelligent automation components collaboratively improve operational efficiency, regulatory compliance, cybersecurity resilience, predictive decision-making, and customer service quality. Particular attention is given to process mining, generative AI-assisted decision support, cloud-based orchestration, enterprise DevOps, intelligent APIs, behavioral authentication, and real-time financial analytics. The review further evaluates implementation challenges involving legacy infrastructure modernization, governance, explainability, data privacy, workforce transformation, interoperability, and regulatory compliance.

The findings indicate that AI-driven hyper-automation represents a strategic evolution from isolated automation initiatives toward intelligent autonomous financial ecosystems capable of continuous learning and adaptive optimization. Nevertheless, successful implementation requires balanced governance frameworks, standardized AI policies, robust cybersecurity mechanisms, enterprise reliability engineering, and transparent decision-support systems. The proposed framework contributes to the growing body of knowledge by integrating fragmented technological developments into a unified financial hyper-automation model suitable for modern digital enterprises

Keywords

Artificial Intelligence, Hyper-Automation, Financial Workflows, Process Mining

References

Abdul Salam Abdul Karim. (2023). Fault-Tolerant Dual-Core Lockstep Architecture for Automotive Zonal Controllers Using NXP S32G Processors. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 877–885. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7749
A K. Bhat and G. Krishnan, "A Review of Artificial Intelligence in Finance: Driving the Next Wave of Industry Innovation," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2033-2040, doi: 10.1109/ICoICI65217.2025.11252894.
Carolina, I. R. &. I. M. D. N., USA, & Tiwari, S. K. (2025b). Automation Driven Digital Transformation Blueprint: Migrating legacy QA to AI augmented pipelines. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 01–20. https://doi.org/10.64917/feaiml/volume02issue12-01
Chakravartula, K. N. (2025). Optimizing the loan origination system using CRM to improve the agri business workflows. Journal of Information Systems Engineering & Management, 10(36s), 287–294. https://doi.org/10.52783/jisem.v10i36s.6413
Dasari, H. (2025). SITE RELIABILITY ENGINEERING PRACTICES FOR ERROR BUDGET MANAGEMENT IN LARGE-SCALE SYSTEMS. International Journal of Apllied Mathematics, 38(5s), 991–1001. https://doi.org/10.12732/ijam.v38i5s.366
Deshpande, A. a. P. S. (2025). Real-Time encryption and secure communication for sensor data in autonomous systems. Journal of Information Systems Engineering & Management, 10(41s), 41–55. https://doi.org/10.52783/jisem.v10i41s.7585
Fnu, H., Mirza, M.H., Marri, M.R. et al. Blockchain-Assisted Transformer CNN Framework with Optimal Feature Selection for Real-Time Digital Payment Fraud Detection. Int J Comput Intell Syst 19, 70 (2026). https://doi.org/10.1007/s44196-025-01126-6.
G. Krishnan and A. K. Bhat, "Empower Financial Workflows: Hyper Automation Framework Utilizing Generative Artificial Intelligence and Process Mining," 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), Coimbatore, India, 2025, pp. 2041-2047, doi: 10.1109/ICoICI65217.2025.11254280.
Gangula, S. (2026). Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices. The American Journal of Engineering and Technology, 8(01), 07–19. https://doi.org/10.37547/tajet/Volume08Issue01-02
Goyal, K., Mirza, M. H., Nutalapati, P., Chawra, V. S., & Mulla, F. M. (2026). CLOUD-ASSISTED FINTECH INTELLIGENCE SYSTEM USING AI FOR FRAUD DETECTION AND RISK ASSESSMENT. Scientific Culture, 12(1, Part 1), 4603.
Hari Dasari. (2025). Implementing Site Reliability Engineering (SRE) in Legacy Retail Infrastructure. The American Journal of Engineering and Technology, 7(07), 167–179. https://doi.org/10.37547/tajet/Volume07Issue07-16
Hebbar, K. S. (2025). Priority-Aware reactive APIs: Leveraging Spring WebFlux for SLA-Tiered traffic in financial services. European Journal of Electrical Engineering and Computer Science, 9(5), 31–40. https://doi.org/10.24018/ejece.2025.9.5.743
H. K. Krishnamurthy Sukumar, "A Novel Hybrid Grey Wolf Whale Optimization for Effectual Job Scheduling and Resource Distribution in Dynamic Cloud Computing," 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), Nagpur, India, 2025, pp. 1-6, doi: 10.1109/ICSIT65336.2025.11293898.
K. S. Hebbar, “Priority-Aware Reactive APIs: Leveraging Spring WebFlux for SLA-Tiered Traffic in Financial Services,” European Journal of Electrical Engineering and Computer Science, vol. 9, no. 5, pp. 31–40, Sep. 2025, doi: 10.24018/ejece.2025.9.5.743.
Kale, A. (2025). FX Hedging Algorithms for Crypto-Native Companies. International Journal of Advanced Artificial Intelligence Research, 2(10), 09-14. https://doi.org/10.55640/ijaair-v02i10-02
Kanikanti, V. S. N., Tiwari, S. K., Nayan, V., Suryawanshi, S., Banerjee, V. and Ahmed, I. E., "Deep Q-Learning Driven Dynamic Optimal Task Scheduling for Cloud Computing Using Optimal Queuing," 2025 10th International Conference on Information Technology Trends (ITT), Dubai, United Arab Emirates, 2025, pp. 112-117, doi: 10.1109/ITT69610.2025.11352940.
Karim, A. S. A. (2025). MITIGATING ELECTROMAGNETIC INTERFERENCE IN 10G AUTOMOTIVE ETHERNET: HYPERLYNX-VALIDATED SHIELDING FOR CAMERA PCB DESIGN IN ADAS LIGHTING CONTROL. International Journal of Apllied Mathematics, 38(2s), 1257–1268. https://doi.org/10.12732/ijam.v38i2s.718
Karim, A. S. A. (2025). MITIGATING ELECTROMAGNETIC INTERFERENCE IN 10G AUTOMOTIVE ETHERNET: HYPERLYNX-VALIDATED SHIELDING FOR CAMERA PCB DESIGN IN ADAS LIGHTING CONTROL. International Journal of Apllied Mathematics, 38(2s), 1257–1268. https://doi.org/10.12732/ijam.v38i2s.718
Kathi, S. R. (2025). Enterprise-Grade CI/CD pipelines for mixed Java version environments using Jenkins in Non-Containerized environments. Journal of Engineering Research and Sciences, 4(9), 12. https://doi.org/10.55708/js0409002
Kathi, S.R., Joshi, P., Muralidhar, V. and Venugopalan, A., 2026. Secure migration patterns from Java 8 to Java 17 in the mission-critical ecosystem: a risk-driven approach to modernization. Future Technology, 5(2), pp.297-309. doi: 10.55670/fpll.futech.5.2.27.
Kaur, K. 2026. Augmented Business Intelligence for Predictive Customer Segmentation. Frontiers in Business Innovations and Management. 3, 01 (Jan. 2026), 01–14. https://doi.org/10.64917/fbim/Volume03Issue01-01.
Kishore Bandela. (2025). Use Of Recycled Plastic In Asphalt Mixtures For Road Construction. (2025). International Journal of Environmental Sciences, 720-739.
M. A. Hussain, V. B. Meruga, A. K. Rajamandrapu, S. R. Varanasi, S. S. S. Valiveti and A. G. Mohapatra, "Generative AI Sensor Fusion for Secure Digital Twin Ecosystems: A Standardization-Aligned Framework for Cyber-Physical Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3660106.
Modadugu, J. K., Venkata, R. T. P., & Venkata, K. P. (2025b). Philip, P. G. (2026). AI-Based Energy Optimization in Smart Buildings with Renewable Energy Integration: A Construction Project Management Perspective. The American Journal of Engineering and Technology, 8(06), 26–37. https://doi.org/10.37547/tajet/Volume08Issue06-01: Integrating AI and data processing in loan platforms. International Journal of Innovative Research and Scientific Studies, 8(6), 400–409. https://doi.org/10.53894/ijirss.v8i6.9617
Padmanabham Venkiteela (Decemeber 2025) n8n: An Open-Source Workflow Automation Platform for Enterprise Integration and AI-Driven Orchestration, International Journal of Computer Applications. https://doi.org/10.5120/ijca2025926031.
Pai, D. (2025). Prompt Engineering Frameworks for Generative AI in Credit Analysis. https://doi.org/10.52783/jisem.v10i45s.9035.
Pandey, C. P., Upadhyay, H., Kale, A., Joshi, P., & Sri, B. (2026). AI-driven fraud detection and risk forecasting framework for real-time financial transactions. Scientific Culture, 12(1 Part 1), 3425.
Philip, P. G. (2025). Predictive Maintenance Approach for Electric Power Systems Using Machine Learning. The American Journal of Interdisciplinary Innovations and Research, 7(09), 145–160. Retrieved from https://theamericanjournals.com/index.php/tajiir/article/view/ml-predictive-maintenance-power-systems
R. Laheri, "AI-Enhanced Biometric Systems for Insurance: Secure Authentication and Regulatory Compliance," 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India, 2025, pp. 1-6, doi: 10.1109/ICECONF65644.2025.11379513.
Sagar Kesarpu. (2025). Chaos Engineering as a Learning Framework: A Human-Centered Model for Developing High-Reliability Engineering Teams. The American Journal of Engineering and Technology, 7(12), 57–64. https://doi.org/10.37547/tajet/Volume07Issue12-05
Sayyed, Z. (2025). Application Level Scalable Leader Selection Algorithm for Distributed Systems. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3856
Shounik, S. (2025). Redefining Entry-Level Analyst Roles in M&A: Essential Skillsets in the Age of AI-Powered Diligence. The American Journal of Applied Sciences, 7(07), 101–110. https://doi.org/10.37547/tajas/Volume07Issue07-11
Shruti Worlikar. (2025). Real-Time Patient Monitoring and Alerting in Hospitals Using AWS Lake House Architecture. Frontiers in Emerging Computer Science and Information Technology. 2, 08 (Aug. 2025), 07–14. https://doi.org/10.37547/fecsit/Volume02Issue08-02.
Singh, J. (2024). The impact of real-time analytics dashboards on decision-making quality and organizational responsiveness: An empirical study. Journal of Information Systems Engineering and Management, 9(3).
Singh, V. (2024). The impact of artificial intelligence on compliance and regulatory reporting. J. Electrical Systems, 20, 4322-4328.
SinghJatav, D., Amin, M. M., Kodela, S., Nayan, V., Wannous, M., & Khalifa, G. S. (2025, November). Hybrid Reinforcement and Deep Learning Model for Payment Delay Optimization in Supply Chain Finance. In 2025 10th International Conference on Information Technology Trends (ITT) (pp. 170-175). IEEE.
Sravan Kumar Nidiganti. (2025). Robotic Process Automation in Pharmacy Benefit Manager (PBM) Quality. The American Journal of Applied Sciences, 7(07), 93–100. https://doi.org/10.37547/tajas/Volume07Issue07-10.
Thanvi, Y. S. (2026). A Longitudinal Review on the State of Cybersecurity 2022-2025: Workforce, Governance, Risk, and Operational Maturity, and findings from ISACA Global Surveys. American Journal of Technology, 5(2), 39–51. https://doi.org/10.58425/ajt.v5i2.486.
Valiveti, S. S. S. (2025). AI-Driven Behavioral Biometrics for 401(k) Account Security. International Research Journal of Advanced Engineering and Technology, 23-26. https://doi.org/10.55640/irjaet-v02i06-04
Venkiteela, P. (2026). An Enterprise Agentic Architecture Framework for Agentic AI Governance and Scalable Autonomy. Scientific Journal of Computer Science, 2(1), 1–17. https://doi.org/10.64539/sjcs.v2i1.2026.368.

Similar Articles

21-30 of 58

You may also start an advanced similarity search for this article.