Articles Open Access
Intelligent Robotic Process Automation Frameworks in Modern Financial Service Industry Operations
MIT Academy of Engineering Pune, India
Abstract
Intelligent Robotic Process Automation (IRPA) represents a convergence of robotic process automation, artificial intelligence, machine learning, and cognitive computing technologies designed to enhance operational efficiency in the financial services industry. Financial institutions are increasingly adopting IRPA frameworks to automate complex, rule-based, and semi-structured processes such as customer onboarding, fraud detection, regulatory compliance, loan processing, and claims management. Unlike traditional automation systems, IRPA enables intelligent decision-making through data learning capabilities, natural language processing, and predictive analytics.
This study examines the role of IRPA frameworks in transforming modern financial service operations by improving accuracy, reducing operational costs, and increasing scalability. The research explores how banks, insurance companies, and investment firms integrate intelligent automation to streamline workflows and enhance customer experience. It also highlights the challenges associated with implementation, including cybersecurity risks, integration with legacy systems, workforce displacement concerns, and regulatory compliance complexities.
A mixed-method approach is proposed to analyze both qualitative and quantitative dimensions of IRPA adoption. The study evaluates operational performance improvements and identifies critical success factors influencing implementation. Findings suggest that IRPA significantly enhances productivity, reduces human error, and supports real-time decision-making. The research contributes to understanding how intelligent automation reshapes financial ecosystems and supports digital transformation strategies in the global financial services industry.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 5 |
| Number | 2 |
| Pages | 7836-7843 |
| Published | 2021-03-03 |
| Issue | Vol. 5 No. 2 (2021): International Research Journal of Innovative Engineering (IRJIE) |
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2. Puthal, D., Sahoo, B., Mishra, S., & Swain, S. (2015). Cloud computing security issues and challenges. IEEE Cloud Computing, 2(1), 1–7.
3. Satzger, B., Hummer, W., Inzinger, C., & Leitner, P. (2013). Elastic cloud orchestration. IEEE Internet Computing, 17(2), 29–37. https://doi.org/10.1109/MIC.2013.14
4. Soltesz, S., et al. (2007). Container-based operating system virtualization. ACM SIGOPS Operating Systems Review, 41(2), 275–287.
5. Vaquero, L. M., Rodero-Merino, L., Caceres, J., & Lindner, M. (2008). A break in the clouds: Towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1), 50–55.
6. Weerasiri, D., Barukh, M. C., Benatallah, B., Sheng, Q. Z., & Ranjan, R. (2017). A taxonomy and survey of cloud resource orchestration techniques. ACM Computing Surveys, 50(2), 1–41. https://doi.org/10.1145/3054177
7. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7–18. https://doi.org/10.1007/s13174-010-0007-6
8. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672
9. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms. Future Generation Computer Systems, 25(6), 599–616. https://doi.org/10.1016/j.future.2008.12.001
10. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
11. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
12. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
13. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
14. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
15. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
16. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
17. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
18. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
19. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
20. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
21. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
22. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
23. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
24. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
25. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
26. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
27. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
28. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
29. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
30. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
31. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
32. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
33. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
34. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
35. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
36. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
37. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
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