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Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis

Olatunde Gaffar Ayoola Olamilekan Sikiru Mary Otunba Adedoyin Adeola Adenuga

Subject area: Science,Engineering and Technology  ·  Area of research: Intelligent Workflow

Abstract

Intelligent workflow orchestration represents a transformative approach to financial operations, enabling organizations to automate and optimize complex processes such as expense attribution and profitability analysis. Traditionally, these tasks relied on static, rule-based systems and manual data handling, often resulting in inefficiencies, inaccuracies, and delayed decision-making. By integrating advanced technologies?such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and business process management systems (BPMS)?intelligent orchestration provides dynamic, scalable, and data-driven solutions that enhance both the speed and precision of financial workflows. In the context of expense attribution, intelligent orchestration systems can automatically classify and route cost data across various business dimensions?such as departments, product lines, geographies, and customer segments?based on real-time business rules and predictive models. These systems leverage structured and unstructured data from diverse sources, including enterprise resource planning (ERP) systems, customer relationship management (CRM) tools, invoices, and external feeds. Machine learning models can identify patterns in expense behavior, detect anomalies, and continuously refine attribution logic, thereby reducing human intervention and enhancing auditability. On the profitability analysis front, intelligent orchestration facilitates the seamless integration of expense data with revenue streams, enabling granular insights into product, customer, and channel-level profitability. This level of analysis empowers decision-makers to optimize resource allocation, pricing strategies, and operational efficiency. Additionally, intuitive dashboards and real-time analytics support more proactive and informed financial planning. This explores the architecture, enabling technologies, implementation strategies, and real-world applications of intelligent workflow orchestration in finance. It also addresses critical challenges, including data governance, interoperability, and organizational change management. As businesses strive for greater agility and intelligence in financial operations, intelligent workflow orchestration emerges as a pivotal capability for driving sustainable profitability and strategic competitiveness in a data-centric economy.

Keywords

Intelligent workflow, Orchestration, Expense, Profitability analysis

References

[1] Ajonbadi Adeniyi H., AboabaMojeed-Sanni, B. and Otokiti, B.O., 2015. Sustaining competitive advantage in medium-sized enterprises (MEs) through employee social interaction and helping behaviours. Journal of Small Business and Entrepreneurship, 3(2), pp.1-16.

[2] Ajonbadi, H.A., Lawal, A.A., Badmus, D.A. and Otokiti, B.O., 2014. Financial control and organisational performance of the Nigerian small and medium enterprises (SMEs): A catalyst for economic growth. American Journal of Business, Economics and Management, 2(2), pp.135-143.

[3] Ajonbadi, H.A., Otokiti, B.O. and Adebayo, P., 2016. The efficacy of planning on organisational performance in the Nigeria SMEs. European Journal of Business and Management, 24(3), pp.25-47.

[4] Akinbola, O.A. and Otokiti, B.O., 2012. Effects of lease options as a source of finance on profitability performance of small and medium enterprises (SMEs) in Lagos State, Nigeria. International Journal of Economic Development Research and Investment, 3(3), pp.70-76.

[5] Akpan, U.U., Awe, T.E. and Idowu, D., 2019. Types and frequency of fingerprint minutiae in individuals of Igbo and Yoruba ethnic groups of Nigeria. Ruhuna Journal of Science, 10(1).

[6] Amos, A.O., Adeniyi, A.O. and Oluwatosin, O.B., 2014. Market based capabilities and results: inference for telecommunication service businesses in Nigeria. European Scientific Journal, 10(7).

[7] Asch, M., Moore, T., Badia, R., Beck, M., Beckman, P., Bidot, T., Bodin, F., Cappello, F., Choudhary, A., De Supinski, B. and Deelman, E., 2018. Big data and extreme -scale computing: Pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry. The International Journal of High Performance Computing Applications, 32(4), pp.435-479.

[8] Awe, E.T. and Akpan, U.U., 2017. Cytological study of Allium cepa and Allium sativum.

[9] Brocchi, C., Brown, B., Machado, J. and Neiman, M., 2016. Using agile to accelerate your data transformation. McKinsey & Company. Available at: https://www. mckinsey. com/businessfunctions/digital-mckinsey/our- insights/using-agile-to-accelerate-your-data- transformation (Accessed: 5 August 2019).

[10] Bruck, C., 2017. Challenges and opportunities of Data Governance in private and public organizations. Vienna University of Economics and Business.

[11] Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X. and Reis, M.S., 2016. How can SMEs benefit from big data? Challenges and a path forward. Quality and reliability engineering international, 32(6), pp.2151-2164.

[12] Cristani, M., Bertolaso, A., Scannapieco, S. and Tomazzoli, C., 2018. Future paradigms of automated processing of business documents. International Journal of Information Management, 40, pp.67-75.

[13] Davenport, T.H. and Westerman, G., 2018. Digital Transformation. Harvard Business Review.

[14] Derbyshire, J. and Wright, G., 2017. Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation. International Journal of Forecasting, 33(1), pp.254-266.

[15] Donepudi, P.K., 2018. Application of artificial intelligence in automation industry. Asian Journal of Applied Science and Engineering, 7(1), pp.7-20.

[16] Dumas, M., Rosa, L.M., Mendling, J. and Reijers, A.H., 2018. Fundamentals of business process management. Springer-Verlag.

[17] Escorcia, A., Mohan, R. and Saputelli, L., 2017, October. Improving Governance of Integrated Reservoir and Information Management Leveraging Business Process Management and Workflow Automation. In SPE Annual Technical Conference and Exhibition? (p. D021S012R005). SPE.

[18] Gal, R., Morag, N. and Shilkrot, R., 2018, December. Visual-linguistic methods for receipt field recognition. In Asian Conference on Computer Vision (pp. 542-557). Cham: Springer International Publishing.

[19] Ganguly, K. and Rai, S.S., 2018. Evaluating the key performance indicators for supply chain information system implementation using IPA model. Benchmarking: An International Journal, 25(6), pp.1844-1863.

[20] Gomber, P., Kauffman, R.J., Parker, C. and Weber, B.W., 2018. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1), pp.220-265.

[21] Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value from big data analytics: A research framework. Journal of management information systems, 35(2), pp.388-423.

[22] Hobert, K.A., Woodbridge, M., Mariano, J. and Tay, G., 2017. Magic quadrant for content services platforms. Gartner, Stamford, CT, available at: https://b2bsalescafe. files. wordpress. com/2017/11/magic-quadrant-for- content-services-platforms-oct-2017. pdf (accessed 15 October 2022).

[23] Hosseini, S., Röglinger, M. and Schmied, F., 2017, December. Omni-Channel Retail Capabilities: An Information Systems Perspective. In ICIS.

[24] Kannan, P.K., Reinartz, W. and Verhoef, P.C., 2016. The path to purchase and attribution modeling: Introduction to special section. International Journal of Research in Marketing, 33(3), pp.449-456.

[25] Khalifa, S., Elshater, Y., Sundaravarathan, K., Bhat, A., Martin, P., Imam, F., Rope, D., Mcroberts, M. and Statchuk, C., 2016. The six pillars for building big data analytics ecosystems. ACM Computing Surveys (CSUR), 49(2), pp.1-36.

[26] Koltay, T., 2016. Data governance, data literacy and the management of data quality. IFLA journal, 42(4), pp.303-312.

[27] Laszewski, T., Arora, K., Farr, E. and Zonooz, P., 2018. Cloud Native Architectures: Design high-availability and cost-effective applications for the cloud. Packt Publishing Ltd.

[28] Lawal, A.A., Ajonbadi, H.A. and Otokiti, B.O., 2014. Leadership and organisational performance in the Nigeria small and medium enterprises (SMEs). American Journal of Business, Economics and Management, 2(5), p.121.

[29] Lawal, A.A., Ajonbadi, H.A. and Otokiti, B.O., 2014. Strategic importance of the Nigerian small and medium enterprises (SMES): Myth or reality. American Journal of Business, Economics and Management, 2(4), pp.94-104.

[30] Lee, S., Cho, C., Hong, E.K. and Yoon, B., 2016. Forecasting mobile broadband traffic: Application of scenario analysis and Delphi method. Expert Systems with Applications, 44, pp.126-137.

[31] Li, F., Nucciarelli, A., Roden, S. and Graham, G., 2016. How smart cities transform operations models: A new research agenda for operations management in the digital economy. Production Planning & Control, 27(6), pp.514-528.

[33] Mehta, N. and Devarakonda, M.V., 2018. Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?. Journal of Allergy and Clinical Immunology, 141(6), pp.2019-2021.

[34] Monciardini, D., 2016. Lawyers, Accountants and Financial Analysts: The'Architects' of the New EU Regime of Corporate Accountability. Oñati Socio-Legal Series, 6(3).

[35] Moretti, L., 2018. Phase 5: Execute. In Distribution Strategy: The BESTX® Method for Sustainably Managing Networks and Channels (pp. 125-163). Cham: Springer International Publishing.

[36] Morton, C., Anable, J. and Nelson, J.D., 2017. Consumer structure in the emerging market for electric vehicles: Identifying market segments using cluster analysis. International Journal of Sustainable Transportation, 11(6), pp.443-459.

[37] Nagar, G., 2018. Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, pp.78-94.

[38] Navarro, L.F.M., 2017. Investigating the influence of data analytics on content lifecycle management for maximizing resource efficiency and audience impact. Journal of Computational Social Dynamics, 2(2), pp.1-22.

[39] Ng, A.W., 2018. From sustainability accounting to a green financing system: Institutional legitimacy and market heterogeneity in a global financial centre. Journal of cleaner production, 195, pp.585-592.

[40] Novotny, P., Zhang, Q., Hull, R., Baset, S., Laredo, J., Vaculin, R., Ford, D.L. and Dillenberger, D.N., 2018. Permissioned blockchain technologies for academic publishing. Information Services and Use, 38(3), pp.159-171.

[41] Ogundipe F, Sampson E, Bakare OI, Oketola O, Folorunso A. Digital Transformation and its Role in Advancing the Sustainable Development Goals (SDGs). transformation. 2019;19:48.

[42] Ongsulee, P., Chotchaung, V., Bamrungsi, E. and Rodcheewit, T., 2018, November. Big data, predictive analytics and machine learning. In 2018 16th international conference on ICT and knowledge engineering (ICT&KE) (pp. 1- 6). IEEE.

[43] Oni, O., Adeshina, Y.T., Iloeje, K.F. and Olatunji, O.O., ARTIFICIAL INTELLIGENCE MODEL FAIRNESS AUDITOR FOR LOAN SYSTEMS. Journal ID, 8993, p.1162.

[44] Osabuohien, F.O., 2017. Review of the environmental impact of polymer degradation. Communication in Physical Sciences, 2(1).

[45] Osabuohien, F.O., 2019. Green Analytical Methods for Monitoring APIs and Metabolites in Nigerian Wastewater: A Pilot Environmental Risk Study. Communication In Physical Sciences, 4(2), pp.174-186.

[46] Otokiti, B.O. and Akinbola, O.A., 2013. Effects of lease options on the organizational growth of small and medium enterprise (SME’s) in Lagos State, Nigeria. Asian Journal of Business and Management Sciences, 3(4), pp.1-12.

[47] Otokiti, B.O. and Akorede, A.F., 2018. Advancing sustainability through change and innovation: A co -evolutionary perspective. Innovation: Taking creativity to the market. Book of Readings in Honour of Professor SO Otokiti, 1(1), pp.161-167.

[48] Otokiti, B.O., 2012. Mode of entry of multinational corporation and their performance in the Nigeria market (Doctoral dissertation, Covenant University).

[49] Otokiti, B.O., 2017. A study of management practices and organisational performance of selected MNCs in emerging market-A Case of Nigeria. International Journal of Business and Management Invention, 6(6), pp.1-7.

[50] Otokiti, B.O., 2017. Social media and business growth of women entrepreneurs in Ilorin metropolis. International Journal of Entrepreneurship, Business and Management, 1(2), pp.50-65.

[51] Otokiti, B.O., 2018. Business regulation and control in Nigeria. Book of readings in honour of Professor SO Otokiti, 1(2), pp.201-215.

[52] Peral, J., Maté, A. and Marco, M., 2017. Application of data mining techniques to identify relevant key performance indicators. Computer Standards & Interfaces, 54, pp.76-85.

[53] Reddicharla, N., Meqbali, N.A., AlSelaiti, I.H. and Singh, S., 2017, November. Best Practices for Successful Implementation of Integrated Asset Model Based Well and Reservoir Workflow Automation-A Practical Learning Experience from Mature Brown Fields. In Abu Dhabi International Petroleum Exhibition and Conference (p. D031S077R005). SPE.

[54] Riikkinen, M., Saarijärvi, H., Sarlin, P. and Lähteenmäki, I., 2018. Using artificial intelligence to create value in insurance. International Journal of Bank Marketing, 36(6), pp.1145-1168.

[55] Roden, S., Nucciarelli, A., Li, F. and Graham, G., 2017. Big data and the transformation of operations models: a framework and a new research agenda. Production Planning & Control, 28(11-12), pp.929-944.

[56] Roy, S., LaFramboise, W.A., Nikiforov, Y.E., Nikiforova, M.N., Routbort, M.J., Pfeifer, J., Nagarajan, R., Carter, A.B. and Pantanowitz, L., 2016. Next-generation sequencing informatics: challenges and strategies for implementation in a clinical environment. Archives of pathology & laboratory medicine, 140(9), pp.958-975.

[57] Salvaris, M., Dean, D. and Tok, W.H., 2018. Deep learning with azure. Building and Deploying Artificial Intelligence Solutions on Microsoft AI Platform, Apress.

[58] Schoemaker, P.J., Heaton, S. and Teece, D., 2018. Innovation, dynamic capabilities, and leadership. California management review, 61(1), pp.15-42.

[59] Srinivasan, V., 2016. The intelligent enterprise in the era of big data. John Wiley & Sons.

[60] Teece, D., Peteraf, M. and Leih, S., 2016. Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California management review, 58(4), pp.13-35.

[61] Ulrich, T.A., Lew, R., Poresky, C.M., Rice, B.C., Thomas, K.D. and Boring, R.L., 2017. Operator-in-the-Loop Study for a Computerized Operator Support System (COSS)–Cross-System and System-Independent Evaluations (No. INL/EXT-17-43390-Rev000). Idaho National Lab.(INL), Idaho Falls, ID (United States).

[62] Vo, Q.D., Thomas, J., Cho, S., De, P., Choi, B.J. and Sael, L., 2017. Next generation business intelligence and analytics: a survey. arXiv preprint arXiv:1704.03402.

[63] Weir, P., Ellerweg, R., Payne, S., Reuter, D., Alhonnoro, T., Voglreiter, P., Mariappan, P., Pollari, M., Park, C.S., Voigt, P. and van Oostenbrugge, T., 2018. Go-smart: open-ended, web-based modelling of minimally invasive cancer treatments via a clinical domain approach. arXiv preprint arXiv:1803.09166.

[64] Werner, F. and Woitsch, R., 2018. Data Processing in Industrie 4.0: Data Analysis and Knowledge Management in Industrie 4.0. Datenbank-Spektrum, 18(1), pp.15-25.

[65] Ying, D., Patel, B. and Dhameliya, N., 2017. Managing Digital Transformation: The Role of Artificial Intelligence and Reciprocal Symmetry in Business. ABC Research Alert, 5(3), pp.67- 77.

[66] Yogeshwar, J. and Quartararo, R., 2018. How content intelligence and machine learning are transforming media workflows. Journal of Digital Media Management, 7(1), pp.24-32.

How to cite this paper

Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga "Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis" Iconic Research And Engineering Journals Volume 3 Issue 6 2019 Page 221-240
Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga "Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis" Iconic Research And Engineering Journals, vol. 3, no. 6, Dec. 2019
Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga (2019). Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis. Iconic Research And Engineering Journals, 3(6).
Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga "Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis" Iconic Research And Engineering Journals, vol. 3, no. 6, Dec. 2019.
@article{1709986,
      author = {Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga},
      title = {Intelligent Workflow Orchestration for Expense Attribution and Profitability Analysis},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {6},
      pages = {221-240},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709986.pdf},
      abstract = {Intelligent workflow orchestration represents a transformative approach to financial operations, enabling organizations to automate and optimize complex processes such as expense attribution and profitability analysis. Traditionally, these tasks relied on static, rule-based systems and manual data handling, often resulting in inefficiencies, inaccuracies, and delayed decision-making. By integrating advanced technologies?such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and business process management systems (BPMS)?intelligent orchestration provides dynamic, scalable, and data-driven solutions that enhance both the speed and precision of financial workflows. In the context of expense attribution, intelligent orchestration systems can automatically classify and route cost data across various business dimensions?such as departments, product lines, geographies, and customer segments?based on real-time business rules and predictive models. These systems leverage structured and unstructured data from diverse sources, including enterprise resource planning (ERP) systems, customer relationship management (CRM) tools, invoices, and external feeds. Machine learning models can identify patterns in expense behavior, detect anomalies, and continuously refine attribution logic, thereby reducing human intervention and enhancing auditability. On the profitability analysis front, intelligent orchestration facilitates the seamless integration of expense data with revenue streams, enabling granular insights into product, customer, and channel-level profitability. This level of analysis empowers decision-makers to optimize resource allocation, pricing strategies, and operational efficiency. Additionally, intuitive dashboards and real-time analytics support more proactive and informed financial planning. This explores the architecture, enabling technologies, implementation strategies, and real-world applications of intelligent workflow orchestration in finance. It also addresses critical challenges, including data governance, interoperability, and organizational change management. As businesses strive for greater agility and intelligence in financial operations, intelligent workflow orchestration emerges as a pivotal capability for driving sustainable profitability and strategic competitiveness in a data-centric economy.},
      keywords = {Intelligent workflow, Orchestration, Expense, Profitability analysis},
      month = {December},
  }