A Conceptual Model for Leveraging Business Intelligence Tools to Improve Financial Forecasting Accuracy

References

[1] R. Mumtaz and I. A. Jadoon, “Effect of explicit deposit insurance premium on the moral hazard of banks’ risk-taking: Around the globe,” International Journal of Financial Engineering, vol. 05, no. 02, p. 1850012, Jun. 2018,

[2] C.-H. Hui, C.-F. Lo, X.-F. Zheng, and T. Fong, “Probabilistic approach to measuring early- warning signals of systemic contagion risk,” International Journal of Financial Engineering, vol. 05, no. 02, p. 1850010, Jun. 2018,

[3] R. Sabherwal and I. Becerra-Fernandez, “Business intelligence: practices, technologies, and management,” Publisher: Wiley, Hoboken, NY, USA. ISBN-13, p. 304, 2013.

[4] Ana. Azevedo and M. Filipe. Santos, “Integration of data mining in business intelligence systems,” p. 314, 2015.

[5] S. Mishra, “Financial management and forecasting using business intelligence and big data analytic tools,” https:// vol. 05, no. 02, p. 1850011, Jul. 2018, 10.1142/S2424786318500111.

[6] G. Sarens, I. De Beelde, and P. Everaert, “Internal audit: A comfort provider to the audit committee,” British Accounting Review, vol. 41, no. 2, pp. 90–106, Jun. 2009, 10.1016/J.BAR.2009.02.002.

[7] E. Burrell Nickell and R. W. Roberts, “Organizational legitimacy, conflict, and hypocrisy: An alternative view of the role of internal auditing,” Critical Perspectives on Accounting, vol. 25, no. 3, pp. 217–221, 2014,

[8] S. Chaudhuri, U. Dayal, and V. Narasayya, “An overview of business intelligence technology,” Commun ACM, vol. 54, no. 8, pp. 88–98, Aug. 2011,

[9] H. J. Watson and B. H. Wixom, “The current state of business intelligence,” Computer (Long Beach Calif), vol. 40, no. 9, pp. 96–99, Sep. 2007,

[10] “THE CURRENT STATE OF BUSINESS INTELLIGENCE AND ANALYTICS IN UTAH,” Issues In Information Systems, 2014,

[11] C. M. Olszak, “Toward Better Understanding and Use of Business Intelligence in Organizations,” Information Systems Management, vol. 33, no. 2, pp. 105–123, Apr. 2016,

[12] H. Chen, R. H. L. Chiang, and V. C. Storey, “Business intelligence and analytics: From big data to big impact,” MIS Q, vol. 36, no. 4, pp. 1165–1188, 2012,

[13] V. H. Trieu, “Getting value from Business Intelligence systems: A review and research agenda,” Decis Support Syst, vol. 93, pp. 111– 124, Jan. 2017, 10.1016/J.DSS.2016.09.019.

[14] A. Brandão et al., “A benchmarking analysis of open-source business intelligence tools in healthcare environments,” Information (Switzerland), vol. 7, no. 4, 2016, 10.3390/INFO7040057.

[15] H. Baars and H. G. Kemper, “Management support with structured and unstructured data - An integrated business intelligence framework,” Information Systems Management, vol. 25, no. 2, pp. 132–148, Mar. 2008,

[16] M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77–84, 2013, 10.1111/JBL.12010.

[17] P. M. Hartmann, M. Zaki, N. Feldmann, and A. Neely, “Capturing value from big data–a taxonomy of data-driven business models used by start-up firms,” International Journal of Operations & Production Management, vol. 36, no. 10, pp. 1382–1406, 2016, 10.1108/ijopm-02-2014-0098.

[18] B. Baesens, R. Bapna, J. R. Marsden, J. Vanthienen, and J. L. Zhao, “Transformational Issues of Big Data and Analytics in Networked Business,” MIS Quarterly, vol. 40, no. 4, pp. 807–818, Apr. 2016, 10.25300/MISQ/2016/40:4.03;

[19] A. Gunasekaran et al., “Big data and predictive analytics for supply chain and organizational performance,” J Bus Res, vol. 70, pp. 308–317, Jan. 2017,

[20] M. Bar-Sinai, L. Sweeney, and M. Crosas, “DataTags, Data Handling Policy Spaces and the Tags Language,” Proceedings - 2016 IEEE Symposium on Security and Privacy Workshops, SPW 2016, pp. 1–8, Aug. 2016,

[21] S. Barocas and H. Nissenbaum, “Big data’s end run around procedural privacy protections,” Commun ACM, vol. 57, no. 11, pp. 31–33, Nov. 2014,

[22] C. Alvez, E. Miranda, G. Etchart, and S. Ruiz, “Efficient Iris Recognition Management in Object-Related Databases,” J Comput Sci Technol, vol. 18, no. 02, p. e12, Oct. 2018, 10.24215/16666038.18.E12.

[23] J. W. Y Zhang, “Data-driven modeling and scientific computing,” Appl Mech Rev, vol. 68, no. 5, pp. 050801–051013, 2016.

[24] F. Collins et al., “A database on global health research in Africa,” Lancet Glob Health, vol. 1, no. 2, pp. e64-5, 2013, 109x(13)70012-3.

[25] J. Hemerly, “Public policy considerations for data-driven innovation,” Computer (Long Beach Calif), vol. 46, no. 6, pp. 25–31, 2013,

[26] H. Cheng, Y. C. Lu, and C. Sheu, “An ontology-based business intelligence application in a financial knowledge management system,” Expert Syst Appl, vol. 36, no. 2 PART 2, pp. 3614–3622, 2009, 10.1016/J.ESWA.2008.02.047.

[27] M. K. Allio, “Strategic dashboards: designing and deploying them to improve implementation,” Strategy & Leadership, vol. 40, no. 5, pp. 24–31, Aug. 2012, 10.1108/10878571211257159.

[28] B. A. Wilbanks and P. A. Langford, “A review of dashboards for data analytics in nursing,” CIN - Computers Informatics Nursing, vol. 32, no. 11, pp. 545 –549, 2014, 10.1097/CIN.0000000000000106.

[29] K. Pauwels et al., “Dashboards as a service: Why, what, how, and what research is needed?,” J Serv Res, vol. 12, no. 2, pp. 175– 189, Nov. 2009, 10.1177/1094670509344213.

[30] O. M. Yigitbasioglu and O. Velcu, “A review of dashboards in performance management: Implications for design and research,” International Journal of Accounting Information Systems, vol. 13, no. 1, pp. 41–59, Mar. 2012, 10.1016/J.ACCINF.2011.08.002.

[31] M. Dyczkowski, J. Korczak, and H. Dudycz, “Multi-criteria evaluation of the intelligent dashboard for SME managers based on scorecard framework,” 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, vol. 2014-January, pp. 1147–1155, Oct. 2014, 10.15439/2014F388.

[32] W. Eckerson, “Characteristics Of Effective Performance Metrics,” Performance Dashboards: Measuring, Monitoring And Managing Your Business, p. 209 pp., 2011, Accessed: Sep. 15, 2017. [Online]. Available: https://books.google.com/books/about/Perfor mance_Dashboards.html?id=5nuYDwAAQB AJ

[33] K. J. Ferreira, B. H. A. Lee, and D. Simchi- Levi, “Analytics for an online retailer: Demand forecasting and price optimization,” Manufacturing and Service Operations Management, vol. 18, no. 1, pp. 69–88, Dec. 2016,

[34] G. J. Hahn and J. Packowski, “A perspective on applications of in-memory analytics in supply chain management,” Decis Support Syst, vol. 76, pp. 45–52, Jul. 2015, 10.1016/J.DSS.2015.01.003.

[35] S. Tiwari, H. M. Wee, and Y. Daryanto, “Big data analytics in supply chain management between 2010 and 2016: Insights to industries,” Comput Ind Eng, vol. 115, pp. 319–330, Jan. 2018,

[36] Y. H. G. X. H Zhang, “Big data analytics for fast-moving consumer goods supply chain management: a review,” J Ind Inf Integr, vol. 9, pp. 52–60, 2018.

[37] S. Purkayastha and J. Braa, “Big data analytics for developing countries-using the cloud for operational bi in health,” Electronic Journal of Information Systems in Developing Countries, vol. 59, no. 1, pp. 1–17, Oct. 2013, 10.1002/J.1681-4835.2013.TB00420.X.

[38] G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: Certain investigations for research and applications,” Int J Prod Econ, vol. 176, pp. 98–110, Jun. 2016, 10.1016/j.ijpe.2016.03.014.

[39] E. Raguseo and C. Vitari, “Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects,” Int J Prod Res, vol. 56, no. 15, pp. 5206–5221, Aug. 2018, 10.1080/00207543.2018.1427900.

[40] K. Nakatani and T. T. Chuang, “A web analytics tool selection method: An analytical hierarchy process approach,” Internet Research, vol. 21, no. 2, pp. 171–186, Jan. 2011,

[41] A. B. Borade, G. Kannan, and S. V. Bansod, “Analytical hierarchy process-based framework for VMI adoption,” Int J Prod Res, vol. 51, no. 4, pp. 963–978, 2013, 10.1080/00207543.2011.650795.

[42] R. Zhao, Y. Liu, N. Zhang, and T. Huang, “An optimization model for green supply chain management by using a big data analytic approach,” J Clean Prod, vol. 142, pp. 1085– 1097, Jan. 2017, 10.1016/j.jclepro.2016.03.006.

[43] L. Li, F. Liu, and C. Li, “Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process,” Comput Ind Eng, vol. 77, pp. 80 –87, 2014, 10.1016/j.cie.2014.09.009.

[44] S. H. Hashemi, A. Karimi, and M. Tavana, “An integrated green supplier selection approach with analytic network process and improved Grey relational analysis,” Int J Prod Econ, vol. 159, pp. 178–191, Jan. 2015, 10.1016/j.ijpe.2014.09.027.

[45] B. Baesens, R. Bapna, J. R. Marsden, J. Vanthienen, and J. L. Zhao, “Transformational issues of big data and analytics in networked business,” MIS Quarterly, vol. 40, no. 4, pp. 807–818, Dec. 2016, 10.25300/misq/2016/40:4.03.

[46] J. N. K. SL Brunton, “Data-driven versus physics-based modeling,” Annu Rev Fluid Mech, vol. 50, pp. 645–668, 2018.

[47] S. Fosso Wamba, S. Akter, A. Edwards, G. Chopin, and D. Gnanzou, “How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study,” Int J Prod Econ, vol. 165, pp. 234–246, Jul. 2015,

[48] P. Kadlec, B. Gabrys, and S. Strandt, “Data- driven Soft Sensors in the process industry,” Comput Chem Eng, vol. 33, no. 4, pp. 795–814, Apr. 2009, 10.1016/j.compchemeng.2008.12.012.

[49] P. Kumar Jain, K. Mandli, I. Hoteit, O. Knio, and C. Dawson, “Dynamically adaptive data- driven simulation of extreme hydrological flows,” Ocean Model (Oxf), vol. 122, pp. 85– 103, Feb. 2018, 10.1016/J.OCEMOD.2017.12.004.

[50] J. E. Austin, “Strategic collaboration between nonprofits and businesses,” Nonprofit Volunt Sect Q, vol. 29, no. SUPPL., pp. 69–97, 2000,

[51] E. K. Clemons and N. Madhani, “Regulation of digital businesses with natural monopolies or third-party payment business models: Antitrust lessons from the analysis of google,” Journal of Management Information Systems, vol. 27, no. 3, pp. 43–80, Jan. 2010, 10.2753/mis0742-1222270303.

[52] A. Z. Acar and C. Zehir, “The harmonized effects of generic strategies and business capabilities on business performance,” Journal of Business Economics and Management, vol. 11, no. 4, pp. 689–711, 2010, 10.3846/JBEM.2010.34.

[53] J. O. Strandhagen, L. R. Vallandingham, G. Fragapane, J. W. Strandhagen, A. B. H. Stangeland, and N. Sharma, “Logistics 4.0 and emerging sustainable business models,” Adv Manuf, vol. 5, no. 4, pp. 359–369, Dec. 2017,

[54] A. Tiwari, C. J. Turner, and B. Majeed, “A review of business process mining: state‐of‐ the‐art and future trends,” Business Process Management Journal, vol. 14, no. 1, pp. 5–22, Feb. 2008,

[55] G. Prause, “Sustainable business models and structures for industry 4.0,” Journal of Security and Sustainability Issues, vol. 5, no. 2, pp. 159– 169, 2015,

[56] K. M. Zuckweiler, K. M. Rosacker, and S. K. Hayes, “Business students’ perceptions of corporate governance best practices,” Corporate Governance (Bingley), vol. 16, no. 2, pp. 361–376, Apr. 2016, 08-2015-0117.

[57] C. N. Pitelis, “Edith Penrose and the resource- based view of (international) business strategy,” International Business Review, vol. 13, no. 4, pp. 523–532, 2004, 10.1016/J.IBUSREV.2004.04.002.

[58] T. McNamee, M. Pearson, and W. Boer, “Africans Investing in Africa: Understanding Business and Trade, Sector by Sector,” Africans Investing in Africa: Understanding Business and Trade, Sector by Sector, pp. 1– 338, May 2015,

[59] C. Legner et al., “Digitalization: Opportunity and challenge for the business and information systems engineering community,” Business & Information Systems Engineering, vol. 59, no. 4, pp. 301–308, Aug. 2017, 10.1007/s12599-017-0484-2.

[60] G. J. Veal and S. Mouzas, “Changing the rules of the game: Business responses to new regulation,” Industrial Marketing Management, vol. 40, no. 2, pp. 290–300, Feb. 2011,

[61] N. Melville, K. Kraemer, and V. Gurbaxani, “Information technology and organizational performance: An integrative model of IT business value,” MIS Q., vol. 28, no. 2, pp. 283–321, 2004,

[62] M. Augier and D. J. Teece, “Dynamic capabilities and the role of managers in business strategy and economic performance,” Organization Science, vol. 20, no. 2, pp. 410– 421, Mar. 2009, 10.1287/ORSC.1090.0424.

[63] P. Kecman and R. M. P. Goverde, “Predictive modelling of running and dwell times in railway traffic,” Public Transport, vol. 7, no. 3, pp. 295–319, Dec. 2015, 015-0106-7.

[64] D. Classen, M. Li, S. Miller, and D. Ladner, “An electronic health record–based real-time analytics program for patient safety surveillance and improvement,” Health Aff, vol. 37, no. 11, pp. 1805–1812, Nov. 2018, 10.1377/HLTHAFF.2018.0728;CTYPE:STRI NG:JOURNAL.

[65] E. Heo, J. Kim, and S. Cho, “Selecting hydrogen production methods using fuzzy analytic hierarchy process with opportunities, costs, and risks,” Int J Hydrogen Energy, vol. 37, no. 23, pp. 17655–17662, Dec. 2012, 10.1016/j.ijhydene.2012.09.055.

[66] D. Bisias, M. Flood, A. W. Lo, and S. Valavanis, “A survey of systemic risk analytics,” Annual Review of Financial Economics, vol. 4, pp. 255–296, Oct. 2012, 10.1146/ANNUREV-FINANCIAL-110311- 101754.

[67] O. Al-Hujran, E. M. Al-Lozi, M. M. Al-Debei, and M. Maqableh, “Challenges of cloud computing adoption from the TOE framework perspective,” International Journal of e- Business Research, vol. 14, no. 3, pp. 77–94, Jul. 2018,

[68] A. P. Monteiro, A. M. Soares, and O. L. Rua, “Entrepreneurial orientation and export performance: the mediating effect of organisational resources and dynamic capabilities,” J. for International Business and Entrepreneurship Development, vol. 10, no. 1, p. 3, 2017,

[69] F. K. Kirogo, “Effect of Risk- Based Audit on Financial Perfomance: A Survey of Insurance Companies in Nakuru Town, Kenya,” IOSR Journal of Business and Management, vol. 16, no. 10, pp. 84–91, 2014, 161038491.

[70] L. Barabesi, A. Cerasa, A. Cerioli, and D. Perrotta, “Goodness-of-Fit Testing for the Newcomb-Benford Law With Application to the Detection of Customs Fraud,” Journal of Business and Economic Statistics, vol. 36, no. 2, pp. 346–358, Apr. 2018, 10.1080/07350015.2016.1172014.

[71] D. J. Teece, “A dynamic capabilities-based entrepreneurial theory of the multinational enterprise,” J Int Bus Stud, vol. 45, no. 1, pp. 8–37, 2014,

[72] K. D. Brouthers, “A retrospective on: Institutional, cultural and transaction cost influences on entry mode choice and performance,” J Int Bus Stud, vol. 44, no. 1, pp. 14–22, 2013,

[73] P. Li et al., “Promoting secondary analysis of electronic medical records in china: Summary of the plagh-mit critical data conference and health datathon,” JMIR Med Inform, vol. 5, no. 4, Oct. 2017, 10.2196/MEDINFORM.7380.

[74] Y. R. Park et al., “Utilization of a clinical trial management system for the whole clinical trial process as an integrated database: System development,” J Med Internet Res, vol. 20, no. 4, Apr. 2018,

[75] A. Geissbuhler et al., “Trustworthy reuse of health data: A transnational perspective,” Int J Med Inform, vol. 82, no. 1, pp. 1–9, Jan. 2013,

[76] J. L. L. T. T Wang, “Data-driven fast moving consumer goods supply chain model and application,” Int J Control Autom, vol. 11, no. 5, pp. 125–138, 2018.

[77] J. A. Burkell, “Remembering me: big data, individual identity, and the psychological necessity of forgetting,” Ethics Inf Technol, vol. 18, no. 1, pp. 17–23, Mar. 2016, 10.1007/S10676-016-9393-1.

[78] J. P. A. Ioannidis, “Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research,” American Journal of Bioethics, vol. 13, no. 4, pp. 40–42, Apr. 2013, 10.1080/15265161.2013.768864.

[79] C. Meng, S. S. Nageshwaraniyer, A. Maghsoudi, Y. J. Son, and S. Dessureault, “Data-driven modeling and simulation framework for material handling systems in coal mines,” Comput Ind Eng, vol. 64, no. 3, pp. 766 –779, 2013, 10.1016/J.CIE.2012.12.017.

[80] B. D. Mittelstadt and L. Floridi, “The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts,” Sci Eng Ethics, vol. 22, no. 2, pp. 303–341, Apr. 2016, 10.1007/S11948-015-9652-2.

[81] A. Aswani, Z. J. M. Shen, and A. Siddiq, “Inverse optimization with noisy data,” Oper Res, vol. 66, no. 3, pp. 870–892, May 2018,

[82] M. A. Gianfrancesco, S. Tamang, J. Yazdany, and G. Schmajuk, “Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data,” JAMA Intern Med, vol. 178, no. 11, pp. 1544–1547, Nov. 2018, 10.1001/JAMAINTERNMED.2018.3763.

[83] X. Liu, P. V. Singh, and K. Srinivasan, “A structured analysis of unstructured big data by leveraging cloud computing,” Marketing Science, vol. 35, no. 3, pp. 363–388, May 2016,

[84] E. W. K. See-To and E. W. T. Ngai, “Customer reviews for demand distribution and sales nowcasting: a big data approach,” Ann Oper Res, vol. 270, no. 1–2, pp. 415–431, Nov. 2018,

[85] N. Khalil Zadeh, M. M. Sepehri, and H. Farvaresh, “Intelligent sales prediction for pharmaceutical distribution companies: A data mining based approach,” Math Probl Eng, vol. 2014, 2014,

[86] J. C. LB Moses, “Using big data for legal and law enforcement decisions: testing the new tools,” Univ. New South Wales Law J., vol. 37, no. 2, pp. 643–678, 2014.

[87] M. Song, Q. Du, and Q. Zhu, “A theoretical method of environmental performance evaluation in the context of big data,” Production Planning and Control, vol. 28, no. 11–12, pp. 976 –984, 2017, 10.1080/09537287.2017.1336801.

[88] A. Moser and I. Korstjens, “Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis,” European Journal of General Practice, vol. 24, no. 1, pp. 9–18, Jan. 2018, 10.1080/13814788.2017.1375091.

[89] G. Y. Song, Y. Cheon, K. Lee, H. Lim, K. Y. Chung, and H. C. Rim, “Multiple categorizations of products: Cognitive modeling of customers through social media data mining,” Pers Ubiquitous Comput, vol. 18, no. 6, pp. 1387–1403, 2014, 10.1007/S00779-013-0740-5.

[90] K. W. Pang and H. L. Chan, “Data mining- based algorithm for storage location assignment in a randomised warehouse,” Int J Prod Res, vol. 55, no. 14, pp. 4035–4052, Jul. 2017,

[91] I. Abubakar, “Exploring the determinants of open defecation in Nigeria using demographic and health survey data,” Science of The Total Environment, vol. 637–638, pp. 1455–1465, 2018.

[92] P. E. Mbondji, D. Kebede, E. W. Soumbey- Alley, C. Zielinski, W. Kouvividila, and P. S. Lusamba-Dikassa, “Health information systems in Africa: Descriptive analysis of data sources, information products and health statistics,” J R Soc Med, vol. 107, pp. 34–45, 2014,

[93] A. Sebaa, F. Chikh, A. Nouicer, and A. K. Tari, “Medical big data warehouse: Architecture and system design, a case study: Improving healthcare resources distribution,” J. of Medical Systems, vol. 42, no. 4, p. 59, Apr. 2018,

[94] P. Quinn, A. K. Habbig, E. Mantovani, and P. De Hert, “The Data protection and Medical Device Frameworks - Obstacles to the deployment of mHealth across Europe?,” Eur J Health Law, vol. 20, no. 2, pp. 185–204, Apr. 2013,

[95] R. Ramanathan, U. Ramanathan, and Y. Zhang, “Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach,” Int J Prod Econ, vol. 176, pp. 111–122, Jun. 2016, 10.1016/j.ijpe.2016.03.010.

[96] F. M. Behlen, R. E. Sayre, J. B. Weldy, and J. S. Michael, “`Permanent’ records: Experience with data migration in radiology information system and picture archiving and communication system replacement,” J Digit Imaging, vol. 13, no. 2 SUPPL. 1, pp. 171–174, 2000,

[97] J. H. Hibbard and E. Peters, “Supporting informed consumer health care decisions: data presentation approaches that facilitate the use of information in choice,” Annu Rev Public Health, vol. 24, pp. 413–33, 2003, 10.1146/annurev.publhealth.24.100901.14100 5.

[98] R. Y. Zhong, S. T. Newman, G. Q. Huang, and S. Lan, “Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives,” Comput Ind Eng, vol. 101, pp. 572–591, Nov. 2016, 10.1016/j.cie.2016.07.013.

[99] J. P. Belaud, S. Negny, F. Dupros, D. Michéa, and B. Vautrin, “Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering,” Comput Ind, vol. 65, no. 3, pp. 521–535, 2014, 10.1016/j.compind.2014.01.009.

[100] L. Sudhof et al., “Local use of geographic information systems to improve data utilisation and health services: mapping caesarean section coverage in rural Rwanda,” Trop Med Int Health, vol. 18, no. 1, pp. 18–26, Jan. 2013,

@article{1712257,
      author = {Michael Uzoma Agu, Olawole Akomolafe},
      title = {A Conceptual Model for Leveraging Business Intelligence Tools to Improve Financial Forecasting Accuracy},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {1},
      pages = {596-613},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712257.pdf},
      abstract = {Financial forecasting accuracy is fundamental to organisational sustainability, competitive strategy, and effective resource allocation. Despite advancements in analytics, many organisations continue to face forecasting challenges driven by volatile markets, fragmented data environments, and lack of integrated decision-support systems. Business Intelligence (BI) tools such as dashboards, data warehousing, OLAP systems, and predictive analytics offer opportunities to transform data into actionable insights, yet the link between BI capability and improved forecasting accuracy remains conceptually underdeveloped. This paper develops a pre-2019 conceptual model that explains how BI tools enhance financial forecasting accuracy by integrating data quality, analytical capability, organisational decision processes, and technological infrastructure. Drawing from literature published up to 2018, the model positions BI-enabled forecasting as an iterative, data-intensive process supported by information integration, analytical sophistication, and organisational learning. The paper provides theoretical grounding for future empirical validation and insights for practitioners seeking to leverage BI tools for more reliable financial forecasting.},
      keywords = {Business Intelligence; Financial Forecasting; Predictive Analytics; Data Warehousing; Organizational Decision Support; Forecast Accuracy},
      month = {July},
  }