International Peer-Reviewed Journal•Open Access•ISSN 2456-8880
irejournals@gmail.com•+91-7433024337

Home / Current Issue / Paper 1712254

1712254 Vol 1 · Issue 12 Download Paper

A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency

Michael Uzoma Agu Olawole Akomolafe

Subject area: Science,Engineering and Technology  ·  Area of research: Financial Transparency

Abstract

Financial transparency has become a critical component of global financial governance, influencing regulatory compliance, corporate accountability, and the stability of financial markets. As financial transactions, reporting processes, and compliance requirements grow increasingly complex, institutions have sought to integrate data systems, compliance tools, and analytical technologies to improve oversight and transparency. Prior to 2018, significant progress was made in developing data integration architectures, automated compliance mechanisms, and risk-based monitoring tools, yet fragmentation, inconsistent implementation, and uneven regulatory harmonisation impeded full realisation of their benefits. This paper reviews scholarly and regulatory literature on integrated data systems, compliance frameworks, financial reporting technologies, and data-governance practices that enhance transparency. It examines the evolution of integrated financial information systems, regulatory technology (RegTech), automated reporting tools, data-quality mechanisms, and cross-border compliance infrastructures. The review synthesises key developments, identifies challenges, and outlines conceptual pathways for strengthening transparency through integrated data-compliance ecosystems.

Keywords

Financial Transparency; Integrated Data Systems; Compliance Management; Regtech; Data Governance; Financial Regulation

References

[1] W. K. T. Cho and B. J. Gaines, “Breaking the (Benford) law: Statistical fraud detection in campaign finance,” American Statistician, vol. 61, no. 3, pp. 218–223, Aug. 2007, 10.1198/000313007X223496.

[2] I. Hasan, K. Jackowicz, O. Kowalewski, and Ł. Kozłowski, “Do local banking market structures matter for SME financing and performance? New evidence from an emerging economy,” J Bank Financ, vol. 79, pp. 142– 158, Jun. 2017, 10.1016/J.JBANKFIN.2017.03.009.

[3] M. Sipa, I. Gorzeń-Mitka, and A. Skibiński, “Determinants of Competitiveness of Small Enterprises: Polish Perspective,” Procedia Economics and Finance, vol. 27, pp. 445–453, 2015,

[4] N. E. Popescu, “Entrepreneurship and SMEs Innovation in Romania,” Procedia Economics and Finance, vol. 16, pp. 512–520, 2014, 10.1016/S2212-5671(14)00832-6.

[5] M. D. Gould, M. A. Porter, S. Williams, M. McDonald, D. J. Fenn, and S. D. Howison, “Limit order books,” Quant Finance, vol. 13, no. 11, pp. 1709–1742, 2013, 10.1080/14697688.2013.803148.

[6] S. Mullainathan, J. Schwartzstein, and W. J. Congdon, “A reduced-form approach to behavioral public finance,” Annu Rev Econom, vol. 4, pp. 511–540, Jul. 2012, 10.1146/ANNUREV-ECONOMICS-111809- 125033.

[7] P. C. Tetlock, “Information transmission in finance,” Annual Review of Financial Economics, vol. 6, pp. 365–384, Dec. 2014, 110613-034449.

[8] D. Hirshleifer, “Behavioral Finance,” Annual Review of Financial Economics, vol. 7, no. Volume 7, 2015, pp. 133–159, Dec. 2015, 10.1146/ANNUREV-FINANCIAL-092214- 043752/CITE/REFWORKS.

[9] 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.

[10] 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,

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

[12] 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.

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

[14] 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,

[15] 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.

[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] 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,

[18] 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.

[19] 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.

[20] J. Sandefur and A. Glassman, “The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics,” Journal of Development Studies, vol. 51, no. 2, pp. 116– 132, Feb. 2015, 10.1080/00220388.2014.968138.

[21] K. Witkowski, “Internet of Things, Big Data, Industry 4.0 - Innovative Solutions in Logistics and Supply Chains Management,” Procedia Eng, vol. 182, pp. 763–769, 2017, 10.1016/j.proeng.2017.03.197.

[22] A. Kaushik and A. Raman, “The new data- driven enterprise architecture for e-healthcare: Lessons from the indian public sector,” Gov Inf Q, vol. 32, no. 1, pp. 63–74, 2015, 10.1016/J.GIQ.2014.11.002.

[23] D. Li, W. Daamen, and R. M. P. Goverde, “Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station,” J Adv Transp, vol. 50, no. 5, pp. 877–896, Aug. 2016, 10.1002/ATR.1380.

[24] S. Rosenbaum, “Data governance and stewardship: Designing data stewardship entities and advancing data access,” Health Serv Res, vol. 45, no. 5 PART 2, pp. 1442– 1455, Oct. 2010, 6773.2010.01140.X.

[25] V. Khatri and C. V. Brown, “Designing data governance,” Commun ACM, vol. 53, no. 1, pp. 148–152, Jan. 2010, 10.1145/1629175.1629210.

[26] S. O’Riain, E. Curry, and A. Harth, “XBRL and open data for global financial ecosystems: A linked data approach,” International Journal of Accounting Information Systems, vol. 13, no. 2, pp. 141–162, Jun. 2012, 10.1016/J.ACCINF.2012.02.002.

[27] A. Halevy, P. Norvig, and F. Pereira, “The unreasonable effectiveness of data,” IEEE Intell Syst, vol. 24, no. 2, pp. 8–12, 2009, 10.1109/MIS.2009.36.

[28] J. Fan, F. Han, and H. Liu, “Challenges of Big Data analysis,” Natl Sci Rev, vol. 1, no. 2, pp. 293–314, Jun. 2014, 10.1093/NSR/NWT032.

[29] L. Edwards, “Privacy, Security and Data Protection in Smart Cities:,” European Data Protection Law Review, vol. 2, no. 1, pp. 28– 58, Feb. 2017,

[30] C. Allen et al., “Data Governance and Data Sharing Agreements for Community-Wide Health Information Exchange: Lessons from the Beacon Communities,” EGEMS, vol. 2, no. 1, p. 1057, Apr. 2014, 9214.1057.

[31] L. Cheng, F. Liu, and D. D. Yao, “Enterprise data breach: causes, challenges, prevention, and future directions,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 7, no. 5, Sep. 2017,

[32] F. H. Cate and V. Mayer-Schönberger, “Tomorrow’s privacy: Notice and consent in a world of Big Data,” International Data Privacy Law, vol. 3, no. 2, pp. 67–73, May 2013, 10.1093/IDPL/IPT005.

[33] R. Schroeder, “Big Data and the brave new world of social media research,” Big Data Soc, vol. 1, no. 2, Jul. 2014, 10.1177/2053951714563194.

[34] A. O’Cathain, E. Murphy, and J. Nicholl, “Three techniques for integrating data in mixed methods studies,” BMJ, vol. 341, no. 7783, pp. 1147–1150, Nov. 2010, 10.1136/bmj.c4587.

[35] W. Wang, M. Winner, and C. R. Burgert- Brucker, “Limited service availability, readiness, and use of facility-based delivery care in Haiti: A study linking health facility data and population data,” Glob Health Sci Pract, vol. 5, no. 2, pp. 244–261, Jun. 2017,

[36] E. Baccarelli, N. Cordeschi, A. Mei, M. Panella, M. Shojafar, and J. Stefa, “Energy- efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: Review, challenges, and a case study,” IEEE Netw, 2016.

[37] N. Terry, “Existential challenges for healthcare data protection in the United States,” Ethics Med Public Health, vol. 3, no. 1, pp. 19–27, Jan. 2017,

[38] L. Tawalbeh, R. Mehmood, E. Benkhlifa, and H. Song, “Cloud Computing Model and Big Data Analysis for Healthcare Applications,” IEEE Access, vol. 4, 2016.

[39] 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.

[40] Y. Sun and S. Upadhyaya, “Secure and privacy preserving data processing support for active authentication,” Information Systems Frontiers, vol. 17, no. 5, pp. 1007–1015, Oct. 2015,

[41] 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,

[42] 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,

[43] 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,

[44] C. S. Kruse, R. Goswamy, Y. Raval, and S. Marawi, “Challenges and opportunities of big data in health care: A systematic review,” JMIR Med Inform, vol. 4, no. 4, Oct. 2016, 10.2196/MEDINFORM.5359.

[45] W. Wang and E. Krishnan, “Big data and clinicians: A review on the state of the science,” JMIR Med Inform, vol. 2, no. 1, Jan. 2014,

[46] J. Wang, Y. Zhou, Y. Wang, J. Zhang, C. L. P. Chen, and Z. Zheng, “Multiobjective Vehicle Routing Problems with Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms,” IEEE Trans Cybern, vol. 46, no. 3, pp. 582–594, Mar. 2016,

[47] L. Oneto et al., “Dynamic delay predictions for large-scale railway networks: Deep and shallow extreme learning machines tuned via thresholdout,” IEEE Trans Syst Man Cybern Syst, vol. 47, no. 10, pp. 2754–2767, Oct. 2017,

[48] A. V. Palagin, “Functionally oriented approach in research-related design,” Cybern. Syst. Analysis, vol. 53, no. 6, pp. 986–992, Nov. 2017,

[49] I. G. Kryvonos, I. V. Krak, O. V. Barmak, and A. I. Kulias, “Methods to Create Systems for the Analysis and Synthesis of Communicative Information,” Cybern Syst Anal, vol. 53, no. 6, pp. 847–856, Nov. 2017, 017-9986-7.

[50] A. Fronzetti Colladon and E. Remondi, “Using social network analysis to prevent money laundering,” Expert Syst Appl, vol. 67, pp. 49– 58, Jan. 2017, 10.1016/J.ESWA.2016.09.029.

[51] L. C. Dreyer, M. Z. Hauschild, and J. Schierbeck, “A framework for social life cycle impact assessment,” International Journal of Life Cycle Assessment, vol. 11, no. 2, pp. 88– 97, Mar. 2006,

[52] V. Mani, R. Agrawal, and V. Sharma, “Supplier selection using social sustainability: AHP based approach in India,” International Strategic Management Review, vol. 2, no. 2, pp. 98–112, Dec. 2014, 10.1016/j.ism.2014.10.003.

[53] N. Barberis and R. Thaler, “Chapter 18 A survey of behavioral finance,” Handbook of the Economics of Finance, vol. 1, no. SUPPL. PART B, pp. 1053 –1128, 2003, 10.1016/S1574-0102(03)01027-6.

[54] R. Kersten, J. Harms, K. Liket, and K. Maas, “Small Firms, large Impact? A systematic review of the SME Finance Literature,” World Dev, vol. 97, pp. 330–348, Sep. 2017, 10.1016/J.WORLDDEV.2017.04.012.

[55] D. E. O’Leary, “Configuring blockchain architectures for transaction information in blockchain consortiums: The case of accounting and supply chain systems,” Intelligent Systems in Accounting, Finance and Management, vol. 24, no. 4, pp. 138–147, Oct. 2017,

[56] W. R. Kerr and R. Nanda, “Financing Innovation,” Annual Review of Financial Economics, vol. 7, pp. 445–462, Dec. 2015, 111914-041825.

[57] I. H. Cheng and W. Xiong, “Financialization of commodity markets,” Annual Review of Financial Economics, vol. 6, pp. 419–941, Dec. 2014, FINANCIAL-110613-034432.

[58] P. Bond, A. Edmans, and I. Goldstein, “The real effects of financial markets,” Annual Review of Financial Economics, vol. 4, pp. 339–360, Oct. 2012, FINANCIAL-110311-101826.

[59] B. Trap et al., “First regulatory inspections measuring adherence to good pharmacy practices in the public sector in uganda: A cross-sectional comparison of performance between supervised and unsupervised facilities,” J Pharm Policy Pract, vol. 9, no. 1, pp. 1–10, 2016, 0068-4.

[60] L. Urquhart and T. Rodden, “New directions in information technology law: learning from human–computer interaction,” International Review of Law, Computers and Technology, vol. 31, no. 2, pp. 150–169, May 2017, 10.1080/13600869.2017.1298501.

[61] B. Boyce, “Emerging Technology and the Health Insurance Portability and Accountability Act,” J Acad Nutr Diet, vol. 117, no. 4, pp. 517–518, Apr. 2017, 10.1016/j.jand.2016.05.013.

[62] J. Abelson et al., “PUBLIC and PATIENT INVOLVEMENT in HEALTH TECHNOLOGY ASSESSMENT: A FRAMEWORK for ACTION,” Int J Technol Assess Health Care, vol. 32, no. 4, pp. 256– 264, 2016,

[63] S. Dünnebeil, A. Sunyaev, I. Blohm, J. M. Leimeister, and H. Krcmar, “Determinants of physicians’ technology acceptance for e-health in ambulatory care,” Int J Med Inform, vol. 81, no. 11, pp. 746–760, Nov. 2012, 10.1016/j.ijmedinf.2012.02.002.

[64] L. M. Mutuku, “The Effect of Technology on Supply Delivery of Online Stores in Kenya,” 2017, Accessed: Jul. 06, 2016. [Online]. Available: http://erepository.uonbi.ac.ke/handle/11295/10 2873

[65] I. Holeman, T. P. Cookson, and C. Pagliari, “Digital technology for health sector governance in low and middle income countries: A scoping review,” J Glob Health, vol. 6, no. 2, 2016, 10.7189/JOGH.06.020408.

[66] B. Chaudhry, J. Wang, S. Wu, M. Maglione, W. Mojica, and E. Roth, “Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care,” Ann Intern Med, vol. 144, no. 10, 2006.

[67] B. Oztaysi, S. Cevik Onar, C. Kahraman, and M. Yavuz, “Multi-criteria alternative-fuel technology selection using interval-valued intuitionistic fuzzy sets,” Transp Res D Transp Environ, vol. 53, pp. 128–148, Jun. 2017, 10.1016/j.trd.2017.04.003.

[68] F. Magrabi, S. T. Liaw, D. Arachi, W. Runciman, E. Coiera, and M. R. Kidd, “Identifying patient safety problems associated with information technology in general practice: An analysis of incident reports,” BMJ Qual Saf, vol. 25, no. 11, pp. 870–880, Nov. 2016,

[69] D. Donoho, “50 Years of Data Science,” Journal of Computational and Graphical Statistics, vol. 26, no. 4, pp. 745–766, Oct. 2017,

[70] A. Cerioli and D. Perrotta, “Robust clustering around regression lines with high density regions,” Adv Data Anal Classif, vol. 8, no. 1, pp. 5–26, 2014, 0151-5.

[71] R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos, and H. L. Shang, “Optimal combination forecasts for hierarchical time series,” Comput Stat Data Anal, vol. 55, no. 9, pp. 2579–2589, Sep. 2011, 10.1016/J.CSDA.2011.03.006.

[72] H. Demirkan and D. Delen, “Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud,” Decis Support Syst, vol. 55, no. 1, pp. 412–421, Apr. 2013, 10.1016/j.dss.2012.05.048.

[73] 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.

[74] J. Wang and H. Yue, “Food safety pre-warning system based on data mining for a sustainable food supply chain,” Food Control, vol. 73, pp. 223–229, Mar. 2017, 10.1016/J.FOODCONT.2016.09.048.

[75] 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.

[76] 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.

[77] 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.

[78] K. A. Endaya and M. M. Hanefah, “Internal auditor characteristics, internal audit effectiveness, and moderating effect of senior management,” Journal of Economic and Administrative Sciences, vol. 32, no. 2, pp. 160–176, 2016, 0023.

[79] G. Sarens and I. De Beelde, “Internal auditors’ perception about their role in risk management: A comparison between US and Belgian companies,” Managerial Auditing Journal, vol. 21, no. 1, pp. 63–80, 2006, 10.1108/02686900610634766.

[80] L. de Zwaan, J. Stewart, and N. Subramaniam, “Internal audit involvement in enterprise risk management,” Managerial Auditing Journal, vol. 26, no. 7, pp. 586–604, Jul. 2011, 10.1108/02686901111151323.

[81] R. Lenz, G. Sarens, and F. Hoos, “Internal Audit Effectiveness: Multiple Case Study Research Involving Chief Audit Executives and Senior Management,” EDPACS, vol. 55, no. 1, pp. 1–17, Jan. 2017, 10.1080/07366981.2017.1278980.

[82] M. Arena and G. Azzone, “Identifying Organizational Drivers of Internal Audit Effectiveness,” International Journal of Auditing, vol. 13, no. 1, pp. 43–60, Mar. 2009,

[83] J. Goodwin, “A comparison of internal audit in the private and public sectors,” Managerial Auditing Journal, vol. 19, no. 5, pp. 640–650, Jun. 2004,

[84] A. Fernández-Laviada, “Internal audit function role in operational risk management,” Journal of Financial Regulation and Compliance, vol. 15, no. 2, pp. 143–155, 2007, 10.1108/13581980710744039.

[85] P. Coetzee and D. Lubbe, “Improving the efficiency and effectiveness of risk-based internal audit engagements,” International Journal of Auditing, vol. 18, no. 2, pp. 115– 125, 2014,

[86] N. H. Z. Abidin, “Factors influencing the implementation of risk-based auditing,” Asian Review of Accounting, vol. 25, no. 3, pp. 361– 375, 2017,

[87] K. Govindan and H. Soleimani, “A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus,” J Clean Prod, vol. 142, pp. 371–384, Jan. 2017,

[88] G. Gereffi, J. Humphrey, and T. Sturgeon, “The governance of global value chains,” Rev Int Polit Econ, vol. 12, no. 1, pp. 78–104, Feb. 2005,

[89] F. Costantino, G. Di Gravio, A. Shaban, and M. Tronci, “Smoothing inventory decision rules in seasonal supply chains,” Expert Syst Appl, vol. 44, pp. 304–319, Feb. 2016, 10.1016/j.eswa.2015.08.052.

[90] P. Yadav, P. Lydon, J. Oswald, M. Dicko, and M. Zaffran, “Integration of vaccine supply chains with other health commodity supply chains: A framework for decision making,” Vaccine, vol. 32, no. 50, pp. 6725–6732, Nov. 2014,

[91] C. Jira and M. W. Toffel, “Engaging supply chains in climate change,” Manufacturing and Service Operations Management, vol. 15, no. 4, pp. 559–577, Sep. 2013, 10.1287/MSOM.1120.0420.

[92] B. K. Mishra, S. Raghunathan, and X. Yue, “Demand forecast sharing in supply chains,” Prod Oper Manag, vol. 18, no. 2, pp. 152–166, Mar. 2009, 5956.2009.01013.X.

[93] C. N. Verdouw, J. Wolfert, A. J. M. Beulens, and A. Rialland, “Virtualization of food supply chains with the internet of things,” J Food Eng, vol. 176, pp. 128–136, May 2016, 10.1016/J.JFOODENG.2015.11.009.

[94] L. B. Schwarz and H. Zhao, “The unexpected impact of information sharing on US pharmaceutical supply chains,” Interfaces (Providence), vol. 41, no. 4, pp. 354–364, Jul. 2011,

[95] K. Rennings and C. Rammer, “The impact of regulation-driven environmental innovation on innovation success and firm performance,” Ind Innov, vol. 18, no. 03, pp. 255–283, Apr. 2011,

[96] J. Campbell, A. Goldfarb, and C. Tucker, “Privacy regulation and market structure,” J Econ Manag Strategy, vol. 24, no. 1, pp. 47– 73, Mar. 2015,

[97] A. Goldfarb and C. E. Tucker, “Privacy regulation and online advertising,” Manage Sci, vol. 57, no. 1, pp. 57–71, Jan. 2011, 10.1287/mnsc.1100.1246.

[98] P. De Hert and V. Papakonstantinou, “The new General Data Protection Regulation: Still a sound system for the protection of individuals?,” Computer Law and Security Review, vol. 32, no. 2, pp. 179–194, Apr. 2016,

[99] Y. Song, R. Routray, R. Jain, and C. H. Tan, “A data-driven storage recommendation service for multitenant storage management environments,” Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, pp. 1026–1040, Jun. 2015, 10.1109/INM.2015.7140429.

[100] Y. Gong, “Data consistency in a voluntary medical incident reporting system.,” J Med Syst, vol. 35, no. 4, pp. 609–615, Aug. 2011,

[101] 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,

[102] G. Rosano, F. Pelliccia, C. Gaudio, and A. J. Coats, “The challenge of performing effective medical research in the era of healthcare data protection,” Int J Cardiol, vol. 177, no. 2, pp. 510–511, Dec. 2014, 10.1016/J.IJCARD.2014.08.077.

[103] S. Son, S. Na, and K. Kim, “Product data quality validation system for product development processes in high-tech industry,” Int J Prod Res, vol. 49, no. 12, pp. 3751–3766, Jun. 2011, 10.1080/00207543.2010.486906.

[104] 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.

[105] 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.

[106] L. Barabesi, A. Cerasa, D. Perrotta, and A. Cerioli, “Modeling international trade data with the Tweedie distribution for anti-fraud and policy support,” Eur J Oper Res, vol. 248, no. 3, pp. 1031–1043, Feb. 2016, 10.1016/J.EJOR.2015.08.042.

How to cite this paper

Michael Uzoma Agu, Olawole Akomolafe "A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency" Iconic Research And Engineering Journals Volume 1 Issue 12 2018 Page 102-119
Michael Uzoma Agu, Olawole Akomolafe "A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency" Iconic Research And Engineering Journals, vol. 1, no. 12, Jun. 2018
Michael Uzoma Agu, Olawole Akomolafe (2018). A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency. Iconic Research And Engineering Journals, 1(12).
Michael Uzoma Agu, Olawole Akomolafe "A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency" Iconic Research And Engineering Journals, vol. 1, no. 12, Jun. 2018.
@article{1712254,
      author = {Michael Uzoma Agu, Olawole Akomolafe},
      title = {A Review of Integrated Data and Compliance Systems for Strengthening Financial Transparency},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {12},
      pages = {102-119},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712254.pdf},
      abstract = {Financial transparency has become a critical component of global financial governance, influencing regulatory compliance, corporate accountability, and the stability of financial markets. As financial transactions, reporting processes, and compliance requirements grow increasingly complex, institutions have sought to integrate data systems, compliance tools, and analytical technologies to improve oversight and transparency. Prior to 2018, significant progress was made in developing data integration architectures, automated compliance mechanisms, and risk-based monitoring tools, yet fragmentation, inconsistent implementation, and uneven regulatory harmonisation impeded full realisation of their benefits. This paper reviews scholarly and regulatory literature on integrated data systems, compliance frameworks, financial reporting technologies, and data-governance practices that enhance transparency. It examines the evolution of integrated financial information systems, regulatory technology (RegTech), automated reporting tools, data-quality mechanisms, and cross-border compliance infrastructures. The review synthesises key developments, identifies challenges, and outlines conceptual pathways for strengthening transparency through integrated data-compliance ecosystems.},
      keywords = {Financial Transparency; Integrated Data Systems; Compliance Management; Regtech; Data Governance; Financial Regulation},
      month = {June},
  }