A Review of Data-Driven Risk Evaluation Models for Emerging Market Financial Institutions

References

[1] J. Gupta and A. Gregoriou, “Impact of market- based finance on SMEs failure,” Econ Model, vol. 69, pp. 13–25, Jan. 2018, 10.1016/J.ECONMOD.2017.09.004.

[2] W. J. Henisz, B. A. Zelner, and M. F. Guillén, “The worldwide diffusion of market-oriented infrastructure reform, 1977-1999,” Am Sociol Rev, vol. 70, no. 6, pp. 871–897, 2005, 10.1177/000312240507000601.

[3] C. Capps, D. Dranove, and M. Satterthwaite, “Competition and Market Power in Option Demand Markets,” Rand J Econ, vol. 34, no. 4, p. 737, Winter 2003,

[4] Y. Akbar, B. Balboni, G. Bortoluzzi, D. Dikova, and A. Tracogna, “Disentangling resource and mode escalation in the context of emerging markets. Evidence from a sample of manufacturing SMEs,” Journal of International Management, vol. 24, no. 3, pp. 257–270, Sep. 2018, 10.1016/J.INTMAN.2018.01.003.

[5] 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.

[6] P. H. Bloch, “Product design and marketing: Reflections after fifteen years,” Journal of Product Innovation Management, vol. 28, no. 3, pp. 378–380, May 2011, 10.1111/J.1540-5885.2011.00805.X.

[7] M. Chaffai and I. Medhioub, “Herding behavior in Islamic GCC stock market: a daily analysis,” International Journal of Islamic and Middle Eastern Finance and Management, vol. 11, no. 2, pp. 182–193, Jun. 2018, 10.1108/IMEFM-08-2017-0220.

[8] 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.

[9] 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.

[10] F. Caccioli, P. Barucca, and T. Kobayashi, “Network Models of Financial Systemic Risk: A Review,” SSRN Electronic Journal, Nov. 2017,

[11] C. Hoffman and M. Mora Rodríguez, “Digitizing Financial Reports – Issues and Insights: A Viewpoint,” The International Journal of Digital Accounting Research, vol. 13, 2013, V13_3.

[12] 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.

[13] 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.

[14] 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.

[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] 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,

[17] 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.

[18] 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.

[19] 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,

[20] 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.

[21] 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.

[22] 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.

[23] 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.

[24] 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.

[25] A. Castleberry and A. Nolen, “Thematic analysis of qualitative research data: Is it as easy as it sounds?,” Curr Pharm Teach Learn, vol. 10, no. 6, pp. 807–815, Jun. 2018, 10.1016/J.CPTL.2018.03.019.

[26] 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,

[27] 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,

[28] 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.

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

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

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

[32] 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.

[33] 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.

[34] 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.

[35] H. Li, L. Xiong, L. Zhang, and X. Jiang, “DPSynthesizer: Differentially private data synthesizer for privacy preserving data sharing,” Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1677–1680, 2014,

[36] 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.

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

[38] 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,

[39] 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.

[40] 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.

[41] N. Park, M. Mohammadi, K. Gorde, S. Jajodia, H. Park, and Y. Kim, “Data synthesis based on generative adversarial networks,” Proceedings of the VLDB Endowment, vol. 11, no. 10, pp. 1071–1083, 2018, 10.14778/3231751.3231757.

[42] E. T. Cheah, W. L. Chan, and C. L. L. Chieng, “The corporate social responsibility of pharmaceutical product recalls: An empirical examination of U.S. and U.K. markets,” Journal of Business Ethics, vol. 76, no. 4, pp. 427–449, Dec. 2007, 006-9292-1.

[43] C. I. Enyinda and D. Tolliver, “Taking counterfeits out of the pharmaceutical supply chain in Nigeria: Leveraging multilayer mitigation approach,” Journal of African Business, vol. 10, no. 2, pp. 218–234, 2009,

[44] S. A. Yawar and S. Seuring, “Management of Social Issues in Supply Chains: A Literature Review Exploring Social Issues, Actions and Performance Outcomes,” Journal of Business Ethics, vol. 141, no. 3, pp. 621–643, Mar. 2017,

[45] P. G. Pilar, A. P. Marta, and A. Antonio, “Profit efficiency and its determinants in small and medium-sized enterprises in Spain,” BRQ Business Research Quarterly, vol. 21, no. 4, pp. 238–250, Oct. 2018, 10.1016/J.BRQ.2018.08.003.

[46] J. Louis and P. S. Dunston, “Integrating IoT into operational workflows for real-time and automated decision-making in repetitive construction operations,” Autom Constr, vol. 94, pp. 317–327, Oct. 2018, 10.1016/J.AUTCON.2018.07.005.

[47] 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.

[48] 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.

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

[50] P. Voigt and A. von dem Bussche, “The EU General Data Protection Regulation (GDPR),” 2017,

[51] 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,

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

[53] 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.

[54] 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.

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

[56] L. Curren and J. Kaye, “Revoking consent: A ‘blind spot’ in data protection law?,” Computer Law and Security Review, vol. 26, no. 3, pp. 273–283, May 2010, 10.1016/J.CLSR.2010.03.001.

[57] 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.

[58] 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.

[59] 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,

[60] 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,

[61] 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.

[62] J. A. Potts et al., “Classification of dengue illness based on readily available laboratory data,” Am J Trop Med Hyg, vol. 83, no. 4, pp. 781–788, Oct. 2010, 10.4269/ajtmh.2010.10-0135.

[63] E. Naydenova, A. Tsanas, S. Howie, C. Casals- Pascual, and M. De Vos, “The power of data mining in diagnosis of childhood pneumonia,” J R Soc Interface, vol. 13, no. 120, Jul. 2016, 10.1098/RSIF.2016.0266;WGROUP:STRING :PUBLICATION.

[64] 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,

[65] 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.

[66] 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,

[67] 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.

[68] 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,

[69] 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,

[70] J. Kaye, “The tension between data sharing and the protection of privacy in genomics research,” Annu Rev Genomics Hum Genet, vol. 13, pp. 415–431, Sep. 2012, 10.1146/ANNUREV-GENOM-082410- 101454.

[71] 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.

[72] 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.

[73] 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,

[74] 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.

[75] 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.

[76] 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,

[77] S. Spiekermann and A. Novotny, “A vision for global privacy bridges: Technical and legal measures for international data markets,” Computer Law and Security Review, vol. 31, no. 2, pp. 181–200, Apr. 2015, 10.1016/J.CLSR.2015.01.009.

[78] 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.

[79] 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,

[80] 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.

[81] 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.

[82] N. G. Weiskopf and C. Weng, “Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research,” Journal of the American Medical Informatics Association, vol. 20, no. 1, pp. 144–151, 2013, 000681.

[83] 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,

[84] 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,

[85] 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,

[86] C. Bartolini and L. Siry, “The right to be forgotten in the light of the consent of the data subject,” Computer Law and Security Review, vol. 32, no. 2, pp. 218–237, Apr. 2016, 10.1016/J.CLSR.2016.01.005.

[87] 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.

[88] 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.

[89] 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.

[90] 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,

[91] 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.

[92] 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.

[93] 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.

[94] 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.

[95] 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.

[96] J. Nickerson, S. Chackungal, L. Knowlton, K. McQueen, and FM. Burkle, “Surgical care during humanitarian crises: a systematic review of published surgical caseload data from foreign medical teams.,” Prehosp Disaster Med, vol. 27, 2012.

[97] A. Mantelero, “The EU Proposal for a General Data Protection Regulation and the roots of the right to be forgotten,” Computer Law and Security Review, vol. 29, no. 3, pp. 229–235, Jun. 2013,

[98] 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.

[99] 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,

[100] T. Sustrova, “A Suitable Artificial Intelligence Model for Inventory Level Optimization,” Trends Economics and Management, vol. 10, no. 25, p. 48, May 2016, 10.13164/TRENDS.2016.25.48.

[101] G. Khoshsima, “A strategic model for measuring agility with fuzzy logic,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5370 LNCS, pp. 258– 268, 2008, 0_29.

[102] B. Alarie, A. Niblett, and A. H. Yoon, “How artificial intelligence will affect the practice of law,” Univ. Tor. Law J., vol. 68, no. supplement 1, pp. 106–124, Jan. 2018, 10.3138/utlj.2017-0052.

[103] L. Tesfatsion, “Agent-based computational economics: Modeling economies as complex adaptive systems,” Inf Sci (N Y), vol. 149, no. 4, pp. 262–268, 2003, 0255(02)00280-3.

[104] D. Strömberg, “Natural disasters, economic development, and humanitarian aid,” Journal of Economic Perspectives, vol. 21, pp. 199– 222, 2007.

[105] “Impact of SMEs in economic growth in Albania,” European Journal of Sustainable Development, vol. 5, no. 3, Oct. 2015, 10.14207/EJSD.2016.V5N3P151.

[106] D. Rodrik, A. Subramanian, and F. Trebbi, “Institutions rule: The primacy of institutions over geography and integration in economic development,” Journal of Economic Growth, vol. 9, no. 2, pp. 131–165, Jun. 2004, 10.1023/B:JOEG.0000031425.72248.85.

[107] O. A. IO Ogunrinola, “Health and economic implications of waste dumpsites in cities: The case of Lagos, Nigeria,” Int J Econ Finance, vol. 4, no. 4, p. 239, 2012.

[108] G. Benito, B. Grøgaard, and R. Narula, “Environmental influences on MNE subsidiary roles: Economic integration and the Nordic countries,” J Int Bus Stud, vol. 34, no. 5, pp. 443–456, 2003, 10.1057/PALGRAVE.JIBS.8400047.

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

[110] N. Wilkinson and P. Coetzee, “Internal audit assurance or consulting services rendered on governance: How does one decide?,” Journal of Governance and Regulation, vol. 4, no. 1, pp. 186 –200, 2015, 10.22495/JGR_V4_I1_C2_P3.

[111] M. Elhelaly, “Related party transactions, corporate governance and accounting quality in Greece,” Jul. 2014.

[112] E. Nicol, D. Bradshaw, J. Uwimana-Nicol, and L. Dudley, “Perceptions about data-informed decisions: An assessment of information-use in high HIV-prevalence settings in South Africa,” BMC Health Serv Res, vol. 17, Dec. 2017, 10.1186/S12913-017-2641-1.

[113] U. Flick, “ Charting the Routes,” The SAGE Handbook of Qualitative Data Collection, pp. 3–16, Jan. 2018,

[114] C. Pope, S. Ziebland, and N. Mays, “Qualitative research in health care: Analysing qualitative data,” BMJ, vol. 320, no. 7227, pp. 114–116, Jan. 2000, 10.1136/bmj.320.7227.114.

[115] T. B. Murdoch and A. S. Detsky, “The inevitable application of big data to health care,” JAMA, vol. 309, no. 13, pp. 1351–1352, Apr. 2013,

@article{1712258,
      author = {Olawole Akomolafe, Michael Uzoma Agu},
      title = {A Review of Data-Driven Risk Evaluation Models for Emerging Market Financial Institutions},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {6},
      pages = {433-448},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712258.pdf},
      abstract = {Emerging market financial institutions face heightened levels of uncertainty due to volatile macroeconomic conditions, weak regulatory environments, data scarcity, and structural market imperfections. Advances in data-driven modelling spanning statistical learning, machine learning, early warning systems, credit scoring, stress-testing frameworks, and macro-prudential forecasting offer significant potential to strengthen risk evaluation and supervisory oversight. This paper provides a comprehensive review of data-driven risk evaluation models applicable to banks, microfinance institutions, insurance firms, and capital-market intermediaries operating in emerging economies. Drawing solely on pre-2019 scholarship, the review evaluates the evolution, methodological foundations, strengths, and limitations of major quantitative approaches used in credit risk assessment, liquidity risk prediction, operational risk detection, and systemic vulnerability monitoring. The article identifies persistent challenges such as data quality issues, modelling instability, weak governance, and context adaptation gaps. A set of implications for institutional practice and future research is outlined.},
      keywords = {Data-Driven Modelling, Emerging Markets, Financial Institutions, Risk Evaluation, Machine Learning, Early Warning Systems.},
      month = {December},
  }