Predictive Analytics Frameworks Strengthening Credit Risk Assessments and Lending Decisions in Financial Technology Ecosystems

References

[1] Abayomi, A. A., Odofin, O. T., Ogbuefi, E., Adekunle, B. I., Agboola, O. A., & Owoade, S. (2020). Evaluating Legacy System Refactoring for Cloud-Native Infrastructure Transformation in African Markets.

[2] Achar, S. (2018). Data Privacy-Preservation: A Method of Machine Learning. ABC Journal of Advanced Research, 7(2), 123-129.

[3] Adenuga, T., Ayobami, A.T. & Okolo, F.C., 2019. Laying the Groundwork for Predictive Workforce Planning Through Strategic Data Analytics and Talent Modeling. IRE Journals, 3(3), pp.159–161. ISSN: 2456-8880.

[4] Adenuga, T., Ayobami, A.T. & Okolo, F.C., 2020. AI-Driven Workforce Forecasting for Peak Planning and Disruption Resilience in Global Logistics and Supply Networks. International Journal of Multidisciplinary Research and Growth Evaluation, 2(2), pp.71–87. Available at: https://doi.org/10.54660/.IJMRGE.2020.1.2.71-87.

[5] Afolabi, T. S., Obamuyi, T. M., & Egbetunde, T. (2020). Credit risk and financial performance: Evidence from microfinance banks in Nigeria. IOSR Journal of Economics and Finance, 11(1), 8-15.

[6] Aisyah, N., Hidayat, R., Zulaikha, S., Rizki, A., Yusof, Z. B., Pertiwi, D., & Ismail, F. (2019). Artificial intelligence in cryptographic protocols: Securing e-commerce transactions and ensuring data integrity.

[7] Akinrinoye, O. V., Kufile, O. T., Otokiti, B. O., Ejike, O. G., Umezurike, S. A., & Onifade, A. Y. (2020). Customer segmentation strategies in emerging markets: a review of tools, models, and applications. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 6(1), 194-217.

[8] Akpe Ejielo, O. E., Ogbuefi, S., Ubamadu, B. C., & Daraojimba, A. I. (2020). Advances in role based access control for cloud enabled operational platforms. IRE Journals (Iconic Research and Engineering Journals), 4(2), 159-174.

[9] Akpe, O. E. E., Mgbame, A. C., Ogbuefi, E., Abayomi, A. A., & Adeyelu, O. O. (2020). Bridging the business intelligence gap in small enterprises: A conceptual framework for scalable adoption. IRE Journals, 4 (2), 159–161.

[10] Appelt, D., Nguyen, C. D., Panichella, A., & Briand, L. C. (2018). A machine-learning-driven evolutionary approach for testing web application firewalls. IEEE Transactions on Reliability, 67(3), 733-757.

[11] Apruzzese, G., Colajanni, M., Ferretti, L., & Marchetti, M. (2019, May). Addressing adversarial attacks against security systems based on machine learning. In 2019 11th international conference on cyber conflict (CyCon) (Vol. 900, pp. 1-18). IEEE.

[12] Ashiedu, B. I., Ogbuefi, E., Nwabekee, U. S., Ogeawuchi, J. C., & Abayomi, A. A. (2020). Developing financial due diligence frameworks for mergers and acquisitions in emerging telecom markets. Iconic Research and Engineering Journals, 4(1), 183–196. https://www.irejournals.com/paper-details/1708562

[13] Bankole, A. O., Nwokediegwu, Z. S., & Okiye, S. E. (2020). Emerging cementitious composites for 3D printed interiors and exteriors: A materials innovation review. Journal of Frontiers in Multidisciplinary Research, 1(1), 127–144. ISSN: 3050-9726

[14] Biggio, B., & Roli, F. (2018, October). Wild patterns: Ten years after the rise of adversarial machine learning. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (pp. 2154-2156).

[15] Bolanle, O., & Bamigboye, K. (2019). AI-Powered Cloud Security: Leveraging Advanced Threat Detection for Maximum Protection. International Journal of Trend in Scientific Research and Development, 3(2), 1407-1412.

[16] Brynskov, M., Facca, F. M., & Hrasko, G. (2018). Next Generation Internet of Things. H2020 Coordination and Support Action (CSA), NGIoT Consortium, 2021, 2019.

[17] Calloway, M. (2010). AI-Powered Threat Detection, Intrusion Prevention, and Network Security. International Journal of Artificial Intelligence and Machine Learning, 10(10).

[18] Chen, T., Liu, J., Xiang, Y., Niu, W., Tong, E., & Han, Z. (2019). Adversarial attack and defense in reinforcement learning-from AI security view. Cybersecurity, 2(1), 11.

[19] Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine learning based mobile malware detection using highly imbalanced network traffic. Information Sciences, 433, 346-364.

[20] Choraś, M., & Kozik, R. (2015). Machine learning techniques applied to detect cyber attacks on web applications. Logic Journal of IGPL, 23(1), 45-56.

[21] Cybenko, G., Jajodia, S., Wellman, M. P., & Liu, P. (2014, December). Adversarial and uncertain reasoning for adaptive cyber defense: Building the scientific foundation. In International conference on information systems security (pp. 1-8). Cham: Springer International Publishing.

[22] Dalal, A. (2018). Cybersecurity And Artificial Intelligence: How AI Is Being Used in Cybersecurity To Improve Detection And Response To Cyber Threats. Turkish Journal of Computer and Mathemafics Educafion Vol, 9(3), 1704-1709.

[23] Dalal, A. (2019). AI Powered Threat Hunting in SAP and ERP Environments: Proactive Approaches to Cyber Defense. Available at SSRN 5198746.

[24] Dasgupta, P., & Collins, J. (2019). A survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks. AI Magazine, 40(2), 31-43.

[25] De Spiegeleire, S., Maas, M., & Sweijs, T. (2017). Artificial intelligence and the future of defense: strategic implications for small-and medium-sized force providers. The Hague Centre for Strategic Studies.

[26] Dogho, M. (2011). The design, fabrication and uses of bioreactors. Obafemi Awolowo University.

[27] Duddu, V. (2018). A survey of adversarial machine learning in cyber warfare. Defence Science Journal, 68(4), 356.

[28] Elish, M. C. (2018, October). The stakes of uncertainty: developing and integrating machine learning in clinical care. In Ethnographic Praxis in Industry Conference Proceedings (Vol. 2018, No. 1, pp. 364-380).

[29] Falaiye, T. (2018). Strategies for Improving Correspondent Banking Cross-Border Remittances. Walden University.

[30] Feng, M., & Xu, H. (2017, November). Deep reinforecement learning based optimal defense for cyber-physical system in presence of unknown cyber-attack. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.

[31] Filani, O. M., Olajide, J. O., & Osho, G. O. (2020). Designing an Integrated Dashboard System for Monitoring Real-Time Sales and Logistics KPIs.

[32] Gan, J., Li, S., Zhai, Y., & Liu, C. (2017, March). 3d convolutional neural network based on face anti-spoofing. In 2017 2nd international conference on multimedia and image processing (ICMIP) (pp. 1-5). IEEE.

[33] Ganesan, R., Jajodia, S., Shah, A., & Cam, H. (2016). Dynamic scheduling of cybersecurity analysts for minimizing risk using reinforcement learning. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), 1-21.

[34] Glomsrud, J. A., Ødegårdstuen, A., Clair, A. L. S., & Smogeli, Ø. (2019, September). Trustworthy versus explainable AI in autonomous vessels. In Proceedings of the International Seminar on Safety and Security of Autonomous Vessels (ISSAV) and European STAMP Workshop and Conference (ESWC) (Vol. 37).

[35] Gopireddy, S. R. (2019). AI-Augmented Honeypots for Cloud Environments: Proactive Threat Deception. European Journal of Advances in Engineering and Technology, 6(12), 85-89.

[36] Gudala, L., Shaik, M., Venkataramanan, S., & Sadhu, A. K. R. (2019). Leveraging artificial intelligence for enhanced threat detection, response, and anomaly identification in resource-constrained iot networks. Distributed Learning and Broad Applications in Scientific Research, 5, 23-54.

[37] Hagras, H. (2018). Toward human-understandable, explainable AI. Computer, 51(9), 28-36.

[38] Hameed, A., & Suleman, M. (2019). AI-Powered Anomaly Detection for Cloud Security: Leveraging Machine Learning and DSPM.

[39] Han, Y., Rubinstein, B. I., Abraham, T., Alpcan, T., De Vel, O., Erfani, S., ... & Montague, P. (2018, September). Reinforcement learning for autonomous defence in software-defined networking. In International conference on decision and game theory for security (pp. 145-165). Cham: Springer International Publishing.

[40] Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2019). Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Transactions on Industrial Informatics, 16(10), 6532-6542.

[41] He, K., & Kim, D. S. (2019, August). Malware detection with malware images using deep learning techniques. In 2019 18th IEEE international conference on trust, security and privacy in computing and communications/13th IEEE international conference on big data science and engineering (TrustCom/BigDataSE) (pp. 95-102). IEEE.

[42] Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2018, August). Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In International cross-domain conference for machine learning and knowledge extraction (pp. 1-8). Cham: Springer International Publishing.

[43] Huang, L., & Zhu, Q. (2019, October). Adaptive honeypot engagement through reinforcement learning of semi-markov decision processes. In International conference on decision and game theory for security (pp. 196-216). Cham: Springer International Publishing.

[44] Hughes, E. (2015). AI-Driven Cybersecurity System: Benefits and Vulnerabilities. International Journal of Artificial Intelligence and Machine Learning, 6(1).

[45] Hurley, J. S. (2018). Enabling successful artificial intelligence implementation in the department of defense. Journal of Information Warfare, 17(2), 65-82.

[46] Ibitoye, O., Abou-Khamis, R., Shehaby, M. E., Matrawy, A., & Shafiq, M. O. (2019). The Threat of Adversarial Attacks on Machine Learning in Network Security--A Survey. arXiv preprint arXiv:1911.02621.

[47] Indriasari, E., Soeparno, H., Gaol, F. L., & Matsuo, T. (2019, July). Application of predictive analytics at financial institutions: a systematic literature review. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 877-883). IEEE.

[48] Jaroszewski, A. C., Morris, R. R., & Nock, M. K. (2019). Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services. Journal of consulting and clinical psychology, 87(4), 370.

[49] Khurana, R., & Kaul, D. (2019). Dynamic cybersecurity strategies for ai-enhanced ecommerce: A federated learning approach to data privacy. Applied Research in Artificial Intelligence and Cloud Computing, 2(1), 32-43.

[50] Kolluri, V. E. N. K. A. T. E. S. W. A. R. A. N. A. I. D. U. (2016). A Pioneering Approach To Forensic Insights: Utilization AI for Cybersecurity Incident Investigations. IJRAR-International Journal of Research and Analytical Reviews (IJRAR), E-ISSN, 2348-1269.

[51] Konn, A. (2018). Next-Generation Cybersecurity: Harnessing AI for Detecting and Preventing Cyber-Attacks in Cloud Environments.

[52] Kozik, R., & Choraś, M. (2014). Machine learning techniques for cyber attacks detection. In Image Processing and Communications Challenges 5 (pp. 391-398). Heidelberg: Springer International Publishing.

[53] Kumari, M., Hsieh, G., & Okonkwo, C. A. (2017, December). Deep learning approach to malware multi-class classification using image processing techniques. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 13-18). IEEE.

[54] Laskov, P., & Lippmann, R. (2010). Machine learning in adversarial environments. Machine learning, 81(2), 115-119.

[55] Laura, M., & James, A. (2019). Cloud Security Mastery: Integrating Firewalls and AI-Powered Defenses for Enterprise Protection. International Journal of Trend in Scientific Research and Development, 3(3), 2000-2007.

[56] Lawless, W. F., Mittu, R., Sofge, D., & Hiatt, L. (2019). Artificial intelligence, autonomy, and human-machine teams interdependence, context, and explainable AI. Ai Magazine, 40(3), 5-13.

[57] Liao, R., Wen, H., Pan, F., Song, H., Xu, A., & Jiang, Y. (2019, March). A novel physical layer authentication method with convolutional neural network. In 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 231-235). IEEE.

[58] Liu, Q., Li, P., Zhao, W., Cai, W., Yu, S., & Leung, V. C. (2018). A survey on security threats and defensive techniques of machine learning: A data driven view. IEEE access, 6, 12103-12117.

[59] Mansoor, A. (2019). Mitigating Cyber-Attacks with AI-Driven Cybersecurity Solutions in Cloud and Device Technologies.

[60] Masoud, M., Jaradat, Y., & Ahmad, A. Q. (2016, December). On tackling social engineering web phishing attacks utilizing software defined networks (SDN) approach. In 2016 2nd International Conference on Open Source Software Computing (OSSCOM) (pp. 1-6). IEEE. Manickam, M., Ramaraj, N., & Chellappan, C. (2019). A combined PFCM and recurrent neural network-based intrusion detection system for cloud environment. International Journal of Business Intelligence and Data Mining, 14(4), 504-527.

[61] Mavroeidis, V., & Bromander, S. (2017, September). Cyber threat intelligence model: an evaluation of taxonomies, sharing standards, and ontologies within cyber threat intelligence. In 2017 European Intelligence and Security Informatics Conference (EISIC) (pp. 91-98). IEEE.

[62] Menson, W. N. A., Olawepo, J. O., Bruno, T., Gbadamosi, S. O., Nalda, N. F., Anyebe, V., ... & Ezeanolue, E. E. (2018). Reliability of self-reported Mobile phone ownership in rural north-Central Nigeria: cross-sectional study. JMIR mHealth and uHealth, 6(3), e8760.

[63] Mgbame, A. C., Akpe, O. E. E., Abayomi, A. A., Ogbuefi, E., Adeyelu, O. O., & Mgbame, A. C. (2020). Barriers and enablers of BI tool implementation in underserved SME communities. IRE Journals, 3(7), 211-223.

[64] Mgbame, C. A., Akpe, O. E., Abayomi, A. A., Ogbuefi, E., & Adeyelu, O. O. (2020). Barriers and Enablers of Healthcare Analytics Tool Implementation in Underserved Healthcare Communities. Healthcare Analytics, 45(45), 45-45.

[65] Mittal, S., Joshi, A., & Finin, T. (2019). Cyber-all-intel: An ai for security related threat intelligence. arXiv preprint arXiv:1905.02895.

[66] Mohammad, R. M., Thabtah, F., & McCluskey, L. (2014). Predicting phishing websites based on self-structuring neural network. Neural Computing and Applications, 25(2), 443-458.

[67] Mohammed, I. A. (2015). A technical and state-of-the-art assessment of machine learning algorithms for cybersecurity applications. International Journal of Current Science (IJCSPUB) www. ijcspub. org, ISSN, 2250-1770.

[68] Mohit, M. (2018). Federated Learning: An Intrusion Detection Privacy Preserving Approach to Decentralized AI Model Training for IOT Security.

[69] Mulafara, A. H. (2015). An Analysis of Credit Risk Impact on Loan Performance-Evidence from Selected Commercial Banks. International Journal of Innovative Research and Practices, 3(12), 23-32.

[70] Nauman, M., Tanveer, T. A., Khan, S., & Syed, T. A. (2018). Deep neural architectures for large scale android malware analysis. Cluster Computing, 21(1), 569-588.

[71] Nwokediegwu, Z. S., Bankole, A. O., & Okiye, S. E. (2019). Advancing interior and exterior construction design through large-scale 3D printing: A comprehensive review. IRE Journals, 3(1), 422-449. ISSN: 2456-8880

[72] Odinaka, N., Okolo, C. H., Chima, O. K., & Adeyelu, O. O. (2020). AI-Enhanced Market Intelligence Models for Global Data Center Expansion: Strategic Framework for Entry into Emerging Markets.

[73] Odinaka, N., Okolo, C. H., Chima, O. K., & Adeyelu, O. O. (2020). Data-Driven Financial Governance in Energy Sector Audits: A Framework for Enhancing SOX Compliance and Cost Efficiency.

[74] Odofin, O. T., Abayomi, A. A., Uzoka, A. C., Adekunle, B. I., Agboola, O. A., & Owoade, S. (2020, March). Developing microservices architecture models for modularization and scalability in enterprise systems. Iconic Research and Engineering Journals, 3(9), 323–333.

[75] Odofin, O. T., Agboola, O. A., Ogbuefi, E., Ogeawuchi, J. C., Adanigbo, O. S., & Gbenle, T. P. (2020). Conceptual framework for unified payment integration in multi-bank financial ecosystems. IRE Journals, 3(12), 1-13.

[76] Olasoji, O., Iziduh, E. F., & Adeyelu, O. O. (2020). A cash flow optimization model for aligning vendor payments and capital commitments in energy projects. IRE Journals, 3(10), 403-404.

[77] Olasoji, O., Iziduh, E. F., & Adeyelu, O. O. (2020). A regulatory reporting framework for strengthening SOX compliance and audit transparency in global finance operations. IRE Journals, 4(2), 240-241.

[78] Olasoji, O., Iziduh, E. F., & Adeyelu, O. O. (2020). A strategic framework for enhancing financial control and planning in multinational energy investment entities. IRE Journals, 3(11), 412-413.

[79] Oni, O., Adeshina, Y. T., Iloeje, K. F., & Olatunji, O. O. (2018). Artificial Intelligence Model Fairness Auditor For Loan Systems. Journal ID, 8993, 1162.

[80] Orren, D. (2019). Safe Employment of Augmented Reality in a Production Environment Final Report (No. ONROLCVA).

[81] O'Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., ... & Ashrafian, H. (2019). Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. The international journal of medical robotics and computer assisted surgery, 15(1), e1968.

[82] Otoum, S. (2019). Machine learning-driven intrusion detection techniques in critical infrastructures monitored by sensor networks (Doctoral dissertation, Université d'Ottawa/University of Ottawa).

[83] Oyedele, M. et al., 2020. Leveraging Multimodal Learning: The Role of Visual and Digital Tools in Enhancing French Language Acquisition. IRE Journals, 4(1), pp.197–199. ISSN: 2456-8880. https://www.irejournals.com/paper-details/1708636

[84] Pauwels, E., & Denton, S. W. (2018). Searching for privacy in the Internet of Bodies. The Wilson Quarterly, 42(2).

[85] Perumallaplli, R. (2017). Federated Learning Applications in Enterprise Network Management. Available at SSRN 5228699.

[86] Petrov, D., & Znati, T. (2018, October). Context-aware deep learning-driven framework for mitigation of security risks in BYOD-enabled environments. In 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC) (pp. 166-175). IEEE.

[87] Pham, C., Nguyen, L. A., Tran, N. H., Huh, E. N., & Hong, C. S. (2018). Phishing-aware: A neuro-fuzzy approach for anti-phishing on fog networks. IEEE Transactions on Network and Service Management, 15(3), 1076-1089.

[88] Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained anomaly detection models for federated learning: An intrusion detection case study. Applied Sciences, 8(12), 2663.

[89] Renda, A. (2019). The age of foodtech: Optimizing the agri-food chain with digital technologies. In Achieving the sustainable development goals through sustainable food systems (pp. 171-187). Cham: Springer International Publishing.

[90] Ridley, A. (2018). Machine learning for autonomous cyber defense. The Next Wave, 22(1), 7-14.

[91] Sahingoz, O. K., Baykal, S. I., & Bulut, D. (2018). Phishing detection from urls by using neural networks. Computer Science & Information Technology (CS & IT), 41-54.

[92] Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, 345-357.

[93] Sareddy, M. R., & Hemnath, R. (2019). Optimized federated learning for cybersecurity: Integrating split learning, graph neural networks, and hashgraph technology. International Journal of HRM and Organizational Behavior, 7(3), 43-54.

[94] Sethi, T. S., Kantardzic, M., Lyu, L., & Chen, J. (2018). A dynamic‐adversarial mining approach to the security of machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(3), e1245.

[95] Shah, H. (2017). Deep Learning in Cloud Environments: Innovations in AI and Cybersecurity Challenges. Revista Espanola de Documentacion Cientifica, 11(1), 146-160.

[96] Shi, Y., Sagduyu, Y. E., Davaslioglu, K., & Levy, R. (2018). Vulnerability detection and analysis in adversarial deep learning. In Guide to vulnerability analysis for computer networks and systems: An artificial intelligence approach (pp. 211-234). Cham: Springer International Publishing.

[97] Smadi, S., Aslam, N., & Zhang, L. (2018). Detection of online phishing email using dynamic evolving neural network based on reinforcement learning. Decision Support Systems, 107, 88-102.

[98] Sowah, R. A., Ofori-Amanfo, K. B., Mills, G. A., & Koumadi, K. M. (2019). Detection and prevention of man‐in‐the‐middle spoofing attacks in MANETs using predictive techniques in artificial neural networks (ANN). Journal of Computer Networks and Communications, 2019(1), 4683982.

[99] Su, X., Zhang, D., Li, W., & Zhao, K. (2016, August). A deep learning approach to android malware feature learning and detection. In 2016 IEEE Trustcom/BigDataSE/ISPA (pp. 244-251). IEEE.

[100] Svenmarck, P., Luotsinen, L., Nilsson, M., & Schubert, J. (2018, May). Possibilities and challenges for artificial intelligence in military applications. In Proceedings of the NATO big data and artificial intelligence for military decision making specialists’ meeting (Vol. 1).

[101] Thangan, M. S. S., Gulhane, V. S., & Karale, N. E. (2019). Review on “Using Big Data to Defend Machines against Network Attacks”.

[102] Tian, Z., Luo, C., Qiu, J., Du, X., & Guizani, M. (2019). A distributed deep learning system for web attack detection on edge devices. IEEE Transactions on Industrial Informatics, 16(3), 1963-1971.

[103] Tobiyama, S., Yamaguchi, Y., Shimada, H., Ikuse, T., & Yagi, T. (2016, June). Malware detection with deep neural network using process behavior. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC) (Vol. 2, pp. 577-582). IEEE.

[104] Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019). Robust intelligent malware detection using deep learning. IEEE access, 7, 46717-46738.

[105] Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2018). Detecting malicious domain names using deep learning approaches at scale. Journal of Intelligent & Fuzzy Systems, 34(3), 1355-1367.

[106] Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019). Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 18(5), 2438-2455.

[107] Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2019). VerifyNet: Secure and verifiable federated learning. IEEE Transactions on Information Forensics and Security, 15, 911-926.

[108] Yarali, A., Ramage, M. L., May, N., & Srinath, M. (2019, April). Uncovering the true potentials of the internet of things (IoT). In 2019 Wireless Telecommunications Symposium (WTS) (pp. 1-6). IEEE.

[109] Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications surveys & tutorials, 21(3), 2224-2287.

[110] Zhong, W., & Gu, F. (2019). A multi-level deep learning system for malware detection. Expert Systems with Applications, 133, 151-162.

[111] Zhou, P., Wang, K., Guo, L., Gong, S., & Zheng, B. (2019). A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems. IEEE Transactions on Knowledge and Data Engineering, 33(3), 824-838.

[112] Zhu, M., Hu, Z., & Liu, P. (2014, November). Reinforcement learning algorithms for adaptive cyber defense against heartbleed. In Proceedings of the first ACM workshop on moving target defense (pp. 51-58).

@article{1710638,
      author = {Olaolu Samuel Adesanya, Akindamola Samuel Akinola, Lawrence Damilare Oyeniyi},
      title = {Predictive Analytics Frameworks Strengthening Credit Risk Assessments and Lending Decisions in Financial Technology Ecosystems},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {10},
      pages = {526-551},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710638.pdf},
      abstract = {The rapid growth of financial technology (FinTech) has transformed traditional lending systems by introducing innovative models that expand financial inclusion and improve service delivery. However, this expansion has heightened the need for robust credit risk assessment frameworks that can balance accessibility with stability. This paper explores predictive analytics frameworks as a strategic tool for strengthening credit risk assessments and lending decisions within FinTech ecosystems. Unlike conventional credit scoring methods that rely heavily on historical financial data, predictive analytics incorporates diverse data sources  including transaction histories, social media activity, mobile usage, and alternative digital footprints  to create more dynamic and inclusive credit profiles. The study examines how machine learning algorithms, data mining techniques, and artificial intelligence are applied to identify patterns and predict borrower behavior with higher accuracy. Predictive analytics frameworks enable real-time risk modeling, adaptive scoring, and early-warning systems that help lenders anticipate defaults, optimize loan portfolios, and reduce non-performing loans. By integrating big data analytics into credit decision-making, FinTech firms can better manage risks while extending credit to underserved populations traditionally excluded from formal financial systems. In addition, the paper highlights the broader benefits of predictive analytics, including enhanced transparency, improved regulatory compliance, and greater investor confidence in digital lending platforms. Case studies illustrate how FinTech companies across emerging and developed markets have leveraged predictive models to achieve significant improvements in lending efficiency, customer acquisition, and repayment performance. Challenges such as data privacy concerns, algorithmic bias, and regulatory constraints are critically assessed, with recommendations for ensuring ethical and responsible use of predictive tools in credit markets. Ultimately, the study concludes that predictive analytics frameworks represent a paradigm shift in credit risk management, offering FinTech ecosystems the ability to strengthen lending decisions, foster financial inclusion, and build resilient digital financial systems. By bridging traditional financial analysis with advanced data-driven methodologies, these frameworks position FinTech as a catalyst for sustainable growth in the global financial sector.},
      keywords = {Predictive Analytics, Credit Risk Assessment, Lending Decisions, Fintech Ecosystems, Machine Learning, Big Data, Financial Inclusion, Non-Performing Loans, Algorithmic Modeling, Digital Lending.},
      month = {April},
  }