AI-Driven Predictive Analytics for Early Detection of Third-Party Financial Distress Across United States Supply Chains

Author

Bridget Akano

Abstract

 Financial distress among third-party suppliers rarely begins with formal insolvency; it often emerges through deteriorating operational and commercial signals that conventional annual financial reviews detect too late. Within United States supply chains, delayed supplier failure can interrupt production, increase procurement costs, and transmit disruption across dependent firms. This study develops an AI-driven predictive analytics framework for identifying financial distress before supplier default or operational breakdown. Rather than relying primarily on static credit ratings, the framework integrates changes in days-payable patterns, liquidity deterioration, leverage, cash-flow volatility, invoice-payment behaviour, order cancellations, lead-time instability, shipment delays, capacity utilization, and buyer concentration. Machine-learning models estimate time-varying distress probabilities and distinguish temporary operational volatility from persistent financial deterioration. Risk trajectories are subsequently connected to intervention thresholds for enhanced supplier review, payment restructuring, inventory buffering, dual sourcing, and supplier substitution. Particular emphasis is placed on detecting distress among privately held suppliers with limited financial disclosure and identifying cascading exposure created by critical supplier dependencies. The framework provides an explainable early-warning mechanism for converting fragmented financial and operational signals into proactive third-party risk mitigation decisions.

Keywords

Third-Party Financial Distress; Supplier Insolvency Prediction; Dynamic Risk Scoring; Financial Early-Warning Systems; Supplier Dependency Risk; Predictive Supply Chain Analytics

Full Text:

Download Paper PDF

References

1. Chen W, Men Y, Fuster N, Osorio C, Juan AA. Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability. 2024 Oct 22;16(21):9145.
2. Rehan H. Enhancing early detection and management of chronic diseases with AI-Driven predictive analytics on healthcare cloud platforms. Journal of AI-Assisted Scientific Discovery. 2024;4(2):1-38.
3. Charles V, Emrouznejad A, Gherman T. A critical analysis of the integration of blockchain and artificial intelligence for supply chain. Annals of operations research. 2023 Aug;327(1):7-47.
4. Adenekan OA, Ezeigweneme C, Chukwurah EG. Strategies for protecting IT supply chains against cybersecurity threats. International Journal of Management & Entrepreneurship Research. 2024 May;6(5):1598-606.
5. Chukwunweike J. Coordinating PLC-based load shedding and variable-speed drives to prevent transformer overloading during industrial production peaks. Int J Electr Data Commun. 2025;6(2 Pt B):118-135. doi:10.22271/27083969.2025.v6.i2b.109.
6. Dey PK, Chowdhury S, Abadie A, Vann Yaroson E, Sarkar S. Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small-and medium-sized enterprises. International Journal of Production Research. 2024 Aug 2;62(15):5417-56.
7. Obunadike TC. Leveraging AI forecasting to quantify tariff-induced food price volatility in net-importing nations. Int J Res Publ Rev. 2025;6(6):12441-12458. doi:10.55248/gengpi.6.0625.23102.
8. Chava K, Saradhi KS. Emerging applications of generative AI and deep neural networks in modern pharmaceutical supply chains: a focus on automated insights and decision-making. South Eastern European Journal of Public Health. 2024;22:20-45.
9. Samuel Addo. Harnessing AI-powered financial forecasting tools to improve risk management, investment strategies, and overall corporate profitability across industries. International Journal of Science and Research Archive, 2023, 09(02), 1175-1192. Article DOI: https://doi.org/10.30574/ijsra.2023.9.2.0653
10. George AS. Finance 4.0: The transformation of financial services in the digital age. Partners Universal Innovative Research Publication. 2024 Jun 25;2(3):104-25.
11. Ogan RK. Predictive anomaly detection for real-time virtual account transactions and API-driven payment processing across enterprise banking platforms. Int J Comput Appl Technol Res. 2024;13(12):298-308. doi:10.7753/IJCATR1312.1023.
12. Adenuga AA, Gaffar O, Sikiru AO, Otunba M. AI in financial services and fraud prevention: Enhancing fraud detection, risk management, and creditworthiness assessment. Gyanshauryam, International Scientific Refereed Research Journal. 2023 Mar;6(2).
13. Samuel Addo. Causal AI for strategic business planning: uncovering latent drivers of long-term organizational performance and resilience. World Journal of Advanced Research and Reviews, 2025, 26(2), 895-912. Article DOI: https://doi.org/10.30574/wjarr.2025.26.2.1738
14. Jinka S. AI Powered Intelligent Enterprise Platform for Financial Services Cloud Security and Predictive Risk Management. International Journal of Emerging Trends in Engineering and Management Research. 2023;8(4):13972.
15. Qu C, Kim E. Reviewing the roles of AI-integrated technologies in sustainable supply chain management: Research propositions and a framework for future directions. Sustainability. 2024 Jul 19;16(14):6186.
16. Ogan RK. Machine-learning framework for predicting point-of-sale integration failures across digital ordering, payment, and third-party delivery technology ecosystems. Int J Res Publ Rev. 2025;6(12):8887-8898.
17. Olowe KJ, Edoh NL, Zouo SJ, Olamijuwon J. Review of predictive modeling and machine learning applications in financial service analysis. Computer Science & IT Research Journal. 2024;5(11):2609-26.
18. Racheal Kikachukwu Ogan. (2023). AUTOMATED CONFIGURATION COMPLIANCE ANALYTICS FOR DETECTING OPERATIONAL DRIFT AND PREVENTING SERVICE DEGRADATION ACROSS MULTI-LOCATION RESTAURANT TECHNOLOGY SYSTEMS. INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY RESEARCH & MANAGEMENT (IJETRM), 07(12), 807–819. https://doi.org/10.5281/zenodo.22267240
19. Goli MG. AI-Driven Logistics for Automotive Supply Chains. American Advanced Journal for Emerging Disciplinaries (AAJED). 2023 Dec 13;1(01).
20. Ethel Esinam Atubrah. The green transition and environmental justice: Understanding unequal community impacts. Int J Geogr Geol Environ 2026;8(8):12-24. DOI: 22271/27067483.2026.v8.i8a.844
21. Akhtar P, Ghouri AM, Khan HU, Amin ul Haq M, Awan U, Zahoor N, Khan Z, Ashraf A. Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of operations research. 2023 Aug;327(2):633-57.
22. Magajiaka J, Adesanmi B. Lifecycle cost and resilience performance evaluation of climate-resilient urban road infrastructure under climate risk scenarios. International Journal of Current Research. 2026;18(5):37242-37251.
23. Alam MK, Fahad ML, Miah N. Guardian of the Vault: The Development of AI-Driven Solutions for Protecting Sensitive Financial Data in the US. American Journal of Economics and Business Management. 2024 Feb 28;7(2):219-49.
24. Racheal Kikachukwu Ogan. Machine-learning-based API failure prediction and root-cause analytics for improving reliability of enterprise banking integration architectures at scale. Int J Comput Artif Intell 2021;2(2):142-153. DOI: 33545/27076571.2021.v2.i2a.378
25. Mithun MM, Tarannum R, Rimon RH. Governance Gaps AI-Driven Fraud Detection: Machine Learning Strategies for Countering Generative Fraud in the US Financial System. Frontiers in Computer Science and Artificial Intelligence. 2024 Jan 25;3(1):97-119.
26. Ogan RK. Risk-based API governance for predicting integration failures, security anomalies, and service degradation across hybrid enterprise application environments. Int J Finance Manage Econ. 2024;7(2):871-882. doi:10.33545/26179210.2024.v7.i2.946.
27. Alonge EO, Eyo-Udo NL, Ubanadu BC, Daraojimba AI, Balogun ED, Ogunsola KO. Real-time data analytics for enhancing supply chain efficiency. Journal of Supply Chain Management and Analytics. 2023;10(1):49-60.
28. Ethel Esinam Atubrah. CLIMATE ADAPTATION POLICY AND COMMUNITY RESILIENCE: CULTURAL IDENTITY, LIVELIHOODS, AND ENVIRONMENTAL DISPLACEMENT. International Journal of Science and Research Archive, 2026, 20(02), 356–370. Article DOI: https://doi.org/10.30574/ijsra.2026.20.2.1635.
29. Gunasekara A. AI-Driven Big Data Analytics for Transforming Cybersecurity for Zero-Day Vulnerabilities in E-Commerce Supply Chains. Journal of Advances in Cybersecurity Science, Threat Intelligence, and Countermeasures. 2023 Dec 7;7(12):17-31.
30. Bello OA, Ogundipe A, Mohammed D, Adebola F, Alonge OA. AI-driven approaches for real-time fraud detection in US financial transactions: Challenges and opportunities. European Journal of Computer Science and Information Technology. 2023 Jan;11(6):84-102.
31. MGBAJIAKA, JOHNBOSCO. 2025. “Co-Designing Resilient Infrastructure: Participatory Approaches for Sustainable Implementation”. Current Journal of Applied Science and Technology44 (8):11-17. https://doi.org/10.9734/cjast/2025/v44i84586.
32. Prova NS. AI-Driven Demand Forecasting and Inventory Optimization for Resilient US Manufacturing Supply Chains: A Data-Driven Framework. Review of Applied Science and Technology. 2024 Dec 26;3(04):509-57.
33. Jabed MM, Ferdous S. Integrating business process intelligence with AI for real-time threat detection in critical US industries. International Journal of Research and Applied Innovations. 2024 Feb 14;7(1):10120-34.
34. Li C, Chen H. Machine learning based supply chain risk prediction and management. InProceeding of the 2024 5th International Conference on Computer Science and Management Technology 2024 Oct 18 (pp. 842-847).
35. Magoni D. AI and Machine Learning Powered Secure Enterprise Platforms with Real-Time APIs Financial Forecasting Risk Analytics and Blockchain. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM). 2024 Nov 22;7(6):11622-30.
36. Jeyachandran P. Implementing AI-Driven Strategies for First-and Third-Party Fraud Mitigation. Available at SSRN 5076791. 2024 Jul 3.
37. Brintrup A, Kosasih E, Schaffer P, Zheng G, Demirel G, MacCarthy BL. Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks. International Journal of Production Research. 2024 Jul 2;62(13):4674-95.
38. Yu X. Research on AI-driven supply chain financial risk management. InProceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management 2024 Nov 15 (pp. 965-970).
39. Atubrah EE. Environmental justice movements and environmental policy change: the role of community knowledge and place-based action. Int J Res Publ Rev. 2026;7(1):1169-1180. doi:10.55248/gengpi.07.0826.2444.
40. Mazumder PT. Data Driven Detection of Trade Based Money Laundering (TBML): A predictive Analytics Framework for Securing US supply chains and Financial Integrity. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology. 2023 Oct 20;15(04):423-32.