AI-Augmented FinTech Platforms for Real-Time Credit Risk and Supply Chain Financing in Smart Industries
subject: Artificial Intelligence; Credit Risk; FinTech; Smart Industries; Supply Chain
description: A combination of Artificial Intelligence (AI) and FinTech platforms has transformed the financial services sector, specifically, real-time credit risk evaluation and supply chain financing of smart industries. The conventional models of credit assessment, based on the use of fixed financial information and manual processing, are ineffective in capturing the dynamic nature of the modern-day industrial process. This paper conducts empirical research on AI-enhanced FinTech applications and utilizes machine learning (ML), natural language processing (NLP), and multi-modal industrial data, such as financial data, IoT sensor data, and supply chain data, to enable better predictive models and decision processes. The study compares several models, such as ensemble learning and deep neural networks, to predict credit risk and maximize the financing. Findings show that this is highly improved with AUC scores more than 0.88 and reduction in decision latency up to 70 percent showing quicker more information-oriented and context-sensitive risk management. It offers practical implications in the design of AI-based financial solutions, which will allow making smarter credit decisions and allocating working capital in intelligent industries more effectively, and it also notes that AI can transform industrial FinTech ecosystems. 2026 IEEE.
creator: Sistla, Swetha; Sankaran, Mohan; Jooluri, Nagaraju; Fikri, Yahya; Shruthaalaxmi; Devesh, Sonal
source: 2026 Innovations in Machine, Engineering, and Digital Conference, IMED 2026;
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833156997-6;
language: English
type: Conference paper
coverage: Sistla S., FinTech & Ai Solutions Infosys Limited, Richmond, 23060, VA, United States; Sankaran M., Software Engineering PayPal Inc, 94555, CA, United States; Jooluri N., Software Engineering Incode Technologies, 20148, VA, United States; Fikri Y., Abdelmalek Essaadi University, Department of Governance And Performance of Organizations, Tetouan, Morocco; Shruthaalaxmi, Statistics University of Toronto, Department of Public Policy, Toronto, Canada; Devesh S., Christ University (Deemed To Be University), School of Business And Management, Karnataka, Bangalore, 560073, India