MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641107130 A) filed by Madhankumar C; Dr. A. Vetrivel - Sns College Of Technology; Ms K. Ramanandhini - Rathinam Technical Campus; Mr P. Chellaiya - Npr College Of Engineering And Technology; Dr. S. V. Sudha - Rathinam Technical Campus; Ms Narmadha R - Karunya Institute Of Technology And Sciences; and Ms Snega C - S. A. College Of Arts And Science on September 06, 2026, for An Artificial Intelligence Framework For Real-Time Digital Commerce, Financial Risk Management, And Tax Compliance Using Explainable Predictive Analytics.
Inventors include Dr. A. Vetrivel - Sns College Of Technology; Ms K. Ramanandhini - Rathinam Technical Campus; Mr P. Chellaiya - Npr College Of Engineering And Technology; Dr. S. V. Sudha - Rathinam Technical Campus; Ms Narmadha R - Karunya Institute Of Technology And Sciences; and Ms Snega C - S. A. College Of Arts And Science.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: An Artificial Intelligence Framework for Real-Time Digital Commerce, Financial Risk Management, and Tax Compliance Using Explainable Predictive Analytics ABSTRACT The present invention relates to an Artificial Intelligence Framework for Real-Time Digital Commerce, Financial Risk Management, and Tax Compliance Using Explainable Predictive Analytics, configured to provide intelligent, transparent, and real-time analysis of digital commerce transactions, financial activities, and taxation-related information. The framework integrates transaction data acquisition, data preprocessing, feature extraction, predictive analytics, financial risk assessment, tax compliance analysis, anomaly detection, explainable artificial intelligence, and real-time decision-support mechanisms within a unified architecture. The system receives heterogeneous data including digital payment transactions, purchase and sales records, invoices, customer and merchant information, financial records, transaction histories, tax-related data, and applicable regulatory parameters. A data processing module performs normalization, validation, categorization, temporal analysis, and feature generation to identify transaction and financial patterns. An artificial intelligence analytics engine applies machine-learning and predictive models to identify abnormal transactions, financial risks, potential revenue leakage, irregular payment behaviour, and tax-related inconsistencies. A tax compliance module compares transaction and financial information with configurable taxation rules, applicable rates, reporting requirements, invoice information, and filing parameters to identify potential discrepancies requiring review. An explainable predictive analytics module generates human-readable explanations by identifying significant transaction attributes, contributing factors, detected patterns, applicable rules, and evidence associated with each prediction or risk score. A real-time monitoring module continuously evaluates incoming transactions and dynamically updates risk and compliance assessments. The framework further provides risk scoring, compliance indicators, alerts, predictive reports, interactive dashboards, and evidence-linked recommendations for authorized merchants, financial institutions, tax professionals, auditors, regulators, and business administrators. The invention improves transparency, early identification of financial and compliance risks, operational efficiency, and traceability while maintaining human oversight for final financial, commercial, and regulatory decisions.
Disclaimer: Curated by HT Syndication.
