A Real Time AI System for Automated Financial Technology Payment Detection and Risk Reduction

Abstract

The growing complexity and velocity of digital financial transactions have elevated the urgency for real-time, intelligent systems capable of detecting and mitigating financial stress. FinCheckAI is introduced as a real-time AI system that automates the detection of financial stress indicators and facilitates proactive risk reduction across institutional environments. The system integrates advanced machine learning models such as XGBoost for credit risk classification, LSTM for temporal pattern analysis, and NLP for parsing legal and compliance documents to extract multidimensional insights from high-frequency financial data. Unlike traditional financial diagnostics, FinCheckAI continuously monitors asset behavior, legal exposure, and performance trends to anticipate financial instability before critical thresholds are breached. Empirical analysis using transactional records, compliance logs, and institutional metrics demonstrates FinCheckAI’s effectiveness in reducing defaults, compliance violations, and liquidity constraints. The integration of explainable AI further ensures regulatory transparency and auditability. This study positions FinCheckAI as a scalable RegTech solution for strengthening digital financial ecosystems through real-time, automated stress surveillance and risk governance.