Integrating Big Data Analytics and Artificial Intelligence for Enterprise Financial Risk Management

Abstract

Enterprises face financial risks that are more numerous, more connected and faster-moving than ever before. At the same time, the volume of data available to measure those risks has grown enormously. IDC estimated that the global datasphere would expand from 33 zettabytes in 2018 to 175 zettabytes by 2025. Big data analytics provides the infrastructure to collect, store and process this information. Artificial intelligence (AI), particularly machine learning and natural language processing, provides the methods to extract predictive signals from it. This article examines how the two can be integrated to strengthen enterprise financial risk management. It reviews the concepts of enterprise risk management, big data and AI, and maps their applications across credit, market, liquidity, operational, cyber, fraud and compliance risk. It proposes a five-layer framework that links data sources, a data platform, AI analytics, risk decisions and governance. A simulation using 60,000 synthetic firm-quarters illustrates two central points. First, adding transaction data and text-based signals to financial ratios raised the accuracy of a financial distress early-warning model substantially: the area under the ROC curve rose from 0.745 to 0.827, and the share of distressed firms caught among the riskiest 10 percent rose from about 34 percent to 53 percent. Second, the more flexible model outperformed logistic regression only when the training sample exceeded about 20,000 observations, showing that big data and AI are complements. The article discusses evidence on adoption, the Indian context, including the Account Aggregator framework and the Digital Personal Data Protection Act, 2023, and the challenges of data quality, privacy, explainability and skills. It concludes with a practical roadmap for implementation.