VISUALIZING DYNAMIC BALANCE-SHEET RISK IN NON-BANKING FINANCIAL COMPANIES THROUGH AI-ENABLED CASH-FLOW NETWORKS - Patil, Rupali; Raghuvanshi, Manisha; Wategaonkar, Somnath; Patil, Savita; Rane, Milind Eknath; Wyawahare, Medha V (ShodhKosh: Journal of Visual and Performing Arts, 2026)
Non-Banking Financial Companies (NBFCs) play a crucial role in the Indian credit landscape. However, their inherent vulnerabilities to liquidity crises position them as highly susceptible to significant shocks. Current approaches to risk assessment for these companies are inadequate for understanding the dynamics of liquidity risk. An AI-enabled approach to modeling the dynamic balance sheets of NBFCs using high-frequency data on their cash flows can help improve risk assessment for these companies. A network of NBFCs can be represented as a graph, with each NBFC as a node in the graph and the transactions between these companies forming the edges of the graph. The AI model that can be used to analyze this graph is a Temporal Graph Neural Network (TGNN). TGNNs are deep learning models specifically designed to learn from network graphs and capture the relationships between the entities represented in the graph nodes. On top of the TGNN, a dynamic risk index for NBFCs can be created by calculating various metrics for the graph created by the NBFCs. These metrics will represent the various aspects of the risk for these companies at any given time. Furthermore, the model can be evaluated using high-frequency datasets of NBFCs that have been simulated to contain realistic liquidity risk dynamics. These results can be compared with those of other risk models currently in use for NBFCs. The proposed model will have superior performance indicators to other risk models for NBFCs. Not only will the model be able to accurately forecast the risk of individual NBFCs, but also the liquidity risk that exists within the entire NBFC industry as a whole. This AI-enabled model will allow for the early identification of the liquidity risks that individual NBFCs and the industry as a whole face. Consequently, the regulators and the companies themselves will be able to take steps to mitigate these risks. Using such a model will improve the monitoring of the NBFC industry by effectively integrating artificial intelligence into the risk assessment of its constituent companies.