• Sathea Sree.S
• Nalini Joseph.L

Contemporary financial operations have increasingly replaced conventional financial administration due to the advanced computational effects of graph theory and machine learning advancements in the field of corporate growth during the last several decades. When it comes to detecting financial risk concerns, most graph-oriented neural networks employ sparse dimensional features of an organization. On the other hand, the past techniques fail to take into account many of the dynamic features, such as their actions and financial risk indicators. Thus, due to the identified issues of ineffectiveness, massive usage of resources and time, and limited intellect in the established automated financial information estimation processes, in this research, we proposed a GLSTM (Graph LSTM) model to assess the uncertain and dynamic financial risk elements of any organization. The model is subdivided into three distinct parts: The first part includes a static strategy to identify the organization's influential risk indicators. The second part proceeds to enhance organizational data by collecting temporal and conceptual data using a graph-based short-term network encoder. Finally, the third part gathers long-term data from the stable components with dynamic recordsets using the GLSTM-based long-term dynamical model with respect to temporal facts. By comparing the findings with several prominent graph-based models, the proposed approach demonstrates the usefulness of GLSTM strategies that could considerably enhance and standardize the financial sectors. For financial assessments, the estimated statistical measures like Kolmogorov-Smirnov statistics (18.97%) and AUC (Area Under Curve) (4.37%) reported better improvements.

LSTM, Graph, Risk Analysis, Financial, Risk Prediction, Accuracy, Loss Function, Machine Learning

"Assessment of Uncertain Financial Risk using GLSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.c217-c242, December-2023, Available :http://www.jetir.org/papers/JETIR2312251.pdf

"Assessment of Uncertain Financial Risk using GLSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppc217-c242, December-2023, Available at : http://www.jetir.org/papers/JETIR2312251.pdf