Citation :

AnirudhParupalli, 2024. "Business Intelligence in ERP ML-Based Comparative Study for Financial Forecasting" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 17-26.

Abstract :

Financial planning in the modern, dynamic business world is highly important in strategic decision-making. Conventional financial forecasting tools, combined with ERP systems, have characteristic difficulties in coping with the nonlinear, time-dependent, and unstructured nature of financial data. Deep learning (DL) is a potential solution for businesses, as forecasting tools that have high intelligence and accuracy become more in demand in business. The present paper suggests a hybrid CNN-LSTM model to improve the financial prediction capabilities of an ERP-based business intelligence system. Based on the CSMAR data containing more than 54,000 financial statements belonging to A-share listed companies, the method is capable of efficiently coping with both feature extraction using convolutional layers and modeling temporal relations using LSTM. A full preprocessing pipeline-data cleaning, data normalization, and dimensionality reduction with PCA offers the best data quality. Experimental evaluation of R², MAE, RMSE and MSE shows that the model has a high quality of performance with an R² of 0.92 and a low MAE of 0.036 as compared to other models like the random forest model and LSTM model. Following the investigation, the findings reveal that the model can be instrumental in coming up with accurate and practical financial projections; thus, it can be used as a valuable tool in the decision-making process, relying on the data in the business environment. This paper therefore examines the revolutionary potential of integrating deep learning technology into ERP systems to drive the next generation of financial analytics.

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Keywords :

ERP Systems, Financial Forecasting, Business Intelligence, Deep Learning, CNN-LSTM, Machine Learning, CSMAR Dataset, Predictive Analytics.