Optimizing Business Process Automation Using A Hybrid Xgboost-Lstm Model

Abstract

In today's digital era, Business Process Automation (BPA) has emerged as a critical solution for achieving efficient operations, minimizing manual workload, and improving decision-making processes. Current BPA systems, however, have a limited ability to deal with sequential dependencies in event log data as well as structured process features, resulting in low prediction accuracy. Based on the Hybrid XGBoost-LSTM model that incorporates feature selection and sequential learning, this study aims to optimize BPA and enhance process prediction and decision-making support. The proposed framework would be based on BPI Challenge 2011 data, where real-world event logs are provided from financial business processes. Data preprocessing involves some data cleaning, normalization, and encoding of categorical data. The most relevant process attributes are selected using XGBoost, and temporal dependency in sequential business events is modeled using LSTM networks. The hybrid model integrates both outputs to enhance forecasting accuracy. Performance evaluation is conducted by the utilization of accuracy, precision, recall, F1-score, RMSE, p-value, and confidence interval for assessing statistical significance. The evaluation of the proposed models yielded experimental data indicating that the Hybrid XGBoost-LSTM model surpasses the individual performances of both XGBoost and LSTM models, achieving an accuracy of 0.92, a precision of 0.90, a recall of 0.94, an F1-score of 0.92, and an RMSE of 0.15. The enhancements are statistically significant at the 5% level (p < 0.05), and the 95% confidence interval is consistent and stable, indicating substantial stability and reliability. The hybrid model proposed here is able to take advantage of structured feature learning and sequential prediction to effectively improve the performance of BPA. It offers a more precise and reliable model for optimization of business processes and decision-making in a dynamic environment.