Abstract

In this work, we describe an adaptive financial management strategy, tailor-made to meet the demands of, and capitalize on, an economy ruled by AI. The suggested solution combines three essential algorithms: LSTM-based machine learning for economic forecasting; SHAP-based explainable AI for openness in decision-making; and blockchain technology with proof-of-work (PoW) security. This LSTM-based method handles the sequential data often seen in time series analysis, which is crucial for effective financial forecasting. It is particularly effective at identifying complex interrelationships in financial time series data, providing a solid basis for reliable forecasting. By giving each feature in a prediction model an equal amount of weight, the SHAP algorithm improves the openness of decisions. The experimental results confirm the superiority of the suggested technique over the conventional methods. It uses dynamic Machine Learning models, in particular LSTM networks, to provide more precise economic forecasts than static models based on averages. Using SHAP, explainable AI solves the problem of interpretability that plagues conventional techniques, allowing for more open deliberation. The combination of Blockchain with PoW gives better security, overcoming the risks of centralized systems employed in previous approaches. The suggested adaptive strategy provides a comprehensive and robust framework for managing finances in a world controlled by artificial intelligence.

Keywords: Adaptive Financial Management; AI Integration, Blockchain, Decision Transparency; Explainable AI; SHAP Algorithm; Tamper Resistance; Time Series Forecasting; Transparent Decision-Making.