Improving Financial Forecasting Accuracy with AI-Driven Predictive Analytics
Keywords:
Financial Service Providers (FSPs), CNN Model, LSTM, Calculation Efficiency, Classification Prediction Model, AI-Driven, Big Data, Financial Profile.Abstract
Propose: Financial inclusion is essential for reducing poverty and promoting prosperity, according to the United Nations World Organisation. Financial Service Providers (FSPs) that provide solutions that are inclusive of all income levels must know how to effectively reach out to the underprivileged. FSPs can anticipate prospective clients' reactions as they approach them by using Artificial Intelligence (AI) on old data. This study predicts schools' and institutions' financial characteristics using big data technology.
Method: This paper uses big data to simulate a human being and uses an AI-driven edge cloud computing assistance optimisation algorithm to form a cluster based on the individual's usage of private passions and interests, daily life consumption, and other indications. This allows the prediction to be realised from a component to a neural network-based cluster using the use of edge computing.
Results: Furthermore, in order to test the model for forecasting, this study uses employment statistics from higher learning institutions in the province of Hunan from June 2020 to May 2021 as the study's sample. It then compares the CNN and LSTM models. The precision of predictions can reach 83.25% since the edge fog computing model in this research contains more analytical indexes as tuples than the model used by CNN.
Conclusion: This research additionally proposes the use of AI-Thinking as a cognitively scaffold to reduce (pull out) actionable findings in order to promote inclusion in the economy. When contrasted to the LSTM-based classification predictions model, this model uses the use of edge computing, which significantly enhances the model's and its parameter' data quality and can increase calculations efficiency by 45%–65%.
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