Assessing Machine Learning Performance in Financial Forecasting and AI-Driven Customer Service Systems

This study examines the multifaceted application of machine learning and artificial intelligence (AI) in two key, dynamically developing sectors: cryptocurrency market capitalisation forecasting and customer service optimisation. An analysis of the effectiveness of various regression models (Linear, Lasso, and Decision Tree Regression) in predicting the market capitalisation of 3 leading cryptocurrencies shows that a model's success is highly dependent on the specific characteristics of the asset. While linear models achieve exceptional accuracy (R2>0.99) for most major and liquid cryptocurrencies, nonlinear approaches like Decision Tree Regression prove superior for assets with more complex and nonlinear market dynamics, highlighting the need for a flexible approach to model selection. In parallel, the study analyses the implementation of AI in customer service, reviewing chat communication data with the AI assistant "Naomi" (January 26–February 8, 2025). The AI "Naomi" demonstrated high overall effectiveness in chat communication, resolving over 60% of inquiries. However, a significant number of unresolved chats due to customer inactivity or AI limitations indicate areas for further optimisation. In conclusion, the effective application of AI and machine learning requires a strategic approach tailored to the specific field. The key to success lies in careful model selection, prioritising technical reliability, and continuous adaptation and optimisation based on empirical data and a deep understanding of AI's limitations.