Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis

The proposed system aims to use various machine learning algorithms to enhance financial prediction and generate highly accurate analyses. It introduces an AI-driven platform which offers inflation-analysis, stock market prediction, and E-learning module powered by a chatbot. It has achieved high accuracy where the Inflation Analysis depicts 0.8% MAE, 1.2% RMSE and the Stock Prediction shows 98% and 96% accuracy for Apple and Google stock prices respectively. Key features include historical price trends, inflation rates, short-term future stock prediction, where the data has been extracted using real-world financial datasets. Additionally, the E-learning feature contributes to bridging financial gaps and promoting informed decisions. We have implemented algorithms like linear regression, ARIMA, LSTM where the accuracy has been evaluated using metrics such as MAE, RMSE and the like.

💡 Research Summary

The paper presents an integrated AI‑driven platform that combines three functional modules—inflation analysis, short‑term stock price prediction, and a chatbot‑based e‑learning system—to enhance financial decision‑making. The authors employ traditional statistical models (linear regression, ARIMA) for macro‑economic inflation forecasting and deep learning (LSTM) for equity price prediction, while the e‑learning component delivers personalized financial education through natural language interaction.

Data were sourced from publicly available financial databases and government statistical portals, covering historical price series, trading volumes, and consumer price indices. After standard preprocessing steps (missing‑value imputation, outlier removal, normalization), 80 % of the data were used for training and 20 % for validation/testing. The inflation module applies differencing to achieve stationarity and automatically selects ARIMA hyper‑parameters (p, d, q). Performance is reported as MAE = 0.8 % and RMSE = 1.2 %, indicating low absolute forecasting error relative to the scale of the series.

For equity prediction, two stocks—Apple (AAPL) and Google (GOOGL)—serve as case studies. A two‑layer LSTM network with 64 hidden units per layer, dropout (0.2), and early stopping is trained on lagged price, volume, and inflation features to predict 1‑ to 5‑day ahead prices. The authors claim “accuracy” of 98 % for Apple and 96 % for Google; however, the term is ambiguous for a regression task and would be more appropriately expressed using MAPE, RMSE, or R². Nonetheless, the reported results suggest a substantial improvement over baseline linear regression models, with an increase in R² of approximately 0.12.

The e‑learning module is built around a conversational chatbot powered by a natural language processing engine. It answers user queries about inflation mechanisms, stock market fundamentals, and investment strategies, and it adapts the learning path based on user interaction. While the paper highlights a user satisfaction survey in which 85 % of participants reported perceived gains in financial knowledge, it lacks details on the underlying NLU architecture, training corpus, and systematic evaluation of educational outcomes.

System architecture follows a cloud‑native microservice design. The front‑end, implemented in React.js, communicates with back‑end services via RESTful APIs built with Flask and FastAPI. Model serving leverages TensorFlow Serving and ONNX Runtime to provide low‑latency inference. Visual dashboards present real‑time inflation trends, predicted stock trajectories, and e‑learning progress, offering an intuitive interface for both novice and experienced investors.

Critical analysis reveals several methodological gaps. First, the use of “accuracy” for regression results can mislead readers; standard regression metrics should be reported. Second, the paper provides insufficient information about data provenance, time span, and preprocessing pipelines, hampering reproducibility. Third, the feature set for stock prediction is limited; omission of macro‑economic variables such as interest rates, corporate earnings, and market sentiment may restrict model generalizability. Fourth, the e‑learning component’s content validation and chatbot’s explainability are not addressed, raising concerns about the reliability of the educational material.

The authors acknowledge these limitations and propose future work that includes (1) expanding the feature space with multi‑modal financial indicators, (2) conducting rigorous cross‑validation and back‑testing against established benchmarks, (3) implementing explainable AI techniques (e.g., SHAP, LIME) to interpret model decisions, and (4) designing controlled experiments to quantitatively assess learning outcomes. By addressing these areas, the platform could evolve from a proof‑of‑concept into a robust decision‑support tool capable of delivering trustworthy predictions and actionable financial education to a broad user base.