Designing an AI-Powered Tool for Financial Planning and Analysis: Enhancing Forecasting and Scenario Planning in Corporate Finance

Topic

This research addresses limitations of traditional, static methods used by the Financial Planning & Analysis (FP&A) team, particularly in the context of market volatility and data complexity. The study focuses on integrating AI capabilities to improve forecasting accuracy and adaptability and aims to develop and validate an AI-driven tool for forecasting and scenario planning.

Relevance to practitioners

The study effectively demonstrates how AI-driven tools enable dynamic forecasting and scenario planning beyond traditional tool capabilities: the combination of XGBoost and Monte Carlo data processing, identifying complex data patterns, and real-time data from multiple data sources, which traditional tools often miss, to enhance forecasting accuracy. Crucially, the findings highlight the value of AI in predicting directional revenue trends and providing insightful trends. These directional trends are highly valuable for practitioners’ decision-making for resource allocation and risk management. The study contributes empirical evidence for evaluating forecast success based on directional trend beyond absolute values.

Results

The study successfully developed and validated an AI-driven model. Results demonstrate that XGBoost surpasses the traditional forecast tool like Prophet based on metrics: Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The XGBoost and Monte Carlo combination quantifies the impact of external factors on revenue forecasts in different scenarios. The study highlights the strength of the model in capturing directional movement of revenue trends.

Implications for practitioners

·       Develop or adopt AI-driven tools (like XGBoost) that move beyond static assumptions and enhance the accuracy of revenue forecasting.

·       Recognize the strength of AI-driven tools in predicting directional trends, improving resource allocation and risk management.

·       Incorporate with Explainable AI techniques such as LIME and SHAP to enhance model transparency and interpretation, removing the “black box” issue from stakeholders. AI systems need to be understandable and explainable for trust and adoption.

·       Imply the continuous loop for users and stakeholders to improve AI-driven models by integrating more data of revenue. Investing in data quality and availability is fundamental for reliable AI tools for FP&A.

Methods

The study employed a quantitative research methodology. Its data sources include historical company revenue, extracted from the company’s Power BI, and external economic factors such as GDP and interest rates. These data went through a data cleaning and standardization process - a multi-stage process using NumPy and Pandas frameworks. NumPy helped to optimize datasets and calculate means and standard deviation, while Pandas was used to remove data inconsistencies. The study uses an AI-driven tool, XGBoost, for revenue forecasting, which was combined with Monte Carlo for scenario planning. The study employed metrics such as MAE, RMSE, and R-squared to measure and evaluate the models.