Financial Forecasting Model

Project Overview

This project involved developing a financial forecasting model for a financial services company to predict revenue, expenses, and cash flow for the next 12 months.

Challenge

The client needed accurate financial forecasts to support strategic planning and investment decisions. Traditional forecasting methods were not capturing seasonal patterns and market trends effectively.

Solution

I developed a time series forecasting model using ARIMA, exponential smoothing, and machine learning techniques in R. The model incorporated external factors such as market indices and economic indicators to improve prediction accuracy.

Results

The forecasting model achieved 92% accuracy for 3-month predictions and 85% accuracy for 6-month predictions, significantly outperforming the client's previous forecasting methods. This improved accuracy helped the company optimize their cash reserves and investment strategy.

Key Visualizations

Revenue Forecast

Time series forecast with confidence intervals for revenue projections.

Expense Prediction Model

Comparison of actual vs. predicted expenses with error analysis.