Cash Management Software Using Machine Learning Forecasting

The patent segments cash management by creating separate cash pools for each currency, allowing independent management and optimization of cash flows in different currencies while maintaining overall centralized control. This reduces complexity by organizing the system into manageable currency-specific modules rather than attempting to manage all currencies in a single undifferentiated system.

The system dynamically adjusts cash management parameters such as cash balance targets, investment thresholds, and transfer amounts based on forecasted cash flows, exchange rates, and interest rates. This allows the system to adapt to changing conditions automatically, improving efficiency without requiring proportional increases in administrative oversight.

The system performs preliminary actions by collecting and preprocessing historical cash flow data, exchange rate data, and interest rate data before applying machine learning algorithms. This preparation work is done in advance to create clean, structured datasets, which simplifies the subsequent modeling process and reduces implementation complexity while maintaining high forecasting accuracy.

The patent introduces intermediate processing layers between raw data and machine learning models, including data cleaning, feature engineering, and model validation steps. These intermediary processes act as mediators that translate complex real-world financial data into formats suitable for machine learning algorithms, improving accuracy while managing implementation complexity through structured intermediate stages.

The system performs ARIMA analysis periodically at scheduled intervals (e.g., daily or weekly) rather than continuously processing all historical data in real-time. This periodic approach maintains accurate forecasting capability while significantly reducing computational burden and data processing time by analyzing data at regular intervals rather than continuously.

The patent applies partial action by focusing ARIMA analysis on the most relevant and recent historical data periods that have the greatest impact on future forecasts, rather than processing the entire historical dataset. This selective approach maintains forecasting accuracy by concentrating computational resources on the most influential data points while reducing overall processing time.