SmartPredictions uses AI to train and test business’ historical liquidity data to accurately predict and forecast future transactions, according to the official press release. With an intuitive interface, treasurers can indicate which of their datasets to include in a given forecast.
They can also customise how much historical data to include, as well as how far into the future to request forecast data. SmartPredictions will algorithmically select the optimal model for each dataset input – based on predicted accuracy – from a variety of machine learning models as well as a traditional projection method that is well-suited for time-series data problems.
Two of the machine learning models are at the core of GTreasury’s new SmartPredictions functionality: Decision Tree Regressor (gradient boosted framework) and Singular Spectrum Analysis.
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