This post was originally published on Fin Extra
How can AI be made tangible for payments? Are there practical use cases for AI, such as improving liquidity management and reducing manual intervention? How can AI be applied in production in the short term to enhance efficiency and reduce costs? How important is aligning data components and achieving higher straight-through processing (STP) rates? Why is it now time to move beyond the theoretical benefits of AI and focus on real-world applications?
We’ve only scratched the surface of AI and the technology’s potential. Despite AI and its subsets, namely machine learning, working its way into everyday processes within financial institutions, the sheer volume of data that organisations and individuals have amassed should be transformed into better decision-making at an organisational level. Beyond historical data, running new data through trained AI models can ensure more informed predictions, which are particularly useful for treasury departments.
AI models can analyse data and identify patterns in a fraction of the time that humans can, allowing for detailed cash forecasting, payment fraud detection, and working capital optimisation. By automating manual gathering, consolidation and formatting, payments can be processed at a much faster rate with higher levels of straight-through processing (STP) while learning from
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