SmartStream Technologies has completed a proof of concept for an artificial intelligence (AI) and machine learning module within its existing TLM Cash and Liquidity Management solution for receipts and payments.
Technology that meets the market demand for forecasting liquidity has been the backbone of SmartStream’s intraday liquidity management solution. The next phase of the solution’s development is about predicting the settlement of cash flows. The company has been working on a proof of concept with its clients for profiling and predicted intraday settlement activity, which includes missed payments and receipts identification planned for settlement within current date. Cash management teams should gain greater visibility into the payment process and manage liquidity risk more efficiently, minimising the potential of payments being missed.
“This proof of concept is clearly another important step towards ensuring that our clients are keeping pace with what the regulators are demanding - and in particular the questioning of a bank’s position and the management of its outstanding balances," said Andreas Burner, chief innovation officer at SmartStream. "By combining our recent achievements in AI with SmartStream’s many years of experience in this area, the Vienna-based Innovation Lab developed this new AI cash and liquidity prediction module. The technology continuously learns data patterns so the service continues to improve and become more efficient.”
The new TLM Cash and Liquidity Management, AI and machine learning module has the ability to predict when credit is going to arrive; giving bank treasurers more control over cash-flows. The proprietary algorithm uses the data and predicts the forecasted settlement time of receipts on an intraday basis. The core of the module is underpinned by machine learning technology that continuously improves, meaning the predictions become more accurate and treasurers can make more informed decisions.
AI and machine learning provide the banks with the opportunity to look at reducing the liquidity buffer. The rigorous analysis of unstructured data and learned settlement predictions reduces costs. It also offers another tool that can be used to mitigate the impact of reputational risk as it relates to the ability to meet payment obligations by allowing greater visibility into exposure limits with predicted forecasting. The solution's user interface enables users to drill down into individual cash-flows.
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