some machine learning capabilities in its Analytics Cloud. IBM Watson Analytics, Amazon Quick- Sight, Microsoft Azure and Google’s Cloud platform also incorporate AI and it seems clear that those treasuries able to centralise the required datasets will be able to choose from a wide range of pro- viders able to intelligently crunch it and provide the forecasts they need. Most firms will simply rent these solutions from the cloud. In addition, companies like Microsoft are making their AI capabilities, for example in language processing and face recognition, available through APIs and they are linking with the largest open source AI platforms, such as H2O.ai, allowing other developers to add AI to their products. AI-enabled ERP solutions will combine intelligent data analytics, smart automa- tion, smart data gathering and sensor technology with deep learning, natural language processing and the technology to respond appropriately to changing situations in real-time. We are only at the beginning of this process, but companies like SAP are already deploying these technologies and corporates will need to re-organise all IT- and data-reliant pro- cesses to incorporate the changes. The impact on staff and organisational struc- ture is hard to overstate. Is the day volumes of invoices and to apply approv- That treasurers struggle with forecasting approaching where an ERP (or TMS) sys- als rules to them. Almost as a by-product is no surprise. Accurate forecasting tem will refuse a treasurer’s instructions of those processes, these systems aggre- requires the intelligent evaluation of a on the grounds that they are sub-optimal? gate large volumes of data that, properly large number of internal and external The impact of bank AI on treasury analysed, can be used by treasury, pro- variables, the weighting of those varia- curement or business units to ensure the bles, comparison with historical pat- It is clear that the incorporation of AI into supply chain is as efficient as possible. terns, the incorporation of real-time data existing and new treasury technology will Similar solutions are available for T&E from the business, procurement and else- have an increasingly profound effect on processing and monitoring. where and a view on how good business the systems, status and staffing of corpo- units themselves are at understanding rate treasuries. But it may be the use of AI All these developments are recognisably their situation. At even a small company, by their providers of financial products a linear continuation of the long-time this process involves far more data points and services that has the greatest impact. treasury drive for process efficiency and than a human, even one equipped with productivity: cost cutting to you and me. Excel or even a good ERP system, can Transaction, risk and asset management As well as forming the backbone of inter- accurately model. are, along with credit provision, the core nal treasury, these AI-based solutions products treasurers need from their will transform next-generation shared Treasurers ought not to feel too bad – it banks. So how will banks’ adoption of service centres. turns out, across hundreds of studies artificial intelligence change the world across a wide range of sectors, that a for corporate treasury? Where AI gets more interesting is in its small number of fairly statistical promise to revolutionise strategic and algorithms, applied to past data, almost Artificial intelligence may well create value-added treasury functions. always outperform even the most quali- interesting new money management fied humans. tools, hedging systems and transaction Forecasting and ERP management services, but by far the most What have treasurers put at or near the top The latest solution is AI-based ERP important implication of AI for any bank systems which promise to optimise customer is how it will transform their of their list of challenges for the last decade? ability to derive insights from their data. Forecasting – cash forecasting, forecasting operational models and transform for risk management, forecasting underly- business operations. So, for example, as We have already seen in the retail market ing business variables from customer announced in mid-February, SAP’s that AI-driven bots can act as financial orders, to supplier payments to procure- S/4/HANA Cloud ERP product now advisers, matching customers to appro- ment need and to inventory levels. incorporates predictive analytics and priate loan, credit card and investment 12 // TREASURY PERSPECTIVES 2017/2018 www.eurofinance.com