With ML, treasurers need to be vigilant in an equivalent way. This is especially true because ML may develop more complex data relationships that can be difficult for treasurers to understand – the so called ‘black box’ or ‘explainability’ problem. So it is all the more important to be vigilant about the potential limitations of the dataset being used. For example does it cover a full economic cycle?
The year 2020 has provided a good example of this problem. For instance, forecasts built from two or three year old datasets will not have captured the turmoil caused by COVID-19. This is analogous to how the Asian currency crisis at the end of the last century ‘broke’ many VaR metrics.
Most treasurers will have read that ‘data is the new oil’. This allusion is based on the importance of data to ‘feed’ AI in order to get the desired results. This is as true of cash flows as it is of clicks and visits that social media use to maximise their manipulation of our attention.
The answers, suggestions, and warnings that AI generates will at best be as good as the data that it is being fed with. Treasurers with effective data solutions, often data warehouses or lakes or oceans, have a big advantage in availing the benefits of AI. Although there are some AI technologies that handle unstructured data, and natural language processing may seem to offer an easy way to cut through data clutter, in practice treasury AI benefits from clean and well-structured raw data.
So treasury AI projects often start with collecting and cleaning underlying data. This can be a large project since, to obtain the most useful results, treasurers will often need to feed their AI with non-treasury data – for example, combining cash flows with accounts receivable and accounts payable data and even sales and production planning.
As alluded to above, there is an issue with explaining AI’s forecasts and suggestions and, where relevant, decisions. AI will comb through datasets and find relationships in the data that are invisible to human observers and, even when exposed, do not make sense to humans. AI is playing with 1s and 0s – it has no common sense. On the other hand it has no preconceptions. When DeepMind’s AlphaGo beat Lee Sedol in March 2016, human experts had great difficulty fathoming that AlphaGo had not simply played with discipline but had in fact developed completely new strategies never considered by thousands of generations of go masters.
Managers will quite naturally feel reluctant to follow tactics or strategies that they do not understand. To some extent top management uses their understanding of subordinate experts to become comfortable with decisions they do not personally understand. But with AI there can often be no explanation, and there is no silver-tongued expert to get management on board.
This issue is such a big problem for the AI industry that it has spawned a new sub industry –eXplainable Artificial Intelligence (XAI) refers to a set of techniques that help show how a ML algorithm comes up with predictions and suggestions. XAI potentially enables humans to better understand (and trust) outputs from complex, ‘black box’ models and helps decision makers know the relevant data characteristics driving the output.
Perhaps most salient for treasurers is that this issue is another reason why the question is not how to replace analysts with AI but rather to find practical ways to augment analysts with AI. Treasurers will need all their domain knowledge more than ever, and they will have to leverage this with understanding what the AI is doing. AI is a new (albeit tireless) team member, and must be understood and managed like the human team members.
Bias is often discussed as a problem with AI. Training data may be skewed, especially when dealing with human issues. The conceptual equivalent for treasury would revolve around changes in historical patterns like the COVID-19 impact mentioned above, and to the potential for business model changes to trip up AIs honed as-is or business as usual (BAU) data.
Treasurers need to be vigilant when new product and or market segments are introduced because AI is definitely not clairvoyant. The AI will proceed on a BAU assumption and will often need to be re-trained. This is a challenge for new products and markets because there is no historical data with which to train the AI. Layering human assumptions will at best reduce AI’s ability to bring new light to issues. Even mixing up data from previous changes may not give the correct results – after all humans are also heading into uncharted territory.
There is no easy fix for this kind of situation. In the end it comes back to the collaborative AI concept – treasurers need to understand the limitations of their AI team members and manage them accordingly.
AI is tireless and fast, and also devoid of common sense. Treasurers need to get a feel for how to use AI productively, just as they need to understand the strengths and weaknesses of their human team members. AI is probably best thought of as a tireless ‘gopher’ assisting human treasury staff. And any attempt to enhance treasury with AI must start with clean and complete data.