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CEO of tech start-up talks AI in finance

Shamus Rae, CEO of tech start-up Engine B, and Kirstin Gillon, Technical Manager in ICAEW’s IT Faculty, consider the progress made by AI within finance.

Kirstin Gillon (KG): We often characterise the use of AI across the profession as in early stages. Can you give some specific examples of how AI is being used to deliver more value to clients?

Shamus Rae (SR): I look at AI hitting the profession in three stages. Stage one is the different focus on play. The second stage is about process efficiency, and the third one is driving business model change and the service delivery of a specific service like audit. Different firms are at different stages of using AI. But now we’re seeing people bringing AI systems into the operational efficiency stage. Some examples include AI being used for better reading of receipts to work out what can be declared and what can’t from the tax point of view. AI has been used in general ledger flows to look for suspicious activity such as fraud and for use with mortgage books in bank audits to consider whether the risk profile allocated is appropriate. But progress is sporadic and not as consistent as it should be. The next wave will be as people start changing the model altogether.

KG: Where do you think AI can create the greatest value in the profession in the future?

SR: We should all be of the assumption that the basic numbers are going to be automated. AI will help clean up some of that unstructured data from invoices and tax receipts, then our ability to automate the transactional parts of audit and tax will happen. The ability for the professionals to focus in on the business, to look at risks in the supply chain for example, will be improved by AI. With audit, for example, you can look back and look forward and question whether the numbers add up. Audit in real time means accountants can start carrying out the risk analysis over the next two quarters to three years.

KG: It may sometimes feel AI is just for the firms and companies with deep pockets. What are the opportunities it presents for smaller businesses and practices?

SR: At Engine B we work with nine audit firms and the idea is to set some standards. In this way third-party companies or audit firms can share technology, and tech companies can build an AI solution. Most data is in poor condition and non-standard, therefore it costs a lot of money to clean up. But if we can sort out the data problem and open up the market, then there’s no reason a small tech start-up can’t produce something around say, audit disclosures, and deliver that against the standards, knowing it will work across the whole industry.
Small firms shouldn’t try and create AI solutions alone. They should either try to do it in conjunction with other firms or with a technology company. To create the AI tool for mortgage work I mentioned above costs over $15m.

KG: Much has been said about the loss of jobs to automation so, looking forward five or 10 years, what do you think accountancy jobs will look like and how can we sell the profession as a good career to go into?

SR: In five years’ time accountants are going to be people who will be looking at business models, ways of working and at business, as they are today. But they will be able to provide greater insight to both shareholders and management about where those risks are.
It’s not going to be about accounts payable and receivable or the general ledger flows. From a skills point of view, advisory and coaching ability is going to be important.

KG: Given the need for changes to skills, what are the main ones that accountants need in order to work with AI? And to what extent will they need to develop deep skills in technologies such as machine learning?

SR: If accountants can cross over to become data scientists that’s fantastic, but it’s not going to be the general rule. Accountants will need to understand the basics about how AI works. They don’t need to be data scientists but we’ll need industry people who have sufficient understanding of AI, how it trains, how it carries on training after it’s implemented and what the risks and weaknesses are.

KG: Ethics is a very hot topic around AI at the moment – does the profession need to do more to respond to concerns about the potential abuses of AI?

SR: In the short-term, providing assurance around the quality and governance of data is something that accountants are well placed to do. It’s going to be all about the ethics, values and bias in a system. AI is a living thing that carries on learning, so providing assurance on that is vital. There’s a big role to play for accountants. In the longer term, we need to have a proper ethics debate about AI in the round and not in the narrow.

KG: To what extent do you see accountancy as an innovative profession around AI, and what would help to support further innovation in the sector?

SR: Accountancy wants to be innovative but the problem is the firms’ business model, particularly the partnership model. AI will change the value proposition, the sales approach and the shape of the pyramid structure of partnership. To really drive innovation, organisations need to be agile. Those that change their business models first will have the potential to really grab market share. There’s a real opportunity for smaller organisations to be more agile and maybe work together to produce AI tools on a specific service line such as transfer pricing, for example, and compete with larger firms to sell the product globally.

KG: Are there any other sectors, or countries, doing particularly well with AI that accountants can learn from?

It’s clear that the smaller countries, in economic terms, are doing more interesting things with AI. If you look at Holland, Spain and Australia there are some really interesting things going on. In the US, the UK and Germany there’s lots of aspiration and lots and lots of money being spent, but they’re not moving as fast. Smaller countries are less culturally constrained by large organisational structures. If we look at sectors, financial services and investment banking are deploying AI systems in more and more ways to remove humans. And they’ve been doing it at a much faster pace than we are. I think that’s cultural.

We want to encourage wider debate about the long-term opportunities and challenges for the profession that AI poses. You might also like to read our other related articles: