The UK government’s consultation on audit and corporate governance reform, proposes a major overhaul for company directors and auditors in the wake of big name company collapses. With the public consultation now closed, the government hopes to publish its response by mid-October.
The far-reaching consultation addressed almost all recommendations outlined in the Brydon, Kingman and Competition and Market Authority (CMA) reviews, proposing wide-ranging reforms to corporate governance, audit standards and competition.
One recommendation in the consultation was to require the Big Four to share their audit technology with smaller challenger firms. I was pleased to see that the consultation does not recommend the licensing of technology across the sector.
New entrants to the market of audit analytics are already few and far between and so adding additional licencing regimes would almost certainly stifle innovation. Tech would not always translate perfectly from a Big four firm to a medium sized firm in any case, and would certainly not help improve the quality of an audit.
Shared audit represents an interesting new market for challenger firms, but they will only be able to access this opportunity if they improve their audit analytics capability. Our experience shows that younger people joining the profession prefer an analytics-led audit approach and therefore embedding AI will future-proof firms’ access to talent too. Moreover, the audit profession must find ways to attract new software devlopers, , satisfy our critics and prove that we are ready to embed AI and analytics in our audit methodologies to restore its reputation.
Bridging the skills gap
Firstly, we must be ready to bridge the skills gap. As AI and analytics become more prevalent it is important that we equip our auditors with the right skills. At Moore Kingston Smith, we decided to take the plunge five years ago and teach our trainees how to code; now we are seeing new technology roles start to become embedded in audit teams across the sector.
It’s not easy to achieve, but what we don’t want to do is create new data analytic teams, separate from the audit teams. It is simply not good enough to send a data set off to a team of data scientists to run their analysis, as they need to have that deep understanding of the client and the industry in which it operates in to draw meaningful conclusions. Context is so vital for data analysis – analysts must truly understand what “normal” looks like for that business – which is why I want to see the skill set of auditors improved rather than farmed out to other teams.
Tech skills alone are not sufficient for AI/analytics to be truly effective. The tools will need to become “sector specialists” in the same way that human auditors are, as this industry knowledge is vital when trying to spot anomalies in a data set.
Nuanced skills in sector knowledge are important too. You can have two clients in the same sector with the same turnover and the same profit and on the face it of it they look very similar, but they in fact are wildly different businesses. If we apply the same analytics to the two businesses we would get very different results.
By way of example, when we first adopted Mindbridge as our audit analytics platform of choice we noted that the standard 30 control points were not particularly suited to not–for-profit entities as the way income performs in a charity is very different to a trading business.
So we worked with them to develop their features and control points, to ensure that the analytics would be just as effective for a sector that is important to us.
We have also collaborated with Oxford Brookes University on a 100 page toolkit for professional services firms to help them get ready to embrace and embed AI. The central aim of the toolkit is to promote the AI readiness of professional services firms by considering plausible future scenarios, identifying opportunities for business model innovation and planning for change.
The value of analytics
As a profession and including our regulators, we must up the ante on making analytics a mandatory part of the audit process. Not only does analytics help create a better quality audit, its value is proven too in terms of flagging other business issues to clients.
Analytics allows the effective screening of all transactions, rather than picking representative samples to analyse. In this way, with the full data set, it can help to reveal patterns.
New technologies can drive efficiency and productivity, especially by leveraging the potential of data.
In one recent audit where we deployed data analytics tools, we found a whole lot of irregular transactions being posted at the weekend. It turned out the transactions were not fraudulent, but due to an overworked team catching up by posting transactions on a Saturday and Sunday. The client was able to fix their staffing issues and was left with a much happier finance team as a result. We might not have spotted that with a random sample of representative transactions without the use of AI.
So I think we owe it to our clients to use the technology to improve our audit services as well as strengthen our relationships with clients by being able to spot issues and trends in their businesses.
Creating a more attractive market
Going back to the reviews, it was particularly disappointing that Kingman and the CMA concluded that challenger firms might need assistance from the Big Four when it comes to technology adoption – as clearly that is not going to give developers confidence in terms of creating new software.
I believe some of the challenger firms are actually very well placed in this regard. With rich sector specialisms and a homogenous group of clients, some will be able to deploy highly effective analytics and are already very used to working closely with their clients in this way.
According to a report by the Financial Reporting Council, the total number of registered audit firms was 5,394 as of December 31, 2018 compared to 5,660 and 6,010 registered firms as at December 31, 2017 and 2016 respectively.
It’s clear the market for audit analytics in the UK is already finite, shrinking and, therefore, very competitive for developers.
Accountants have become a bit lazy in their software adoption. We’ve allowed the market to be dominated by a handful of players which has stifled innovation and it is not currently attractive for new developers.
But we need to create a bigger, more attractive market, against the odds.
As a profession we need to work together to shout about good technological practice and good use cases. We need to stimulate the ecosystem so that we see new AI platforms being created and deployed as this is key to increasing quality in audit, adding value to our clients and ultimately winning back trust.