Continuous accounting and AI: What to expect in the next decade

This is a guest post by Aaron Harris, the CTO at Sage.com.

The next ten years will be pivotal for the finance function. Where once CFOs and their teams fulfilled the role of company historians, processing cash flow over a specific period to create a picture of past – or at a stretch, current – company performance, they’re now increasingly taking a strategic decision-making role. This is largely thanks to the vast amounts of data companies now have to work with, and that the responsibility for analyzing and interpreting this data is frequently falling at the feet of the CFO.

But what’s causing this change, and how can you take advantage? Here, I’ll look at the challenges finance leaders face today, and the future technologies that could address these and transform the way the finance teams of tomorrow work.

The challenges today’s finance leaders face

Today’s finance leaders want more time to focus on strategic work. They want to empower their teams with better technology to automate repetitive, routine tasks so they can shift their team’s view from looking at the past to looking at the future.

Accounting and finance solutions should embed powerful analytic capabilities, enabling interactive exploration in the system of record. However, that’s not enough. They should also offer “active insights” to finance leaders. “Active” means the solutions push the insights when they’re discovered so leaders can make decisions.

These insights shouldn’t necessarily be answers to questions that are well-defined. Ultimately, we can use modern technology to continuously scan business activity, looking for emerging opportunities and risks finance leaders may not have thought to explore.

Continuous accounting and how it will affect the world of finance

As the value of new technological developments becomes apparent, there will be more discussion about how innovation can transform the accounting industry. This will get CFOs and finance leaders and their teams out of these periodic, redundant, frankly low-value processes, and focused on the future.

Imagine a world where the books are always ready for reporting, where you always have real-time, complete, up-to-date information about the performance of your business. If you can achieve that nirvana of continuous accounting, you can see what’s going on in your business.

Continuous accounting isn’t valuable without confidence that the data’s gone through some assurance processes, that anomalies have been identified, and that exceptions have been reviewed.  That’s why we’ll see machine learning and AI technology become more and more prevalent as a way to test the accuracy of the data you use for reporting.

Getting to this future requires the power of multi-tenant cloud computing. The next step is harnessing that power to develop new reporting capabilities that are designed based on artificial intelligence (AI) and machine learning.

6 ways that AI could affect the future of finance

With the potential that AI offers, it’s important to start thinking now about how your organization can use it to gain a competitive edge. It’s also important to start thinking about regulatory and data privacy issues that it could throw up, and how your organization is going to navigate those.

The six future use cases outlined below – from my experience likely to be the most prevalent – will help give CFOs more time to analyze the data that’s available and suggest a strategic course of action:

1. Continuous analytics and performance monitoring: With all that rich data flowing through the system in real time, we will be able to analyze that data continuously with powerful computing capability to develop data models.
2. Anomaly detection: Your accounting solution will be able to find anomalies in real time, and alert you from that sea of thousands of transactions which ones probably warrant a human going in and looking at what could be inaccurate, irregular or fraudulent.
3. Continuous security monitoring: In the same way that you’ll be able to continuously monitor the business to find anomalies in the data, also it might also be possible to look for anomalies in activity that may be evidence of a malicious actor trying to gain unauthorized access, or perhaps an employee trying to do something irregular.
4. Adaptable user interfaces: User interfaces could adapt automatically to how the user likes to work. It becomes a much more dynamic, reactive user experience as the system learn more about the behaviors of the user.
5. Process automation: Machine learning could automate the long tale of manual activity that still happens within your teams.
6. Conversational AI and bots: In some cases, employees refuse to log into the accounting system to complete simple tasks like purchase approvals. Conversational AI will allow these individuals to complete those tasks using the tools they already know and love, like corporate communications platforms, digital assistants, etc.

It goes without saying that all use of AI should be transparent and meet customers’ demands for privacy – for example by ensuring that all data is anonymized. For AI to really work, rather than just being a ticking time bomb of bad customer experience and loss of trust, you might need to realign your organization’s ethos on the topic, and ensure you have a solid framework in place to deal with any issues that could arise.

AI and peer groups: how it could transform how you work

With the amount and quality of data AI-enabled systems will generate, you could develop powerful collective intelligence capabilities.  This means that if you’re part of a peer group, there’s a couple of hundred other companies similar to yours, generating the same sort of data as you do. This is going to make it significantly easier for you to compare how you are performing against your competitors.

What will be really powerful though, is when you can actually analyze all that information among your peer groups and develop the relationships that drive performance.

Your accounting software provider would be able to analyze data across different segments and offer recommendations based on this – for example, businesses that have greater value renewals tend to have shorter cash collection cycles. If you want to drive high value renewals, then you need to focus on your cash collections, because that’s what we can see in the performance across your peer group, and where you can improve your overall performance.

AI and risk – what you need to know

Artificial Intelligence projects can fail for all the same reasons that any other software project fails, though the risk profile for AI projects tends to be higher. Broadly, these reasons can be divided into six categories:

• Technology
• Market
• Team
• Legal
• Financial
• Business model

However, AI projects have an added layer of risk that is often underestimated, especially among executives that aren’t educated in AI. When building an AI service, it’s mostly unknown whether it’s at all possible to create the desired output from the available data set. The signs of failure here are clear: there is no signal in the data, model accuracy is low, the delivered output isn’t as good as the chosen benchmark, etc.

In the Sage AI Labs, the way we mitigate the above risks is by running a disciplined agile process, combined with a highly cross-functional team that’s in constant contact with customers and experts to validate outputs. This can only work well if all the basics have been taken care off, such as seamless data access, security and data governance, and a frictionless self-service “you own it, you run it” platform.

There will be so many great things to come from AI as the technology develops – the 2020s will be an incredibly exciting time to be a part of this industry.

This post originally published on the Sage Blog.