Bringing Finance Teams into the Digital Future Is a Hard-Won Battle

By Matthew Debbage, CEO of the Americas and Asia, Creditsafe

Digital transformation has been a big buzzword and priority in businesses for the last few years. And rightfully so. We live in a digital world where technology and data are commodities. Research has shown that AI has delivered massive benefits for businesses – from yielding more accurate business models and improving competitive advantage to increasing annual revenue.

While most finance professionals are wizards at using Excel to manage cash flow, invoicing and collections, they’re still behind the digital transformation curve. According to a new Creditsafe study, 29% of finance managers have little to no digital skills and aren’t comfortable with AI. And with only 14% citing expert digital skills, getting finance teams to embrace AI and automation won’t be that easy. So, let’s explore a few ways to bring finance teams into the digital future and the benefits companies will see as a result.  

AI is a boon, not a nuisance, to financial operations 

When most people think of customer onboarding, they typically think of what happens after a contract has been signed. But onboarding starts before the contract has been signed. It’s at that stage when the sales team has worked on a deal and has now brought it to the finance team to review and make sure the business would be a good fit as a customer. By ‘good fit,’ I mean that the customer has good financials, a strong enough cash flow to pay its bills in full and on time and has a low risk of becoming a liability. 

But running a B2B credit check is just one part of the customer onboarding process, albeit a crucial one. Customer onboarding should include the following processes if you want to fully protect your business from financial, legal and compliance risks. 

• B2B credit checks
• KYC / ID verification
• Anti-money laundering checks
• Compliance checks

All in all, running all these checks can be a lengthy and arduous process. It can also involve multiple people (sometimes up to 10) to coordinate and cross-check everything. Imagine how much time it could take if you do it all manually. It would take weeks, maybe even months. Do you think a potential customer will be willing to wait that long to get the contract signed? I don’t think so. So, now you’ve turned off a potential customer and lost the revenue that would come from them (likely for a few years). 

The great thing about AI is that it both accelerates and improves the accuracy of these checks, which is what both finance teams and potential customers want. For finance teams, that means there’s less of a chance that financial, legal and compliance risks are missed, meaning the company’s cash flow and reputation are both protected. That’s a crucial part of the finance team’s job – so it’s going to help them do their jobs more easily and more effectively. 

Manual credit decision processes lead to errors and skewed analysis

We surveyed finance managers to get a better picture of their credit decision process. Here are some insights into what we found:

• 97% of finance managers process up to 100 credit applications a day – that comes to 500 applications a week.

• Several people are involved in the credit decision process. For 63% of businesses, it takes up to 5 people to make credit decisions on new customers. Meanwhile, 22% of companies involve 6-10 people in the process and 14% of companies involve over 10 people. 

• 75% of finance managers take up to a full day (8 hours) to reach a credit decision on a single customer. Plus, 16% take one to two days to reach a decision and 10% take over three days.

These findings highlight a few of the problems that can arise from using a manual credit decisioning process. For one, it makes the credit decision process – from start to finish – excessively long and complex. And because multiple people (up to 10) are involved, there’s certain to be overlaps, mistakes and inaccurate analysis as a result. 

Another issue will be that the process itself is inconsistent, meaning that businesses could open themselves up to accusations of playing favorites and agreeing to work with certain customers and suppliers over others. On top of that, these inconsistencies can make it tough to scale the onboarding process, especially if a company is running over 500 credit checks in a day. That’s going to add up and become a huge burden, which is only going to become more complicated and riddled with more errors if it’s all done manually. 

AI makes it possible to build workflows based on a company’s credit policy and automate the credit decision process. So, finance teams can set certain parameters based on their credit policy (i.e. if DBT reaches a certain threshold) and then automate credit decisions based on that rule. 

Of course, I’m not saying that AI will fully automate the credit decision process for every application finance teams get. While it will do so for the applications that fall into the ‘easy approval’ category, finance teams will still need to be involved in the applications that aren’t as cut-and-dry and require further analysis. This is a good thing because it means finance teams can focus their attention on onboarding customers quickly and effectively, while also saving hundreds (even thousands) of hours and being more productive. But more importantly, they can detect financial risks more easily and more effectively – reducing their company’s overall risk and maximizing growth. 

Including finance teams in digital transformation strategies is a win-win for efficiency and revenue growth

Given how big of a priority digital transformation is in businesses right now, you’d think the same rigor would be given to digitally transforming financial processes. But that doesn’t seem to be the case.

The Creditsafe study found that a significant portion of companies (39%) aren’t investing as much in digitally transforming financial processes compared to overall digital transformation. This gap in investment priorities is likely due to the ongoing ‘bean counter’ stereotype of finance professionals and the fact that they’re usually seen as ‘blockers.’ This needs to change if businesses want to position themselves for long-term growth, especially amid the looming recession.

A key reason this is happening is that, in most cases, the CFO is having higher-level conversations with the executive team and board of directors about the company’s business and growth strategy. And since they aren’t typically involved in the day-to-day running of finance, they can’t identify what processes are working well and what processes are in desperate need of AI and automation. Unfortunately, this incomplete view and understanding of the day-to-day financial processes plays a part in how much CFOs push for increased investment in AI and automation in finance. 

Another reason is that many CFOs are baby boomers – a generation that grew up with fax machines and answering machines. While they may have learned to use digital to some extent in their roles, they’re still nowhere near as digitally savvy or AI-aware as the younger workforce of Millennials and Gen Z. So, what they understand about the role and value of AI in finance is limited. And when you don’t know much about something, you’re not going to be as likely to advocate for investing in AI and automation to improve financial processes. 

The only way things will change is if we shine the spotlight on why including finance teams in digital transformation and innovation strategy discussions. By doing so, businesses will get:

• A real-time view into financial, legal and compliance risks 
• Better visibility and management of cash flow 
• Standardization of processes and policies, leading to more consistencies in decision-making
• Improvement in working capital
• Faster customer onboarding, leading to higher satisfaction and retention rates
• Faster, more accurate processing of invoices and payment collections
• Reduced likelihood of fraud, compliance violations and government fines

CFOs must prioritize and invest in digital upskilling for finance teams

It’s quite common for finance teams to have limited budgets and a digital skills gap compared to other departments. But this can often create more problems when it comes to cracking the finance talent code. As the Creditsafe study found, 29% of businesses said not having enough budget and resources was the biggest challenge they faced with recruiting and retaining finance talent. Plus, 19% believe insufficient digital skills and lack of experience with finance software make it hard to build strong finance teams.

If companies want to see more of their finance teams embracing AI and automation, then they need to invest in upskilling the employees they already have. But this is where a lot of companies fail because they focus on short-term results and then forget about it a few months later. Upskilling needs to be a strategic initiative that starts with analysis of the current finance team. 

As part of this audit process, finance leaders should be asking each member of their team the following questions:

• What do you think AI is and what does it do?
• What are your concerns about how AI and automation could affect your performance and job stability? 
• Have you ever used any type of tools for automating financial processes? If so, what was your experience like? Did it feel weird and uncomfortable? Did it feel satisfying when you could ditch repetitive tasks? 
• How would you like to cut back on the amount of time you spend on monotonous admin tasks and instead contribute more to the company’s financial strategy? 
• Would you be more open to using AI and automation if ongoing training and workshops were provided throughout the year?
• What if you could use AI to improve the accuracy of your analysis and make better decisions?  

Once finance leaders have asked these questions, it’s about getting a clear picture of the digital skills, savviness and readiness of your existing team and identifying what levels and types of training will be most useful to properly upskill them. This is something a lot of businesses skip – mostly because they’re going into it with assumptions of what their team knows and doesn’t know. That’s not going to help the situation. It’s the finance leader’s job to do their own audit and analysis of the team before they decide to design and implement any training program. 

For example, if a few members of a finance team lack digital skills beyond using Excel sheets, this will indicate that this group needs basic training to start. But if other members of the team have used some digital tools and platforms to automate AR and AP, for instance, then the finance leader would likely want to set up mid-level training for these employees that dives deeper into those tools to make sure they’re getting the most out of them and automating the AR/AP processes in the best way possible. 

The key takeaway here is that businesses can’t just use a one-size-fits-all upskilling program for their finance teams. Each person’s technical skills, comfort level and willingness will vary. So, businesses will need to customize their upskilling program based on these factors.