Connecting the Dots Between Automation and Risk Management

According to data from IBM, 35% of organizations reported that they were using AI technology in one way or another in their businesses. On top of that, 42% said they were looking into the benefits of AI and are considering incorporating AI into their business processes.

But for many finance professionals, it’s not always easy to connect the dots between AI and risk management. That’s what we want to explore in our blog. So, I chatted with credit risk and financial automation experts from Blackline, Quadient and Creditsafe to get their take on AI’s role in risk management. Here’s what they had to say.

We’ve talked about how finance teams are afraid of technology, AI and automation before. This fear won’t go away overnight. But it’s important to understand what’s driving those fears.

Danny Wheeler, Solutions Strategy Manager at Blackline, believes fear of AI and automation comes from businesses and teams going into automation projects without clear communication to the people who will see the job impacted by the change.

“Teams not knowing what the future state looks like and what their day to day will be can naturally lead to concerns for job preservation. It’s also based on the view that you’re ‘giving control to the software’ and losing that element of a human eyeball in the data collection and analysis process.

In reality, automation takes away the parts of the job that can be structured and easily repeated, which then lets teams go and focus on more value-added activities that they may not get the time to do ordinarily. But there’s so much more AI can do for finance teams.

AI will allow better predictive modelling for forecasting and identifying ideal customer profiles. Both are done today, but both require a herculean effort to collect, process, analyze and share the data. Better forecasting will allow finance teams to have a better grasp of what cash flow looks like, where there could be gaps and where they need to take action. Thanks to AI, you’ll get deeper analyses and insights from the data so you can properly understand your customer portfolio, their financial strengths and weaknesses and payment behaviors, which means finance teams can be far more proactive in mitigating risks.”

For Sarah-Jayne Martin, Director, ICA Global AR Practice, Quadient, a generational tech gap plays a key part in finance’s fears and hesitation to adopt AI and automation in their roles.

“Finance professionals tend to lean on the side of ‘trust but verify.’ Any finance professional reading this will likely be nodding their head. It’s obviously good practice to always make sure that accounting entries are accurate. And for some, the thought of a software platform or other solution automating those transactions can be slightly unnerving or even intimidating.

Second, we’ve all been through technology implementations where things didn’t go as planned and, in some cases, a solution has ended up causing more problems. So, there’s a healthy skepticism towards anything that removes control from accounting and finance managers. 

Lastly, according to a study by Zippia, the average age of an accountant in the U.S. is 43 years of age. While this doesn’t make us all dinosaurs, it does put us in the category of being slightly less technologically savvy. For most of us working in finance, we’ve been performing our job functions manually for many years. And change is frankly scary.

The benefits of automating these functions though definitely outweigh the concerns. Technology can reduce manual tasks and repetitive chores which in turn allows professionals to focus on more value-added or strategic initiatives. This leads to overall better employee satisfaction and reduces churn. In addition, leveraging technology can provide greater transparency into the finance function which allows teams to assess areas of their processes that can be improved, ultimately benefitting the bottom line.”

When most people think of customer onboarding, they typically think of what happens after a contract has been signed. But Matthew Debbage, CEO of the Americas and Asia for Creditsafe, thinks this is part of the bigger problem.

“Customer 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 and sanctions 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 were 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.”

We surveyed over 300 US 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.

I shared these findings with Matthew Debbage, CEO of the Americas and Asia for Creditsafe. Matthew believes these findings highlight a few of the problems that can arise from using a manual credit decisioning process.  

“For one, it can make 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 you could open yourself 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 your company’s credit policy and automate the credit decision process. So, you can set certain parameters based on your 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 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, finance teams can detect financial risks more easily and more effectively – reducing their company’s overall risk and maximizing growth.”

Danny Wheeler, Solutions Strategy Manager at Blackline, believes automation can be valuable in improving the quality of data and reducing the likelihood of inaccurate or skewed analysis.

“On top of increased efficiency and cost savings, AI also allows finance teams to get access to the latest data. And that isn’t always the case in a manual process. This means there are far fewer wasted activities due to data not being up to date and people having to do unnecessary work. One example of this is if a customer makes a payment but the finance team can’t apply it against the invoice and process it for a few days while they work through the pile of payments that need to be processed.

In that time, the collections team have probably chased the customer again for payment for the invoice or the account has been put on hold. Both cases result in wasted effort for the person chasing payment and a terrible experience for the customer, who may decide to take their business elsewhere as a result.

What’s more, automated systems can minimize human errors and inconsistencies in data processing, data analysis and decision-making. This improved accuracy can help mitigate credit risk and reduce financial losses. It also means that finance teams don’t waste time having to go back and re-do work. It also gives the business more confidence in the numbers they report on.

Automation allows for real-time data processing and analysis, allowing for faster credit risk assessments and decision-making. This speed can result in quicker responses to credit inquiries and customer issues (leading to an improved customer service as I mentioned earlier). And best of all, the business can see trends and patterns in real-time, meaning they can mitigate risk far better and make better decisions on where to focus their efforts based on the data provided.”

Sarah-Jayne Martin, Director, ICA Global AR Practice, Quadient, spends a lot of time working with businesses to unlock ROI from automation.

“Ultimately, automation is an investment that delivers real improvements to working capital and the bottom line. By automating the credit application and assessment function, companies can gain better insight into risk and mitigate it effectively. This results in fewer delinquent accounts and better credit line management, which reduces exposure to financial risks. By leveraging automation in the credit risk and customer onboarding process, finance teams can streamline the process and improve the experience for both internal stakeholders and prospective customers. That’s a win-win.”

Danny Wheeler, Solutions Strategy Manager at Blackline, has seen massive improvement in the finance function with the emergence of automated solutions.

“There’s better accuracy, better visibility and better experiences for internal stakeholders and external customers. There are now automated solutions for most finance functions, which have reduced paper driven or Excel-based processes.

Looking into the future, I think we’ll see more use of artificial intelligence and machine learning to further improve the function. I don’t believe the finance back office will ever be completely automated. But in the future, ideally human interaction and decision-making will only occur when needed, as opposed to at every touchpoint.”