By Manish Singh 09.05.18

For decades, the world of corporate financial systems has been black and white. Auditors’ calculations on Excel spreadsheets were crisp, exacting, and discrete. The increasing problem is that much of corporate spending falls into a gray area.

We can put things in black-and-white buckets. But, at times we need to interpret those numbers. But this is not something traditional transactional systems can do.

On the other hand, artificial intelligence (AI) systems were built to analyze and interpret data. AI replicates human logic, meaning it can look at data and decide what it means. Then, it can make a judgment call. Unlike humans, AI can work consistently and tirelessly.

Human decision-making can be more nuanced than a computer program. But a robo-auditor can apply its judgments all day long against mountains of detail.

A human brain asked to repeatedly do the same thing would tire and grow numb. But a software program never gets bored or tired. It is constant, dependable, repeatable, and scalable.

The pitfalls of preventive controls
One trap that some companies fall into is they create a bureaucratic corporate financial system that restricts business activity. Most systems have preventive controls that allow you to determine what you want and what you don’t want to happen.

These preventive controls include a field that’s only supposed to have numbers, so it won’t let you type in letters. Another example is when someone is authorized to spend up to $10,000, they can’t put $10,001 into the purchase order (PO) field.

It’s impossible to write a preventive control rule for every contingency. When you layer rules on top of each other to try and handle all the potential risks, you get a brittle system that makes it impossible for a company to be efficient.

Think back a few years to what it was like to buy a cellphone and sign up for service. The process was laborious and slow. A sales representative had to fill out several forms and ask you dozens of questions. The process was designed to help you the right plan and prevent fraud, but it was brutally inefficient.

Today, the process is much smoother and faster. The wireless carriers have automated their controls to the point where the process takes just a few moments. This kind of automation not only saves customers time, but it also makes it easier for the carriers to sign up new customers and cut wait times dramatically.

Efficient customer interactions are good for business and show how automation can replace restrictive practices that slow down business and irritate customers. Without an AI system and a robo-auditor, detective controls require a person to audit transactions to find problems in the gray areas. When your company has a high volume of transactions, this means the best you can do is sample audits.

Avoiding positive confirmation bias
But sample audits are expensive and inefficient. They don’t happen frequently enough and when they do, human auditors can fall victim to positive confirmation bias. This happens when data is correct often enough that people begin to assume everything will be correct.

But a robo-auditor does this quickly and accurately. Its mind never wanders.A robo-auditor allows you to institute superior detective controls that let employees work in a system that isn’t overly bureaucratic. This makes your business more flexible.

When you have a system that is constantly monitoring for outlier risks, you can feel comfortable instituting more relaxed preventive measures. For example, you can increase purchase-card limits and enjoy the greater rebates that result.

That’s not to say AI replaces human auditors within the world of corporate finance.

By offloading much of the heavy lifting onto the AI system, human auditors can focus on making decisions based on the data that a robo-auditor presents. Humans don’t need to find the needles in the haystack, but rather interpret what those needles mean.

In a world that’s becoming more gray, human decision making still reigns supreme. But AI can help corporate finance teams analyze data and make decisions more efficiently.  

This is the fourth blog in an ongoing series based on the recently released book, Robo Auditing: Using Artificial Intelligence to Optimize Corporate Finance Processes by Patrick J.D. Taylor, Manish Singh and Nathanael L’Heureux.

See Related Blog Posts: Artificial Intelligence

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