For businesses, adequate cash flow is vital to the smooth running of the organization, yet a lack of visibility and control of spending can impede this. This lack of visibility is felt by 40% of U.S. SMEs who say they don’t have visibility of spend on a day-to-day basis, while 22% of SMEs have to regularly spend significant time and money investigating who spent what.
However, thanks to banks increasingly adopting new technologies that make use of artificial intelligence (AI) and machine learning (ML), these struggles could soon be a thing of the past. With banks using AI and ML to build up a better picture of their business customers and improve the service on offer, enterprises are set to see substantial benefits, particularly in three areas.
As banks make better use of AI for fraud detection, U.S. businesses will benefit from improved security features. In this use case, AI will help businesses keep their accounts safe by detecting any fraudulent activity and anomalies in their accounts much quicker than previously possible. This works by the model having an understanding of what is ‘normal’ for each account or card and recognizing patterns based on past transactions and behaviors. For example, if 99% of the transactions for one account happen Monday through Friday, a transaction that occurs over the weekend will be seen as abnormal and flagged as such.
Of course, anomalous transactions aren’t always fraud. Often they’re just out of the ordinary, requiring some more investigation—flagging them to the business would certainly allow for this. With companies currently losing an average of 7% of their annual expenditure to fraud, these technologies will help lower incidences of fraud as shown by Visa’s use of AI reducing global fraud rates to less than 0.1%. In the future, AI could be used to detect fraud in real-time, stopping fraudulent transactions from being processed altogether.
AI will allow banks to more accurately forecast how much credit businesses require and limits on spending will be set automatically, enabling businesses to gain a better understanding of their spending. This can then be implemented within the organization as it will enable businesses to redistribute credit limits based on what different employees regularly spend. This means that credit will be allocated in an optimal way, ensuring the amount of credit employees are given reflects their spend history. This ensures that those employees who often make large transactions are given the credit to do so, while those who use their company accounts for lower-cost transactions don’t receive as much, therefore ensuring that credit is being used to the greatest effect.
In addition to providing enterprises with a greater degree of control and understanding of their finances, banks are also beginning to use AI to offer businesses extra tools and services. A prime example of this is expense management systems which use AI to simplify the expense process and reduce the amount of time employees and finance departments spend on such tasks. As with fraud detection, the system would establish patterns based on the employees historic spending behavior. For example, it may pick up that once a week the sum of $10 is spent in a coffee shop, which the user then applies a particular expense code to. Once this behavior has been demonstrated enough times, it becomes a pattern. So, the user will no longer have to code the transaction themselves, the system would automatically identify the type of expense it is and code it correctly.
As the system establishes more patterns and understands what the user or business is doing, smart coding could start to be applied to a greater number of transactions. This would significantly reduce the amount of time spent manually sorting through and coding expenses as the employee then only has to check that the correct codes have been applied.
Through increased use of AI and ML, banks will begin to build a fuller and more accurate idea of their business customers. While this will no doubt be useful for banks in terms of being able to target customers more closely with new products, it will be of significant value to companies. Thanks to these technologies, businesses will gain a greater level of control over their accounts and spend, improved visibility on a day to day basis and a better, more accurate understanding of their finances. In time, this will also reward businesses in other ways, allowing their employees to spend less time manually interrogating accounts and focus more on bigger picture and value-adding tasks.
David Duan is data science stream lead and principal data scientist at Fraedom. The second part of AFP’s Executive Guide on Emerging Technologies, underwritten by Kyriba, will focus on artificial intelligence and machine learning and is due to be released in November. Download the first part, on robotic process automation, here.