For businesses, gaining visibility and control of spending is vital to maintaining adequate cash flow. However, currently, this is something many U.S. businesses, SMEs in particular, struggle with, as shown by the fact 82 percent of business failures are due to poor cash management. As banks begin to address this problem, we are seeing the adoption of more technologies that make use of artificial intelligence (AI) and machine learning (ML), with research from Fraedom finding that 40 percent of U.S. banks planned to invest in these technologies in last year. Consequently, U.S. businesses could soon benefit from a wider range of capabilities and tools that give them greater visibility of and control over their accounts in the following ways:
Businesses will benefit from greater account protections thanks to the use of AI for fraud detection. AI will help businesses keep their accounts safe by detecting any anomalies in their accounts and fraudulent activities much more quickly than previously possible. The beauty of using AI and machine learning in this way lies in their ability to understand what is “normal” for each account or card by recognising patterns based on past transactions and behaviors.
With AI capable of detecting any deviations from the normal patterns faster than currently possible, banks will be able to inform businesses if their accounts appear to have had unusual activity. Certainly, anomalous transactions aren’t always fraud; it may just mean that they’re out of the ordinary and need some more investigation, and flagging them to the business will allow for this. Being able to identify anomalies faster could be significant for U.S. businesses as it will allow them to pick up on any misuse of their accounts and deal with it immediately. In cases where fraud has occurred, businesses will be able to get to the root of the problem quickly, rather than finding out months down the line when the employee the transaction relates to may have moved on.
In the future, we may get to a point where fraud detection can be done in real-time in order to stop fraudulent transactions happening altogether. In these cases, we could see the account being frozen or the card being blocked in order to prevent the transaction from being completed. However, this is still some ways off from becoming a reality.
Control of Spend
As banks come to grips with continuous machine learning, they will be able to accurately forecast how much credit businesses require and limits on spending will be set automatically. This will provide businesses with a better understanding of their spending and help to prevent them overspending. It will also allow for credit limit redistribution based on historic spending patterns. For example, if an employee within an organization is consistently spending a certain amount, the bank will know that’s how much credit should be allocated to them. This gives banks a mathematical way of understanding the optimal way to provide credit.
This means that credit will be allocated in an optimal way, ensuring the amount of credit employees are given reflects their spend history. This guarantees 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, to ensure credit is being used to the greatest effect.
Banks can also use AI to create more personalized customer experiences and to ensure they offer their customers the products and services most likely to be of interest to or benefit them. In this respect, AI would recognize patterns in spending and use this to determine which loans or credit cards would be best for different business customers. AI will also be able to flag how the individual tends to interact with the banks; for example, whether they prefer to use online or telephone banking. This will then enable banks to use this information to personalize the way they communicate with customers, choosing the method that the individual is known to prefer and helping to streamline communications for businesses.
The use of AI and ML in banking will ultimately provide organizations with a greater level of control over their accounts, more streamlined processes and a better understanding of their finances. It will also offer improved visibility, helping to prevent instances of fraud and allow businesses to deal with suspected cases of fraud faster to come to a swifter resolution. As this is realized, employees will gradually spend less time manually interrogating accounts and instead be able to focus on more value-adding tasks.
David Duan is Data science stream lead and principal data scientist at Fraedom. Over the past 20 years, Fraedom has managed more than 1.5 billion transactions through its web-based platform. Supporting more than 100 commercial issuing banks, more than 600,000 organizations benefit from Fraedom’s technology, managing transactions for more than 7 million employees worldwide. The company has offices in the UK, U.S., Canada, Australia and New Zealand. Fraedom is a wholly owned subsidiary of Visa.