For businesses, gaining visibility and control of spending is vital to maintaining adequate cash flow. However, in recent research, we found that almost a third of UK SMEs claimed to have a clear picture of business spend at the end of each month but little visibility on a day-to-day basis. To remedy these issues, we are now seeing banks begin to adopt more technologies that make use of artificial intelligence (AI) and machine learning (ML). These technologies enable them to build up a more accurate picture of their business customers, automate processes and use them to cater more effectively to their needs.
Businesses in turn benefit from greater control over their accounts, improved visibility and a better understanding of their finance. In time, this will result in employees spending less time manually interrogating accounts and instead being able to focus on more value-adding tasks.
Specifically, the use of artificial intelligence (AI) and machine learning (ML), will allow banks to offer the following benefits to their business customers:
AI technology will enable banks to gain a better understanding of their commercial customers spending habits. In turn, this will help banks to more accurately forecast how much credit businesses require and set automated limits on spending accordingly.
For the businesses themselves, AI will allow for credit limit redistribution based on what different employees regularly spend. This means that credit could be dynamically allocated in an optimal way, anticipating the amount of credit employees actually need based on their spend history; 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, ensuring credit is being used to the greatest effect.
Banks are also beginning to use AI to offer businesses extra tools and services, such as AI enabled expense management systems, which simplify the expense process and reduce the amount of time employees and finance departments spend on such tasks.
For example, such systems may pick up that a user consistently applies a particular expense code to log a sum of £5 in a coffee shop on a weekly basis. Once this behaviour has been demonstrated enough times, it becomes a pattern. Hence, the user is no longer obligated to code the transaction themselves, as the system automatically identifies the type of expense and codes it accordingly, ready for review and submission.
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, with the employee then only needing to check that the correct codes have been applied.
As banks make better use of AI for fraud detection, businesses will benefit from improved security features. Currently, companies lose an average of 7% of their annual expenditure to fraud, but the use of AI will reduce this by detecting any anomalies in business accounts and fraudulent activities much quicker than previously possible. Similar to expense management, this works by the model understanding what is ‘normal’ for each account or card and recognising patterns based on past transactions and behaviours. For example, if 99% of the transactions for one account happen Monday to Friday, a transaction that occurs at the weekend will be viewed 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. The effectiveness of this technology in lowering incidences of fraud is also demonstrated at Visa, by using AI to reduce 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.