Machine learning in corporate banking is creating new opportunities for financial institutions to strengthen fraud detection while reducing the false positives that can disrupt legitimate transactions. By analyzing large volumes of payment data and identifying behavioral patterns, machine learning models can help banks detect suspicious activity that might otherwise be difficult to uncover.
Invoice fraud is one area where these capabilities can be particularly valuable. With millions of legitimate payments processed every day, identifying fraudulent transactions can be challenging, especially for small and midsize businesses that may still rely heavily on manual and paper-based processes. Digital systems combined with advanced analytics can help financial institutions identify anomalies earlier and reduce the potentially significant financial impact of fraud.
In a recently published Viewpoint titled Artificial Intelligence in Corporate Banking, we discussed the increasing usage of machine learning in certain areas of corporate processes, including AP and AR to improve document match rates and so forth. So this particular news release is within that same sphere, and points to a specific use case around fraud management. One of the earliest deployed scenarios for machine learning is for both preventing fraud and effectively reducing false positives.
“We apply sophisticated analytical techniques to vast amounts of payments data to build models which identify suspicious activity. Every time a business pays an invoice, a behavioural signature is left behind. By analysing these signatures, and the signatures of historical frauds, we are able to identify and flag suspected incidents of fraud.”
The pre-requisite for successful use of machine learning is an ability to manage large data sets, which means implementing digital systems and processes. In this particular announcement, the bank seems to be directing the service more towards SMEs, which have historically been burdened under the weight of paper, exacerbating ongoing cash flow concerns.
“Detecting invoice redirection fraud is akin to finding a needle in a haystack, as there are tens of millions of legitimate non-real time payments every day. While the volume of fraud is relatively low, the values are typically large amounts, so the business impact of this type of fraud can be crippling.”
Effective fraud detection increasingly depends on financial institutions’ ability to collect, manage, and analyze large volumes of payment data. Machine learning can identify behavioral signatures and compare transactions against historical fraud patterns, allowing suspicious activity to be flagged without treating every unusual transaction as fraudulent.
For businesses, particularly SMEs, these capabilities can provide an additional layer of protection against costly threats such as invoice redirection fraud. As corporate banking processes become more digital, machine learning in corporate banking can play an increasingly important role in improving fraud prevention, reducing false positives, and protecting business payments.
Overview by Steve Murphy, Director, Commercial and Enterprise Payments Advisory Service at Mercator Advisory Group
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