This article appears in Finextra and covers a high profile topic, artificial intelligence (more specifically machine learning), and how it might (and already does in some cases) apply to the corporate banking part of financial institutions. It is a fairly lengthy piece compared to many we see, and digs into several use cases. As you might imagine the corporate sector lags retail (consumer), and consumerization of the CFO suite raises the expectations for banks to deliver better services.
“Unlike in the Retail Banking industry, where most customers use only one institution for their banking services, Corporate Bankers have always had to operate on the basis that their customers will have relationships with several institutions and therefore they have to compete for share of wallet.”
We agree with the author and have already written a viewpoint on the topic back in February, which is titled (unsurprisingly) Artificial Intelligence in Corporate Banking. One of the interesting cases discussed by the author in this indicated headline posting is how banks may well be use AI to make find leading edge indicators of corporate intentions to change primary through tertiary bank relationships. This type of analysis can help to predict when a relationship might be changing and why, allowing the institution to take proactive and corrective action to preserve revenue streams.
“What is it that corporate treasurers (the principle owners of the relationships with the banks) want and what will incentivise them to increase the proportion of their banking business that they give to one institution over another?….. With significant returns if this potential loss of share of wallet is addressed prior to it occurring this makes it an ideal case for using Machine Learning.”
Several other uses are discussed, including reduction in payment errors, fraud and so forth. Worth a read.
Overview by Steve Murphy Director, Commercial and Enterprise Advisory Service at Mercator Advisory Group
