There are a few reasons for a recent spike in bank account closures, according to the recent American Banker article
Penny Crosman writes, "One is a heavy reliance on AML software to monitor transactions overseen by decision-makers who don't know individual customers. Another is outdated rules used to determine which transactions are suspicious. A third is a set of incentives that push banks to rush and not take the time to understand individual cases."
It's true that algorithms alert financial institutions to transactions that appear suspicious. Thomson Reuters found the number of
Are banks trapped in a broken model with no alternative but to prioritize efficiency at the expense of their customers?
The reality is much more nuanced. Certainly, the existing AI transaction monitoring model is flawed, but it is only part of the puzzle. Financial institutions have options — if they take the time to explore them.
In the recent American Banker article, a former AML executive at a large bank says banks' emphasis on efficiency has shut customers out of the process. In a risk/reward trade-off, we're led to believe it's too expensive to include customer input and losing a "minuscule" number of customers is preferable to the inevitable "regulatory headaches."
The problem with this logic is that when you make a mistake, you not only lose the account, but you may also lose that customer relationship forever. Even if it's a small subset, there is an increased risk that these customers will complain to regulators about their accounts being closed.
There are regulatory requirements to report and crack down on SARs, as well as regulatory requirements to monitor customer outcomes — and they need to coexist. These issues impact one another but are often approached in silos.
Bank customers' complaints of sudden account closures track a rise in automated anti-money-laundering decisions and possibly outdated AML rules.
Syncing transaction data requires a complex interplay of software and systems, yet customer input must become a source of data in this decision-making process. Customer voices are a business's most valuable asset. Each complaint or inquiry can be unpacked for value, as a data source rich with insights.
In fact, here's where AI can be really useful. Cases with 100% SARs certainty can be delegated to AI, whereas cases with less certainty should incorporate some human involvement.
Building processes that take customer input into account is not only vital to humanizing the banking system, it's an opportunity to improve business outcomes.
The human impacts of SAR processes gone wrong were captured by the New York Times in a recent
If this problem impacts customers in such a big way, it's worth diving into, and there's no quick fix. Dedicated cross-functional teams can test, refine and release new approaches focused on understanding the when, where, what and how.
When are the problems occurring? Where are the problems in the process? What information would help ascertain whether or not there was criminal activity, and then how can change be implemented?
Companies must venture down a funnel where each new layer of doubt is addressed by data and intelligence. It's imperative that companies go through their processes, tear them apart and understand the data to see where the errors are occurring. Only then, with a grasp of where improvements are needed, can solutions become clearer.
Solving the problem of sudden account closures will require a refined data-driven process of how everything comes together and where it's going wrong. Indeed, the nuance is always the opportunity.
