In the digital era of banking, concerns over financial crime are not uncommon. Although it’s important for financial institutions to continue moving forward with digital transformation, they must take into account the risks they’re exposed to in the process. That’s why it’s crucial for banking companies to take the necessary measures to mitigate those risks with digital banking compliance.
What is Compliance?
Compliance ensures that banking companies are acting in accordance with laws, regulations, and rules, keeping their integrity and reputation in check, according to the Houston Chronicle. Furthermore, every banking company must have a compliance department to make sure their policies and equipment are sufficient to rule out any risks. If any risks are discovered, they must create solutions and update regulations.
Anti-Money Laundering (AML)
A more specific issue related to compliance is AML, which “refers to a set of procedures, laws and regulations designed to stop the practice of generating income through illegal actions” (Source Investopedia).
When it comes to fighting financial crime, three factors play a role in banks’ increased vulnerability, according to a McKinsey article:
· There’s an increased amount of cross-border transactions and merging of global economies.
· Rules are constantly being changed as the priority shifts to terrorism.
· Governments are incorporating more economic sanctions in their foreign policies.
In order to mitigate such risks, major U.S. banks now invest in more AML compliance staff, as well as other processes and systems that come with a hefty cost and sometimes aren’t as effective.
As a result, banks are struggling with poor-quality data, excessive reporting of suspicious activity due to lack of consistency, and more. Fortunately, new technologies are being made available to banks, such as data aggregation, advanced analytics, and automated processes, which will help reduce costs and enhance customer experience.
1. Data Aggregation – When it comes to data aggregation, banks are now utilizing cost-saving tools that can more effectively collect poor-quality data. Machine learning (artificial intelligence) can be used on unstructured data to detect money laundering. Thanks to these tools, banks can now “automatically validate more customer identities, identify beneficial owners faster, and map how specific customers are connected to other individuals and legal entities, especially those earmarked as higher risks.”
2. Advanced Analytics – Advanced analytics can be used to assess customer risk and detect suspicious activity. Furthermore, statistical models, which rely on machine learning, can prevent a fraction of false reports, allowing investigators to focus their attention on high-risk activity. In contrast, linear rules, which are “based on an institution’s experience, a typology of known money-laundering events, and explicit regulatory requirements” cause an outrageous number of false positives and sometimes even false negatives.
3. Automated Processes – In many industries, automation has several benefits, including higher productivity and increased efficiency. In the banking industry, robots can complete tedious processes much faster, like due diligence and investigation, allowing staff to be able to concentrate more on detecting money laundering. Specific tasks that can be automated include “the population of case files for investigators, the closing of level-one alerts, and the population of SAR forms.”
(Source McKinsey)
Maximizing your Success with Compliance
To maximize your success, you should ensure that the leaders at your organization are communicating to their teams the importance of compliance. According to a LinkedIn article, it takes a strong and vocal leader to communicate to their organization why compliance matters and to actually execute it, emphasizing processes and solutions over rules. Leaders need to start putting what they say into action by making compliance part of “the organizational DNA as something that is followed and tracked, just as credit losses, investment performance, and top-line growth is followed and tracked.” In order to do this, “leaders need to set clear targets for their people, build robust management information to track the progress and act on non-performance on these targets” (Source LinkedIn).
