If you grew up in a small or medium sized town, your parents and neighbors most likely banked with a small community institution.  If you think back to opening your first savings or checking account, it most likely was at a small local bank. Small community banks remain vitally important to the nation’s economy because they provide important financial services to folks like small business owners, farmers, and everyday citizens.

As you’d expect, with the different types of services offered by these banks comes a different set of anti-money laundering (AML) risks that are unique compared to the risks of national or global banks.  Nevertheless, small community banks share the same responsibility of mitigating their AML risks to comply with state and federal regulatory requirements. Facing various compliance demands is an extremely expensive endeavor, and small community banks can’t afford to just throw the necessary personnel at AML and compliance challenges as easily as their larger brethren.  This is why advanced data science solutions are a perfect fit for community banks in need of a more efficient and effective risk mitigation tool.

According to the FDIC’s Community Banking Study released in December 2012, there were approximately 7,016 FDIC-chartered community banks in the United States.  Community banks are generally classified as having holdings of around $1 billion; however, the FDIC points out that this number is a moving target based on inflation and other factors.

Other interesting facts to demonstrate the importance of community bankers is evidenced by the Independent Community Bankers of America (ICBA) which reports that of all U.S. banking organizations, 88.2% have assets under $1 billion and almost 50% have assets under $250 million.  The ICBA reported that there are more than 600 counties, almost one out of every five U.S. counties, that have no other physical banking offices except those operated by community banks.   Also noteworthy is that community banks make more than 50% of small business loans and 82% of agricultural loans.  These are staggering statistics that visualize the importance of community banks.

National and global banks deal with widely different risks such as correspondent banking, securities trading, and trade finance.  National and global banks have compliance staffs in the hundreds and devote upwards of 20% of their operating budget to compliance.  Community banks, on the other hand, might only have one or two branches and a few ATMs, but they still deal with equally important compliance risks with a smaller compliance team.

Even though they process fewer transactions, community banks are still required to maintain effective transaction monitoring systems (TMS) to seek out structuring transactions, funnel account activity, and other common money laundering and terrorism financing red flags.  Smart white-collar criminals and money launderers are known to attempt to evade larger bank’s compliance programs by opening accounts at smaller community banks.  The advantage community banks bring to the compliance fight is that they are smaller and generally know their customers better.   Some community banks might only file five to 10 SARs per year.  For example, in one county in Kentucky with a population of 66,000, the FDIC-insured bank there filed 18 SARs in 2016.

Advanced data science and artificial intelligence (AI) solutions that are leveraged today by national and global banks, can greatly assist small community banks in efficiently addressing risk and more effectively, without increasing their already burdensome compliance budgets.


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