Community bankers are like everyone else when it comes to good customers. No matter how one defines a "good" customer, once found, bankers will typically expand the total relationship as much as possible, to the maximum allowable concentration.
Like the links in a chain, we know intuitively that every large exposure we add also increases risk to the organization. While each customer by themselves may be a good single credit risk, the very act that some are concentrated and many are linked increases risk of the overall portfolio.
By structure, community banks carry many concentrations including geography (usually locked into a tight regional area), customer (often the top 10% of customers represent 85% of loans/deposits outstanding), issuer (large exposures to FNMA, FHLMC and FHLB) and even rate structure (most loans on the books are Prime-based). Each of these concentrations carries its own issues and risk profile, but it is a given that community banks have less diversified portfolios than larger banks.
As a result, when it comes to risk management, community banks need to spend extra time quantifying concentration risks and setting proper limits to ensure exposures are managed.
One way many community bankers think about concentration risk is also one of the easiest to calculate. Simply take the outstanding loan (or deposit) position of a given credit and divide it by total capital. Then, sort descending by largest percentage to smallest and take a close look at anything above 5% and a very, very close look at anything above 10%. Simply put, the bigger the single outstanding credit is compared to capital, the more risk the bank is generally carrying.
While this is a simple way to calculate concentration risk, it does not capture other key elements bankers should consider. For example, to manage concentration risks, bankers should take into account (and allocate capital accordingly) concentration exposures by lending type, industry sector, collateral, correlations (both asset and default) and other factors.
Without getting too carried away, community banks can begin to examine risk by thinking about the loan portfolio not as individual credits, but rather as a series of interconnected dots of risk. In so doing, it becomes readily apparent that a certain group of loans within the portfolio may have a strong linkage (or correlation) to others.
Thus, within the context of a portfolio of borrowers, default correlation measures the strength of the default relationship between a given set of borrowers. Knowing this not only gives bankers better insight into the risks within the loan portfolio, but also helps insulate against risks from one borrower/sector that could ultimately spill over into another borrower/sector, as a result of high correlation. Generally speaking, if two companies are exposed to the same environmental and risk factors, then they also have a higher default correlation.
As lenders know only too well, over time, some borrowers will default on their loans. That is why diversification of credit exposures is critical; large single exposures are inherently dangerous and why risk management is vital to long-term profitability of the institution.
You don't have to be a mathematician to know too much of a single thing can be bad for business and a long chain of interconnected links can be risky. Ultimately, banks should strive to understand these risk correlations/linkages within the portfolio and limit individual and linked exposures. In so doing, bankers help protect against economic downturns, borrower difficulties and other unforeseen events.