Financial Institutions and Banking News

Breaking Up Is Hard To Do

Protect Bank Interests After a Divorce
Privately owned family businesses typically make up a significant portion of community banks’ loan portfolios. Often, such businesses are co-owned by two partners — who are also married. If the marriage falls apart, will the business follow suit? There are several factors to be aware of if your bank’s loans are at risk due to divorce.
Control and goodwill matter
Sometimes one spouse controls the business, and the other spouse pursues outside interests. A key question in these cases is how much of the private business interest to include in the marital estate. The answer is a function of purchase date, prenuptial agreements, length of marriage, legal precedent and state law.
Goodwill is another point of contention. If a business has value beyond its tangible net worth, how is intangible “goodwill” split up? All goodwill is included in (or excluded from) the marital estate in some states. But about half the states divide goodwill into two pieces: business goodwill and personal goodwill. The latter is excluded from value in these states.
Accurate valuations and reasonable payout periods are important. Settlements that disproportionately favor the noncontrolling spouse can drain company resources and cause financial distress. If the parties can’t reach an equitable settlement, it’s also possible for the court to mandate a liquidation, which threatens business continuity.
When the company buys out a spouse, Treasury stock might appear on the customer’s balance sheet. Or you might see an increase in shareholder loans if the owner-spouse borrows money from the business to pay divorce settlement obligations.
Avoidance strategies can backfire
The noncontrolling (or nonmonied) spouse also may receive alimony and child support from the controlling shareholder. Maintenance payments typically are based on the owner’s annual salary, bonus and perks.
Unscrupulous owner-spouses may try to change compensation levels in anticipation of divorce. Depending on the type of entity they own, a lower wage level may benefit them in negotiations for spousal maintenance and child support.
Also be aware that what divorcing borrowers say about unreported revenues, below-market compensation and personal expenses run through the business could lead to negative tax consequences. Publicly admitting these tax avoidance strategies puts both spouses and the business at risk for IRS inquiry, which could lead to difficulties repaying the loan.
Buyout plans can prevent dissolution
Many private businesses are run by both spouses, whose complementary skill sets make for a hard decision: Who’s going to run the business after the divorce? In limited cases, the spouses may want to continue to run the business together. Like most stakeholders, if co-owners decide to split up personally, but maintain their professional relationships and continue co-managing the business, you may be rightfully skeptical about their future business relationship. Usually, however, the parties can’t imagine working with each other. Such a scenario requires a buyout and a non-compete agreement.
Buyouts should occur over a reasonable time period and can include an earnout — wherein a portion of the selling price is contingent on future earnings — to avoid undue strain on the business. Future success is uncertain when a business loses a key person. It’s fair for both shareholders to bear that risk. If they don’t, the remaining owner, and your bank, could be at risk.
Even if your family-owned business borrowers aren’t currently contemplating divorce, consider what might happen if they did. Proactive family businesses have a buy-sell agreement in place before personal relationships sour. Factors to consider include valuation formulas and methods, valuation discounts, earnout schedules, postbuyout consulting contracts, non-compete agreements and payment of appraisal fees.
Staying engaged with borrowers is key
Keeping your bank’s loans stable and profitable requires you to stay aware of many issues that might crop up for your borrowers over time — including divorce. If you stay on top of potential problems, you’re likely to be able to help your borrowers navigate these difficult waters and come out relatively unscathed, protecting your loans in the process. Visit our financial institutions’ page to connect with an expert.  © 2020
Financial Institutions and Banking

AI Benefits in Community Banking

Artificial intelligence may be the future of community banking
Recent technological developments — such as artificial intelligence (AI), robotic process automation (RPA) and machine learning — are rapidly changing the way we do business. And the banking industry is no exception. Although large banks were the first to embrace these technologies, an increasing number of community banks are now recognizing their value. It may be some time before smaller banks can afford these technologies, but their potential benefits shouldn’t be ignored.
Enhance relationships
At first glance, AI and automation may seem inconsistent with the personalized attention most community banks rely on to distinguish themselves from their larger competitors. But in fact, these technologies enhance a community bank’s ability to personalize a customer’s experience.
Of course, technology can’t replace human judgment, but by automating and streamlining routine tasks, it can free up staff to focus on what they do best: onboarding new customers, developing personal relationships with current customers, and educating all customers about products, services and promotional opportunities.
Take advantage of new technologies
The potential uses for AI, RPA and other new technologies are virtually limitless. For instance, community banks can use these technologies to:
Open accounts. Some banks are using RPA to automate the account opening process and even accept loan applications. It may take a staff person only five or 10 minutes to open an account. But when you consider the many thousands — or tens of thousands — of accounts opened every year, automating the process can save a significant amount of staff time. Plus, automated systems can help ensure all required information is collected.
Change addresses and other information. Typically, when a customer calls a bank to change his or her address or other information, an employee must go through multiple computer screens in the bank’s system to process the change. With RPA, once the initial information is input, the system can complete the remaining steps automatically, saving time for both customers and bank staff.
Detect BSA/AML crimes. Banks can use AI and machine learning to support their Bank Secrecy Act (BSA) and anti-money laundering (AML) compliance efforts. For example, these technologies can sift through enormous amounts of transaction data and identify suspicious behavioral patterns that would be virtually impossible for humans to detect. And by minimizing the number of false positives and negatives, they can help ensure that investigators focus on truly suspicious activities rather than legitimate transactions.
Improve cybersecurity and fraud protection. The ability of AI to mine huge amounts of data and quickly spot anomalies makes it a powerful fraud detection tool. It’s particularly effective when it comes to cybersecurity. A bank’s IT department may receive hundreds of thousands, or even millions, of cyber threat alerts every month — too many to investigate effectively. AI can comb through this information and alert the bank to potential threats that require immediate attention.
Mind the data gap
As advanced technologies become more commonplace, one of the biggest challenges for community banks will be to ensure they have sufficient data to use these technologies effectively. To do their jobs, AI and machine learning require large amounts of data from which to learn and train. For large institutions with millions of customers, this generally isn’t an obstacle — but many community banks lack the data they need to ensure these technology solutions are effective and accurate.
To prepare to take advantage of the many benefits offered by AI and machine learning, banks should start by taking inventory of their own data. If necessary, banks can supplement this data through data-sharing arrangements or by purchasing data from third parties. A relatively new technique that shows promise is “synthetic data,” which is generated by applying algorithms to a bank’s existing data.
Ready for prime time?
AI and automation have great potential, but it may be some time before community banks fully embrace the technology, which is expensive to implement and maintain. In addition, there may be significant costs associated with gathering the data needed to run it effectively. Nevertheless, it’s important for community banks to monitor developments in this area and consider how these technologies might improve their businesses down the road. © 2020