Despite the efficiencies and ROI promised by the latest developments in technology, FIs are often slow to implement new solutions. With the onset of the pandemic in early 2020, many FIs were forced to expedite their digital banking offerings and were hit with challenges resulting from outdated and inefficient technology. However, due to current economic conditions, the financial services industry is on the cusp of facing similar obstacles again.
The inflation the US is experiencing will result in increased consumer debt, delinquencies and loan loss reserves, which will have a negative impact on a credit union’s success. Implementing technology equipped with tools like predictive intelligence and delinquency risk scoring will help credit unions stay ahead of the curve and efficiently reduce loan loss reserves – ultimately increasing revenue and reducing costs associated with the management of delinquencies, loss mitigation and recoveries.
For the past decade or so, the economy has benefitted from a positive stock market, leading to a decrease in delinquencies while consumers continue to take out loans. In fact, the percentage of mortgage delinquencies dropped to record lows of 3.4% in December 2021. This has allowed many credit unions to operate without a heightened importance in collections and recovery.
However, the industry is experiencing a shift in the economy in the wake of the pandemic as interest rates and inflation begin to soar.Reports show that consumer credit hit a record high of $52.4B in March, up from $37.7B the previous month, while the pre-COVID average was close to $15B per month. In the case of the increasingly popular “buy now, pay later” (BNPL) option, consumers are enticed to split-up payments, only to be hit with interest later down the line when cash flow dries up and installments go unpaid. Studies show that a third of U.S. consumers who used BNPL services fell behind on one or more payments, while savings rates spiked over the past two years. As consumers’ savings accounts go down, their credit limit is extended, yet “payday” nears.
Additionally, not only is the economy declining, but funds – PPP and COVID impact loans, as well as disaster relief – have dissipated, with an influx of delinquencies expected on the horizon now that repayments are required.
Until recently, community financial institutions were accustomed to seeing few late charges, over-due accounts and delinquencies. Credit unions’ collections and recovery departments largely made do with spreadsheets, manual systems, or inexpensive and unscalable solutions that just barely met the needs of a minimal amount of collections.
Because credit unions specialize in lending, the increase in delinquencies means there is an exponential number of loans on the books, requiring a system that can not only track and work the loans efficiently but also meet members where they are most comfortable – especially as this can be a sensitive topic.
Once members start to feel the downward pressure on their credit score and truly reach the “valley of despair,” the cadence of completed payments often slows to a halt, with folks opting to spend available cash flow on more pressing necessities, such as food to feed the family or gasoline to get to work.
As the economy improves or after overcoming financial hardships, members will eventually return to a profitable, creditworthy state and need to bank with a financial institution. It is important to also remember that the credit union spent time and money attracting the member in the first place, so you do not want to tarnish the relationship over delinquency or a blip on their credit score, because most folks will return from the “valley of despair” victorious.
Rather than abandoning members to navigate this trek alone, credit unions can provide a guiding light to help members navigate their financial journey, and have a positive impact on their financial health, by replacing or augmenting existing solutions with predictive intelligence and risk scoring capabilities.
The process of predictive intelligence includes gathering and analyzing data on members’ expenses overtime to make informed decisions about what may occur in the future. Using tools like machine learning and business logic automation, credit unions can continuously update projections by redefining workflows based on how members’ deposit and buying patterns change.
Using this collected data, members are then placed into tiers of risk. As members move up or down to a different risk level, there is more customer involvement required in collecting on loans, as well as collaborating with internal committees to determine the best payment plan or modification.
In the case of a member that always makes payments although occasionally late, the individual is very low risk in the risk scoring model. Using predictive analysis, the credit union can then reach out via SMS text with a reminder, or even an offer to extend the payment’s due date to later in the month, ultimately enhancing the member experience.
Those with the highest level of risk often involve higher dollars, and yet, these are less likely to provide a return on that payment from the end user. As a result, the workflow automation with both the high and low risk members saves the credit union time and money.
Credit unions are at an advantage, too, sitting on lifetimes of insightful data and account history that goes mainly untapped. Tracking historical averages equips credit unions to proactively identify which members could potentially go into delinquency, so instead of starting from scratch, the institution can virtually view the past several years of payment history, along with other outside data, to determine credit worthiness and risk. This data can be run through business logic and machine learning generators, performing historical analysis systems to grow and recognize predictive patterns.
For instance, an easy red flag to identify through the financial analysis of transaction history is a member that comes through with payday loans. This reveals that the individual is turning to other providers for money, and likely means the member is in a financial pinch until their next paycheck.
Using member data in this case can really turn into more of a member service, because most folks will need assistance with a workout plan. The credit union can then work with members through these hard times, such as offering to lower or extend the monthly car payment on an auto loan.
Unlike banks, credit unions are budget-driven nonprofits, so the increase in delinquencies means credit unions are missing funds and resources needed to manage day-to-day operations. Increased loan losses force credit unions to tap automation and workflows to help them better service their members.
Manual processes in today’s digitally driven world are no longer scalable or effective. Regardless of the number of collection or delinquent accounts, augmenting data analysis with an automated system to assist in reporting requirements saves the credit union from inefficient and costly processes. More than just delinquent loans, technology can also be used to analyze different types of accounts, including share draft, negative share draft and credit card, as well as tracking and recovering these assets. If a credit union is not using automation and automated workflows to reach their members and make collections easier, that is a key sign that it is time to invest in new system with these processes built in.
When it comes to collections, most people want to repay their debts. Implementing technology to streamline what has been a manual process allows credit unions to devote more resources to handling these situations quickly, and with a sense of humility and sensitivity. When done successfully, these members are much more likely to come back and bank with their credit union once returning from the “valley of despair.”
Kris Bishop is CEO of FIntegrate, a data-driven analytics company focused on portfolio tracking, collections and revenue recovery software for the financial industry. For more information, visit www.fintegratetech.com.
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