This article was originally posted on CUinsight.com on November 19th, 2014 by Austin Wentzlaff
Although a credit union is operated cooperatively, as a “not-for-profit” entity, the “not-for-profit” status of credit unions does not prevent them from making money. Credit unions generate their revenue almost entirely from lending, both business and personal. In fact, loans make up almost all of some credit union’s total assets with investments and cash attributing to the rest. Credit unions’ preferential tax treatment allows them to compete with banks by offering lower rates, making their loans more attractive to borrowers. But what would happen to credit unions if the competitive advantage were to disappear?
Competitors that leverage their data using Big Data/Analytics present a serious threat to credit unions who ignore it. This threat is coming from old, established adversaries that are implementing Big Data/Analytics solutions and from a new breed of competitors entering the market place, lending clubs.
Lending clubs are transforming the banking system by making it more efficient, transparent, and customer-friendly. They use a data-driven, technological approach to operate a credit marketplace at a lower cost than traditional bank loan programs, passing the savings on to borrowers in the form of lower rates. Lending clubs work as follows:
- Interested customers complete a simple online application.
- The application’s data is then evaluated by the lending club who determines an interest rate and instantly presents a variety of offers to qualified borrowers.
- Investors ranging from individuals to institutions then select loans in which to invest and earn monthly returns.
One of the top lending clubs in the nation, Lending Club (lendingclub.com), was founded in 2006. By March 2014, the company had already generated $5 billion in loans. The reason for this astronomical growth is their technological, data-driven approach. By investing more in Big Data/Analytics and less in the physical nature of lending, lending clubs have been able to satisfy more customers and capture market share. Data is being used in several ways to make lending clubs’ loans more attractive than credit unions’ historically lower rate options.
Loan Pricing Built off of Data (Analytics)
Utilizing Big Data/Analytics allows lenders to more accurately price loans and achieve a higher net interest margin. Many of the older, meet-the-market models currently used are overpricing loans based off of their risk categories. In this economy, many of the lower credit score categories are actually far less risky than they have been in the past but this is failing to be factored into the pricing models of non-data-driven lenders. New data-driven models are exposing many missed opportunities in the high yielding, “too risky” credit categories. By utilizing all off the data submitted on a borrower’s application rather than just the credit score, lending clubs and other data-driven organizations are able to identify the loan segments that offer opportunity to increase net margin.
Lending clubs have taken an online/mobile approach which allows them to cut costs dramatically. By operating almost entirely online and leveraging the data that is created, lending clubs avoid the cost of purchasing and maintaining physical assets. They are also capable of serving a larger customer base with fewer employees. Technological, data-driven approaches to lending allow lenders to automate many processes once done by several employees, greatly reducing the resources and human error often associated with the process. Luckily for credit unions, they have far less physical locations than larger banks and can easily capitalize on this trend toward mobile while avoiding the cost of liquidating physical assets. The move toward mobile will require a proper data management strategy, however.
Big Data and Analytics presents both opportunity and challenge for credit unions. While credit unions are rich in data that allows them to more accurately price loans, they are failing to utilize it. Meanwhile, competitors such as lending clubs are leveraging their data to capture market share. Credit unions already have an advantage over other financial institutions with their preferential tax treatment but this will certainly fade away if their loan pricing fails to compete with lending clubs. The combination of Big Data/Analytics and preferential tax treatment offers credit unions the opportunity to be one of the most attractive lenders in all of the financial services industry.
If credit unions fail to adopt Big Data/Analytics solutions, they may lose their most competitive pricing advantage to lending clubs and may fade away like many other companies that failed to adapt in the past. Credit unions are at a crossroads, much like Borders Books was when Amazon entered the book retailing space. Today, they are much richer in data and experience than the upstart lending clubs that are entering the space. What remains to be seen is how seriously credit union CEOs view the threat from these new entrants. If credit unions engage in a strategy to become highly competent in Big Data & Analytics, they will retain the upper hand.