Challenge 2025

The Digitization of the Global Credit Union System

 

A New Decade, A New Goal

In 2014, World Council of Credit Unions set a goal of reaching 260 million credit union members worldwide by 2020.

Through a concentrated worldwide effort, credit unions were able to reach our "Vision 2020" goal by 2017. But that growth was not even across all countries or among all credit unions. The credit unions that grew were those that offered core services via online and mobile channels. That is why we are now addressing how we increase membership going forward—through the digitization of the global credit union system by 2025.

Measuring Global Digitization

World Council will measure the digitization of credit unions in four key areas and report annually on the progress being made toward Challenge 2025.

Digital Channels

Offering members core digital transaction services such as online and mobile banking, online payments and online loan processing.

Digital Payments

Connecting your credit union to a shared payments system that allows for mobile payments and integrated with a national payments system.

Data Analytics

Employing data analytics to determine additional service offerings to members, and helping to identify those that need financial literacy or counseling services.

Cybersecurity

Implementation of a cybersecurity system that complies with national regulations to protect members' consumer data from digital attacks and intrusions.

 

Follow Our Progress, Tell Us About Yours

Track the latest developments in digitization by subscribing to our Challenge 2025 Blog. You can also send us updates on how your credit union or credit union system is striving to help us meet Challenge 2025 at communications@woccu.org

Machine Learning Can Improve the Consumer Experience

The following post is provided by Origence, a financial technology provider dedicated to creating new approaches to the loan origination experience—and a brand of CU Direct, a World Council associate member. 

Computer programming has come a long way since the late 1970s and early 80s, when high school students learned BASIC and wrote simple programs on Commodore 64s. Today, six-year-olds learn coding from LEGO kits purchased at Target and young entrepreneurs are earning millions writing apps and selling them to global fintech companies.

Until recently, however, one thing had remained constant for nearly 40 years in computer programming: humans had to first write explicit rules and models.

AI, machine learning and your data

Artificial intelligence (AI) doesn’t require explicit rules. Instead, computers perceive their environments and make decisions that will increase the likelihood of achieving their goal.

Machine learning is a subset of AI. It gives computers the ability to learn—usually by providing statistical data—without being programmed every step of the way.

Lenders are ripe with data, which means machine learning can bring a lot to the table to the lending community. Early adopters in financial services are already using machine learning to a competitive advantage. Within the next couple of years, machine learning software will be widely adopted as a general practice, and within five years institutions that aren’t using it will find themselves at a competitive disadvantage.

If you’re in charge of your institution’s five-year operational, financial or technology plan, that news may come as a surprise. Thankfully, you don’t have to develop the technology yourself. Forward-thinking vendors are already providing machine learning solutions and creating new ones. You have the opportunity to partner with a provider within the next few years and apply machine learning in a way that caters to your specific needs and improves both your bottom line and your member experience.

Origence has been exploring new ways to effectively leverage and incorporate machine learning at financial institutions in our Innovation Lab.

The benefits of machine learning technology

For lending executives, the development of machine learning applications that assist with processing and quality control during the loan funding stage can have a dramatic positive affect on your lending process. Even if you’ve automated your lending process, when it’s time to fund a loan, someone still has to manually “stare and compare” key information on the loan docs to make sure it matches what is on your system. Machine learning can automate most of that process. Machine learning even has the ability to enhance optical character recognition (OCR) and automatic document recognition (ADR).

Further, machine learning can deliver a quality control confidence score, informing you with how much certainty each field in your loan package is correct. You have the ability to adjust the acceptable confidence score to increase loan processing efficiency. Interest rate will need a 100% confidence score, of course, but to speed things up you may decide a loan can be approved with a lower confidence score in the email address field.

The result is a system that can process more loans, without having to hire additional staff. Your lending team won’t spend hours on quality control, so they can dedicate more time to other areas and needs.

Other potential machine learning solutions, when built upon AI, can effectively analyze data to improve underwriting, loan portfolio management, marketing, cross-sales and the consumer experience.

To properly value a loan, significant data in addition to borrower creditworthiness must be considered, such as fluctuating collateral value, economic predictions and other factors. Machine learning cannot only analyze all of these data sources together to produce a coherent decision, it can also learn from the data to make better predictions about future loan performance.

Machine learning can also identify new correlations among borrower subsets that can be used to improve marketing. For example, this technology can determine which owners are likely to buy a new car based on their current car model or that pet owners who spend more than $300 per month on supplies are more likely to take out and use a credit card (purely hypothetical examples).

The beauty of using machine learning in this way is that it combines the digital ability to analyze large collections of data. Machine learning algorithms can crunch millions of fields of data for days on end to uncover correlations and insights that we may never discover on our own. With these answers, we can further refine the questions to produce even better results. Imagine how much time that would take one of your in-house data analysts—time that would be taken away from other strategic initiatives.

Preparing for machine learning

What can financial institution executives do to prepare for these powerful new tools? The most important step is to clean up your data. The old adage “junk in, junk out” still applies. Machine learning may be able to infer a more intelligent answer, but it does not think abstractly. It must have clean data from which to work.

The second step is to prepare for the cloud. Cloud solutions make machine learning ventures more affordable and effective. Lending institutions don’t currently have to host their systems in the cloud, but they should become cloud-ready to take advantage of hybrid systems and/or to leverage APIs to take advantage of emerging solutions.

The benefits of this technology speak primarily to the bottom line, but they also support the ability to provide a more personalized customer experience. For example, when applied to underwriting, machine learning can be used to expand credit to borrowers that lack a credit history or whose credit score may not accurately reflect propensity to repay. Freeing up staff from mundane quality control tasks allows them to spend more time assisting borrowers, ensuring quality service and experience.

Simply put, machine learning can free up your team to provide even better service to your borrowers.