The Decision Maker

Data Use Cases for Credit Unions: Chapter 2

Posted by Lou Grilli on Jun 6, 2019 10:09:00 AM

Getting Out from Behind The Curve

Chapter 1 of this series considered the importance in establishing a specific goal to solve using data analytics and proving the ROI in order to justify automation and decisioning using business intelligence in a credit union. Chapter 1 also highlighted two real use cases of success credit unions have had using data analytics to solve real-world problems. According to a recent study conducted by Best Innovation Group (BIG) and OnApproach (now Trellance), 45 percent of credit unions don’t currently have a strategy in place, and those that do have a strategy still say it will take three to five years to implement. Credit unions that aren’t making the most of data analytics today could be in even bigger trouble if an economic downturn occurs, as some economists are forecasting. “As we go forward there will be a significant performance difference between those that have invested and those that have not,” says Kirk Kordeleski, senior managing partner at BIG. “We think any downturn in the economy will highlight the advantage that data-oriented FIs will have over their competitors.”

How Much Will It Cost

The survey revealed that more than half of the 85 credit unions surveyed have budgets in place for data analytics. Of those, one-third plan to spend more than $200,000, the other two-thirds plan to spend between $50k to $200k. In addition, credit unions need to consider on-going costs. A rough rule of thumb is that a CU with $500 million in assets should budget between $150,000 and $300,000 per year for three years to cover software/hardware, analytic applications, and strategy. Smaller credit unions can find some savings by relying on a CUSO to provide the analytics and associated services.

The following paragraphs are real use cases that credit unions have shown to prove out their investments.

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Topics: Business Intelligence, Credit Unions, Data

To Build or Not to Build (Buy) – That is the Question for Credit Unions

Posted by Peter Keers, PMP on Jan 31, 2019 1:52:49 PM

As the Age of Analytics for credit unions rolls forward, the question of “Build or Buy” is faced almost daily by decisionmakers. It comes at all stages in the data and analytics journey, so credit unions must understand the tradeoffs in deciding to Build or Buy.

First, however, consider the question itself: Build or Buy. “Build” means the credit union uses its own resources to design, construct, launch, and maintain an application or capability. “Buy” means acquiring these same elements from an entity outside the organization.

The fast pace of technological evolution has added an innovative dimension the definition of “Buy”. Increasingly, “Buy” includes Software as a Service (SaaS) as well as on-premises implementations.

The Build Option

The perceived advantages of Build are customization and control. By keeping projects in-house, the Credit Union can design a system tailored to its unique requirements. Although all credit unions are chartered to do a specific set of services, each has its own flavor for delivering these services.

These Build option advantages favor larger credit unions with greater resources. Having the team depth of a larger organization enables greater possibilities for having both the skills and numbers to take on Build projects.

The major disadvantage of Build is cost. A custom-tailored suit is more expensive than an off-the-rack brand. Another, subtle but important disadvantage is strategic focus. A credit union is wired to be a member-oriented financial services organization. Though it may have gifted technologists on its staff, most credit unions are unlikely to have the technical breadth and depth to build a truly industrial grade application. There is also a big risk of knowledge experts leaving the organization in the current low unemployment environment.

Another cost concern is ongoing maintenance and enhancements. Experience shows custom-built applications are notoriously expensive to keep up-to-date and in efficient working order. The credit union is saddled with this ongoing burden for its data and analytics capability to keep pace with new industry trends.

See 7 Challenges to Consider When Building a Data Warehouse: http://blog.onapproach.com/7-challenges-consider-building-data-warehouse

The Buy Option

At first glance, it might be assumed the Buy option is the mirror opposite of Build. A purchased product will not be exactly customized to the credit union’s specific requirements nor will the organization have as much control over the project. However, this is a game of trade-offs driven by primarily by the size of the credit union. In order to survive, all credit unions must embark on the data and analytics journey. Those ignoring this trend will ultimately be acquired by credit unions that do take data and analytics seriously or simply become obsolete.

For the majority of credit unions, the Buy option holds significant advantages. By giving up some customization and control, the organization gains significant data and analytics capabilities at a more affordable price. In fact, not only is a tested commercial product liable to cost less up front, it also has the advantage of having the bugs worked out as the result of use at multiple sites. Therefore, the cost and headaches of the inevitable errors in complex programming code are avoided. If fact, the perception that a Build project results in a more tailored outcome may be overstated. Most commercial products are very configurable to meet specific credit union requirements.

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Topics: Credit Unions, Data Integration, Insight Platform

Why Credit Union Digital Transformation Can’t Work Without Credit Union Data Integration

Posted by CU 2.0 on Jan 17, 2019 11:01:00 AM

It’s no good to be a dinosaur in the financial sector. Not only are dinosaurs notoriously temperamental, but they can’t type. Oh, and they’re extinct. If branches don’t want to go the way of the dinosaur, then a little credit union digital transformation is their best hope.

(Hint: credit unions aren’t the only industry affected by digital transformation and the emerging primacy of data.)

While digital transformation is certainly the goal, it can’t just organically happen. Credit union digital transformation is a strategic process that incorporates several approaches, from digital engagement to data integration. In this blog, we’ll talk about the challenges of credit union data integration and collaborative analytics strategies.

Tying Together Data Sources

Typical credit unions have somewhere around six to eight data sources. Some have more. While having the data is certainly nice, it’s not much good to just sit on it.

Core and ancillary systems produce data at prodigious rates. These streams of data are all separate, too. Siloed data streams are great when you need to understand only the data produced by one source. However, individual sources of data have a nasty habit of not producing a clear, complete, actionable picture.

Making matters worse is that each system stores its data differently. If you want to perform data analysis on any of your credit union members, you have to check in on each system and pull different data sets from them.

This lack of robust credit union data integration hampers solid, actionable analytics. The first challenge for credit unions then is reconciling individual data streams into one single source of truth.

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Topics: Credit Unions, Data Integration, Digital

Top 5 (and 5 most missed) of 2018: Credit Union Data Analytics – Part 2

Posted by Mark Portz on Dec 27, 2018 11:05:00 AM

2018 has been yet another exciting year in the credit union space as we continue to see the growing significance and adoption of digital and data strategies. As the year comes to a close, we like to reflect on the lessons we have learned and prepare for what is to come in 2019 and beyond. Through collaboration, the credit union movement has incredible unrealized potential. As we look back at 2018, we have compiled a list of some of the industry’s favorite articles regarding credit union big data/analytics (and some others you may have missed) featured on OnApproach’s blog, The Decision Maker. Enjoy!

5 Posts You Might Have Missed:

1. Collaborating for Analytics and Shared Data Applications with Paul Ablack via CUbroadcast [Video]

NAFCU Interviews_ OnApproach's Paul Ablack Discusses Launch of Central Data Repository for Industry.

Paul Ablack, CEO, OnApproach, had the chance to catch up with Mike Lawson of CUbroadcast at the NAFCU 51st Annual Conference & Solutions Expo. The conversation covers topics from evolution of A.I.digital transformation, a collaborative data lake for the credit union industryplatform analyticsdata encryptioncyber security, peer benchmarking, and shared applications on the CU App Store community.  

As a part of the discussion, Paul Ablack explained the progress of the collaborative online analytics marketplace, the CU App Store. In the conversation, Paul explains that, "[OnApproach is] going to build a community around the CU App Store, where credit unions can come in, they can contribute content, and they can comment on the content. Let's say someone puts a really good marketing segmentation report [on the CU App Store], others can build on it, can make it better, they can comment, and place reviews.

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Topics: Big Data, Credit Unions, Video

Top 5 (and 5 most missed) of 2018: Credit Union Data Analytics – Part 1

Posted by Mark Portz on Dec 20, 2018 11:04:00 AM

2018 has been yet another exciting year in the credit union space as we continue to see the growing significance and adoption of digital and data strategies. As the year comes to a close, we like to reflect on the lessons we have learned and prepare for what is to come in 2019 and beyond. Through collaboration, the credit union movement has incredible unrealized potential. As we look back at 2018, we have compiled a list of some of the industry’s favorite articles regarding credit union big data/analytics (and some others you may have missed) featured on OnApproach’s blog, The Decision Maker. Enjoy!

The Top 5 Favorites:

1. Leveraging Data to Create Exceptional Experiences at Ideal Credit Union [Video]

MnCUN Interviews_ Ideal CU and OnApproach Work Together to Leverage Data Analytics' Potential...

At the Minnesota Credit Union Network (MnCUN) Annual Conference, Paul Ablack, CEO, OnApproach and Alisha Johnson, Executive Vice President of Operations for Ideal Credit Union, joined Mike Lawson, Host of CUbroadcast, to discuss data access, member profitability, member engagement, data lakes, timely and targeted marketing, chatbotsreal-time analytics, and credit union collaboration.  

Part of the conversation focuses on the success of Ideal Credit Union's VIP Program. As stated by Alisha, "... It means a lot to our members... The first [program] that we worked with Paul and OnApproach on, before we started accessing data directly, was our creation of our VIP program. So, we had paid back to our membership over the last couple of years $6 Million, and that is because we have been able to identify who brings money to our membership, how successful they make us, and then we return it to them based on a number of different criteria. Without OnApproach, we would never be able to access that criteria, and even be fair in the distribution of the funds that we give back to our members." 

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Topics: Big Data, Credit Unions, Video

The Death of the Branch: A Lesson About Credit Union Data

Posted by Austin Wentzlaff on Dec 14, 2018 11:01:00 AM

The way we think about credit union data these days doesn’t mesh with what’s actually happening in the industry. Credit unions now have access to more data than they ever have. Failure to leverage that data though? That’s where you should be concerned.

Let’s walk through an example: just over 20 years ago, Amazon entered the book retail market. Their mission was simple: deliver personalized experiences to its customers and make each interaction unique and customized to the individual.

At the time, Amazon was just one man, Jeff Bezos, selling books out of his home. For the book market retail giants, Amazon was hardly a threat, just some crazy guy trying to compete with very large and long-established institutions. Companies such as Barnes and Noble and Borders Books had well over a thousand retail locations and were selling books hand over fist.

Well, we all know how that story ends—Amazon is one of the top retailers in the world and Borders Books is now bankrupt and Barnes and Noble is struggling.

Failure to properly leverage credit union data may hurt as many branches as Amazon hurt bookstores. Basically, the outlook is grim. 

Declining Emphasis on Branches

In the past, credit union success was closely tied to the number of branches it could open. The more branches, the more members, the larger volumes of deposits and loans, and the greater the success of the credit union. All of this success is measured by credit union size rather than credit union data.

As we’ve seen in other industries such as Amazon versus the book market, this has started to change dramatically. The emphasis on the branch at credit unions has since gone away. Members are now looking to more convenient avenues to do their financial transactions.

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Topics: Credit Unions, Branch, Data Analytics

Big Data and Analytics can do some pretty cool things for Credit Unions

Posted by Mark Portz on Nov 17, 2016 11:04:00 AM

Last week, OnApproach’s CEO, Paul Ablack discussed Big Data and Analytics with CUNA’s Senior Editor, Craig Sauer. In the podcast, we learn about the state of the credit union industry, what data means for financial institutions today, and how credit unions can thrive in an industry facing intense fintech disruption.

“95% of credit unions today are not able to truly integrate their data”, according to Ablack. Core vendor solutions do not allow credit unions to easily integrate data from disparate sources, or share and benefit from data of other credit unions. This means 95% of credit unions are at the bottom of the curve for analytics capabilities. As discussed in the podcast, less than 10% of credit union members are profitable. Unfortunately, credit unions at the bottom of this curve aren’t even capable of determining which members are not profitable, as factors such as product mix have proven to be an outdated and misleading determinant. Credit unions need to take action to integrate data and improve analytics to seize market opportunities.

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Topics: Big Data, Credit Unions, Data Analytics

Sharpen Collateral Valuation through Data Integration

Posted by Nate Wentzlaff on Jul 13, 2016 11:30:00 AM

Collateral Valuations are essential while serving members and maintaining a healthy credit union. However, credit unions are relying on inaccurate valuations of their members’ collateral values because of disintegrated data.

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Topics: Big Data, Credit Unions, Data Integration

FASB’s New CECL Comments Could be a Worst-of-Both-Worlds Approach

Posted by Joe Breeden (Deep Future Analytics) on Apr 19, 2016 11:30:00 AM

Last week’s comments by the Financial Accounting Standards Board (FASB) about how they will allow the different levels of complexity in credit loss calculations for lenders of different sizes would seem to be a victory for smaller credit unions and community banks.

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Topics: Credit Unions, CECL, Lending

Saving Small Credit Unions with Big Data

Posted by Nate Wentzlaff on Mar 30, 2016 1:00:00 PM

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Topics: Big Data, Credit Unions