The Decision Maker

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.

Read More

Topics: Insight Platform, Credit Unions, Data Integration

The Cost of Building a Data Warehouse for an Analytics Platform

Posted by CU 2.0 on Jan 25, 2019 10:02:00 AM

Credit unions can benefit greatly from collecting and storing information to leverage Big Data. The cost of building a data warehouse can be steep, though. If you’re considering building a data warehouse for your credit union, it’s important to know what you’re getting yourself into.

The benefits of building a data warehouse speak for themselves in the financial world. Getting into the data analytics game isn’t cheap, however. It’s not as simple as just buying a data warehouse and watching a video tutorial; no, getting started requires a large initial investment as well as ongoing support and upkeep costs.

Here are a couple of the common issues associated with building a data warehouse for the credit union industry.

Initial Investment Costs

There are two major expense considerations for any enterprising credit union looking to construct its own data warehouse. The most pressing of the two is the financial cost, and the second is the time invested. Because we’re talking specifically about credit unions, let’s discuss the monetary side of this investment first.

For an individual credit union, the cost of building a data warehouse or data lake for an analytics platform starts at around $500,000 at the low end. Most data warehouses and data lakes run well over the million-dollar mark. While it’s certainly a worthwhile investment, it can also be prohibitively expensive for smaller, more community-focused credit unions.

 The second major cost factor is time, though we could also say that it costs patience as well. Regardless of the size of the warehouse and the experience of the people putting it together, building a data warehouse takes an average of two or three years. If you want an analytics platform immediately, then creating one in-house from the ground up might not be your best option.

Read More

Topics: Insight Platform, Data Storage, Enterprise Data Management, Analytic Data Model, Data Integration

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.

Read More

Topics: Digital, Data Integration, Credit Unions

How is Digital Growth and Transformation Vital to my Credit Union?

Posted by CU 2.0 on Jan 10, 2019 11:00:00 AM

The traditional brick and mortar model has worked well for credit unions over the years. So long as they continue to deliver superior member experiences, that won’t change anytime soon. However, as more financial institutions offer mobile and online services, digital transformation will quickly grow in importance for your credit union.

Increasingly, the platforms on which credit unions engage and support their members are in the digital realm. While credit unions still offer phenomenal services in their brick and mortar branches, not all have the same level of digital sophistication typical of tech-savvy newer businesses. As younger generations begin their financial journey, poor technological performance will become an issue.

Who is the Competition?

Normally, credit unions have to deal with fairly predictable competition: big banks, community banks, other credit unions, and boxes hidden under mattresses. Credit unions have been able to distinguish themselves well in the field of financial institutions.

Currently however, entire generations feel comfortable bringing their phones or other mobile platforms with them to disrupt perfectly good dates, game nights, dinner parties, and so on. It’s not abnormal to see an entire group of young adults sharing a table at a bar while staring at their phones.

Businesses that have embraced technological growth by developing applications, online support, and mobile functionality have prospered: think Netflix, Amazon, and any pizza company that lets you order delivery with an app (that’s phone application, not appetizer, although personally, I want the option for both).

If Netflix is going to build a media production and streaming empire by beginning with mail-order DVD rentals, then credit union digital transformation can turn a small brick-and-mortar branch into a mobile-friendly powerhouse.

Read More

Topics: Digital, Disruption, FinTech

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]

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.

Read More

Topics: Video, Credit Unions, Big Data

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]

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." 

Read More

Topics: Video, Credit Unions, Big Data

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.

Read More

Topics: Credit Unions, Branch, Data Analytics

How Data Integration Helps Credit Union Analytics Platforms

Posted by CU 2.0 on Dec 11, 2018 10:59:00 AM

The immediate future of banking and the financial industry is in analytics. The ability to draw conclusions from massive sets of data helps financial institutions improve their advertisement targeting, their ability to underwrite loans, and a whole host of other things. One of the sticking points in the machinery is in credit unions’ ability to perform adequate data integration.

Data integration sounds relatively simple on its own. Data integration is the practice of combining multiple streams or forms of data into a single readable format. The extent of data integration needed increases as the amount of data—or the number of data sources—increases.

Why do Credit Unions Need Data Integration?

This will be a long answer, and so I’ll break it up into three parts. The first part will address the many sources of data that credit unions deal with on a daily basis. The second part will introduce the necessity of analytics platforms in finance. The third section will explain the role of credit union data integration in the grand scheme of things.

1.     Credit Unions Generate Lots of Data

Credit unions exist in the financial sector, which is technologically fast-moving. Partially because of this, and partially because credit unions must record financial and member data, credit unions are inundated with a massive amount of data daily.

Read More

Topics: Data Integration, Insight Platform, Data Analytics

Using Big Data to Move Beyond FICO and LTV for Loan Analytics

Posted by Paul Ablack on Dec 4, 2018 12:02:00 PM

The FICO score has a long and well-established history as a key metric in the determination of credit-worthiness. The FICO score has the power to influence whether a person can experience significant life events, like the purchase of their first car or home. Currently, it’s a major factor in credit union loan analytics.

However, as we rapidly enter the age of Big Data and loan analytics, does the FICO score utilize enough information to make an accurate determination of a borrower’s ability to pay? The wealth of data available to credit unions should augment their loan analytics.

A New Age of Loan Analytics

As I consider the future of credit unions, I believe the industry’s position on the significance of the FICO score in their underwriting process is an important issue. Is FICO a major determining factor, or is it merely one of many data points that can be used to predict probability of default for a given loan?

The mission of the credit union movement is to improve the lives of their members. While this is a very altruistic and admirable goal, it is only possible if credit unions can effectively assess and manage their loan portfolio risk. Current loan analytics strategies privilege the credit union over the member. At the end of the day, credit unions have a fiduciary responsibility to protect the assets entrusted to them by their members.

Credit unions are faced with delicately balancing two diametrically-opposed objectives when serving their members:

  1. Being more compassionate than the big banks when it comes to lending.
  2. Being “prudent,” as defined by NCUA guidelines, in their lending practices. For any loan application that is being processed by a credit union, the decision comes down to the FICO score and the Loan to Value (LTV), which is no different than the big banks.

Is there a better way to balance for loan analytics? The answer is a resounding, “yes.” Big Data and analytics is the new frontier for the retail lending industry.

If Others are Doing It…

Credit unions have access to volumes of internal data and the means to access external data. However, they lack the infrastructure and the culture to perform the loan analytics needed to improve their underwriting processes.

Expanded loan analytics platforms may have eluded credit unions, but others are leveraging more complete information. Lending Clubs are entering the retail lending market with lots of data (which credit unions also have) and loan analytics (an area where credit unions are behind the curve).

Read More

Topics: Lending, Insight Platform, Big Data

The Comfort of Data-Driven Analytics Decisions for Your Credit Union

Posted by Nate Wentzlaff on Nov 29, 2018 12:22:22 PM

As the next generation begins making financial decisions, credit unions will be able to comfort them with data- and analytics-driven product recommendations.

In the realm of financial institutions, the credit union still offers more than its competition. Whereas credit unions were hampered by limited technological options in the past, new developments in data collection, integrations, and analytics are helping them compete with banks.

Recently, my wife and I were shopping for a mattress. We began the process by “trying out” mattresses by how they felt. My wife thought she preferred firm mattresses, while I thought I preferred soft ones. As we tried mattress after mattress, my wife would ask me, “what do you think about this one,” to which I would usually reply, “It feels pretty good to me.” We became frustrated by our search until we found a mattress store that comforted us with data.

The mattress store (Becker Furniture World) is locally owned with only 8 locations (does this sound familiar to your credit union?). They approached mattress shopping from a data-driven way. By using an analytic data model (developed by Sleep to Live Institute), they are using analytics to aid customers in their mattress investments through data sensors and user input. The data comforted us enough that we decided to purchase one of the mattresses it recommended.

Data Acquisition from Users

When we walked into the Becker Furniture World, it was different than all the other mattress stores. There was a futuristic-looking canopy near the front of the store. We asked a store associate what the machine was and were informed that it collected data from our bodies and sleeping patterns to recommend the best mattresses. Before entering the contraption, we entered in personal data about ourselves using ranges for age, weight, and height, along with other qualitative data including where we currently have pain and our sleeping preferences.

After entering in our personal data, we both laid down on the bed (hooked up to data sensors). This data was then sent to the Sleep to Live’s data pool, and a report was printed for us. The report displayed statistics about us and recommended mattresses throughout the store.

Read More

Topics: Data-Driven, Analytic Data Model, Big Data