The Decision Maker

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: 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.

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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.

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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." 

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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.

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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.

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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).

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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.

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

Making the World a More Predictable Place: Which CECL Model is Best for your Credit Union?

Posted by Alex Beversdorf on Nov 27, 2018 12:05:00 PM

 

Trouble playing the video above? Click here. from CUbroadcast on Vimeo.

Current Expected Credit Loss, or CECL, is an important upcoming accounting requirement that requires financial institutions to attempt to predict the expected losses on loans and other debt securities over the entire life of the loan. Large retailing banks and credit unions of all sizes can benefit from an accurate CECL model as both entities provide much of the same services to their customers and members, respectively.

The two main metrics you have to consider when choosing the right CECL model should be accuracy and procyclicality. If a loss model lacks accuracy and consistency, what’s the point of spending all that time, money, and effort in a meaningless implementation? A good CECL model will be adequately equipped to better track credit losses. There is a strong correlation between the credit cycle and the economic cycle. Models that account for implied volatility better estimate the timings and severity of economic recessions and manage to do so in a timely manner.

In the webinar, “Which CECL Model Should You Use”, Dr. Joseph Breeden, Chief Scientist and COO, at Deep Future Analytics and Prescient Models LLC, talks about the various types of CECL models. He clarifies the key differences between simple “spreadsheet” models and more advanced statistical models and how they can directly benefit credit unions with improved predictability.

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Topics: CECL, Lending, Video

Collaboration, CUSOs, and Credit Union Opportunities: Partnering for Advanced Analytics

Posted by Alex Beversdorf on Nov 13, 2018 1:30:45 PM

In the 4th and final Data Lake Series BIGcast, Your Data: The Ultimate Toy Box, John Best speaks with Karan Bhalla (CEO) and Suchit Shah (COO) of CU Rise Analytics. In this podcast they discuss how their strategic partnership with OnApproach came to be and how they have been continually shaping the collaborative nature in the credit union industry together.

Collaboration is Key for Credit Union

The banking industry is currently dominated by big commercial banks like JP Morgan Chase, Bank of America, and Wells Fargo (to name a few) with assets exceeding $1.5 trillion. Financial institutions this big, serving millions of customers per year, possess an unparalleled amount of data. Credit unions’ local and more communal feel have given them the reputation of being much smaller and “weaker” than commercial banks. However, there is one thing credit unions possess that differentiates them from their “stronger” counterparts, and that is collaboration. Karan Bhalla explains:

Every credit union that I’ve talked to, talks about collaboration. Every vendor I’ve talked to mentions it, but none of them really do anything about it. What I’ve found is that there is more of a competitive spirit within the vendors than there is collaborative. OnApproach is one of the only places that I’ve interacted with that is truly trying to create a collaborative workspace for credit unions... That is part of the reason why we’ve partnered with them. We want to be really collaborative and we want to bring the power of numbers/scale to credit unions. And that’s what really excites me about this [Caspian] Data Lake.

The relationship between OnApproach and CU Rise has proven to be a great start to the era of collaboration. They are currently laying the very complex road maps to allow for greater and greater collaboration and predictive analytics across the credit union movement.

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Topics: CUSO, Collaboration