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.

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Topics: Credit Unions, Data Integration, Insight Platform

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.

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

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, Data Analytics, Insight Platform

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: Big Data, Insight Platform, Lending

Taking Data Analysis at Credit Unions to Another Level with Collaborative Analytics [Video]

Posted by Mark Portz on Sep 28, 2017 1:47:41 PM

  

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Topics: Video, Collaboration, Insight Platform, Digital, Data Ownership

CuOS – A Platform for Credit Unions Similar to Apple iOS

Posted by Austin Wentzlaff on Jul 31, 2017 9:02:00 AM

Apple has made a tremendously successful company off of one thing… Is it the iPhone, iPad, iPod, or Mac series? No.  What makes Apple so powerful and successful is not its products, but rather the ecosystem it’s created through its standard operating system, the iOS.  An operating system or “platform” that enables its users to connect with the rest of its users as a community and its developers.  With this common platform, all users are on a level playing field with a similar access to all “apps” and services that have been created on the platform – rather than each user building everything themselves.

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Topics: Data Analytics, Collaboration, CU Analytics Ecosystem, Insight Platform

Credit Union Cooperation: Google Maps Style

Posted by Nate Wentzlaff on Feb 7, 2017 12:02:00 PM

As credit unions begin their journey into the future, they must rely on an industry standard analytics platform to guide them to their destinations.

Google Maps has revolutionized how we navigate our lives. It saves us from headaches caused by unnecessary traffic and other challenges in traveling. My journey from work to home has many different routes depending on traffic patterns. During days with slower traffic (i.e. - winter snowstorms), the Google Maps recommended route will change every 5 – 10 minutes. Using an analytics engine that informs me of the best route allows me to spend extra time on more important things in life. Credit unions have a similar opportunity when navigating their institutions into the uncertain future of financial services. Establishing an industry standard analytics platform will enable credit unions to cooperate on analytics and guide them to their desired destinations.

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Topics: Data Integration, Data Pool, Data Analytics, Collaboration, Data Visualization, Insight Platform