The Decision Maker

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

How Credit Unions Can Win the Big Data Play

Posted by CU 2.0 on Jul 2, 2018 12:47:00 PM

Ask executives at the money center banks how they plan to win, against both fintechs and smaller institutions like credit unions, and they smirk as they say two words, big data.

Big data is today’s magic.  How does Amazon knows what book you want to read next, or what music you want to buy, or when you are about to run out of cat treats? Those are simple examples but the answer is big data. Amazon crunches a lot of data, in a blink of an eye, and it knows what you want, maybe before you know.

The race now is on inside financial institutions to crunch lots of data and to achieve similar predictive intimacy about their customers and members. 

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Topics: Data Lake, Data Ownership, Big Data, Data Pool, Collaboration, Analytic Data Model

Data “De-Identification”: The Stairway to Big Data Heaven

Posted by Peter Keers, PMP on Feb 13, 2018 12:03:00 PM

Credit union interest in Big Data is at an all-time high. The promise of predictive analytics and other Big Data opportunities will be a key part of helping the industry compete more effectively with traditional banks and fintech upstarts.

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Topics: Encryption, Security, Collaboration, Big Data, Digital

Lagging Contenders: How Credit Unions Can Catch Up in Data and Analytics - Part 2

Posted by Peter Keers, PMP on Aug 15, 2017 10:16:00 AM

This is Part 2 of 2 in a blog series on how credit unions can catch up in data and analytics. In Part 1, we discussed which questions credit unions need to be asking to get off the bench, the issue with data silos, and what it will take to move forward with data analytics. In Part 2, we will further discuss the concept of big data, staffing for data analytics, and creating value from the data.

"A recent McKinsey & Company report emphasizes the fact that many industries are achieving only a fraction of their “digital potential”. However, the report observes, “In the United States, the information and communications technology sector, media, financial services, and professional services are surging ahead…”. This means other players in the marketplace served by credit unions have a big head start.

Credit unions that have been sitting on the sidelines can wait no longer. To get off the bench, these organizations need to ask:

  • What are the basic questions about the organization’s strategic direction that cannot be answered today?
  • How can existing data be better “generated, collected, and organized”?
  • What data outside the organization would be useful?
  • What skillsets are missing internally and to what degree can they (or should they) be outsourced?
  • Once “insights” are uncovered from analytics, what are the practical steps to leveraging them to create value?"
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Topics: Digital, Data, Big Data

Building for the Future

Posted by Michael Cochrum on Apr 5, 2017 11:15:00 AM

When my brother and I were kids, we liked to build things. We built forts, ramps and anything else we could fashion out of scrap wood.  Typically, our projects served a specific function, to ward off rival “street gangs” of preteens from another block, to propel our dirt bikes into the air, or whatever else we decided could or would result in our becoming temporarily disabled.  We thought we were good builders, but the greatest evidence that we were not, is that our work does not exist in any form today.

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Topics: FinTech, Big Data, Analytic Data Model

CU Analytics and Collaboration on CUbroadcast at CUNA GAC [VIDEO]

Posted by Mark Portz on Mar 21, 2017 12:02:00 PM

 

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Topics: AXFI Conference, Collaboration, Big Data

Big Data vs. Little Data: Part 2 - The Steps to Data Analytics Mastery

Posted by Austin Wentzlaff on Feb 14, 2017 11:03:00 AM

In my previous blog, Big Data vs. Little Data: Part 1 - Structured and Unstructured Data, I discussed the two main types of data that should be top of mind for any organization thinking of becoming truly “Data-Driven.” In the world of data and data analytics, credit unions must leverage ALL the data accessible to them but the journey of mastering data analytics can be very tricky.

Determining the right tools will be critical.  When it comes to data and data analytics, the order in which you introduce new tools is extremely important.  In order to make each step up the analytics curve effective as the last, credit unions must consider the following steps:

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Topics: Big Data, Data, Data Analytics

Top 5 (and 5 most missed) of 2016: Credit Union Big Data and Analytics – Part 2

Posted by Mark Portz on Jan 4, 2017 11:01:00 AM

As 2016 draws to a close, it is time to look back on all that we’ve learned, and apply it to better our organizations for 2017. The credit union industry is rapidly changing as financial institutions are gaining better understandings of the necessity to optimize analytics and become truly data-driven organizations. As 2016 comes to a close, I have taken the liberty of compiling some of the industry’s favorite Big Data & Analytics related articles (and some you may have missed) from OnApproach’s blog, The Decision Maker. This is part two of two and features the top 5 articles you may have missed in 2016. Click here to see Part One highlighting the most popular blogs of the year. Enjoy!

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Topics: Big Data, Data Analytics

Top 5 (and 5 most missed) of 2016: Credit Union Big Data and Analytics – Part 1

Posted by Mark Portz on Dec 29, 2016 12:39:55 PM

As 2016 draws to a close, it is time to look back on all that we’ve learned, and apply it to better our organizations for 2017. The credit union industry is rapidly changing as financial institutions are gaining better understandings of the necessity to optimize analytics and become truly data-driven organizations. As 2016 comes comes to an end, I have taken the liberty of compiling some of the industry’s favorite Big Data & Analytics related articles (and some you may have missed) from OnApproach’s blog, The Decision Maker. This is part one of two and features the most read pieces of 2016. Click here to see Part Two highlighting the some good reads you may have missed over the year. Enjoy!

 

The Top 5 Favorites:

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Topics: Big Data, Data Analytics