The Decision Maker

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

In the Thick of Disruptive Innovation: Credit Unions and Collaborating for Analytics

Posted by Alex Beversdorf on Nov 6, 2018 11:06:00 AM

In the 3rd episode of the Data Lake BIGcast series, Disruptive Data, John Best speaks with Allied Solutions’ David Hilger and Michael Bryan, Senior Vice President and Head of Digital Strategy respectively. The discussion features a very interesting conversation with Allied Solutions about the drastic changes taking place in the financial services industry. They also discuss their partnership with OnApproach and how the implementation of a collaborative data lake for credit unions will completely change the landscape of the industry.

Technology-Driven Business

The worlds of business, financial services, and even our daily lives are constantly changing because of increasing technological capabilities. One major discussion point lately has been the introduction of autonomous vehicles. Leading the charge are large car manufacturers like GM, Ford, BMW (to name a few) and the well-known tech giant, Google. How would this change the insurance industry and how could big data and analytics impact this massive change? David Hilger explains:

Some say it’s a threat to the individual insurance market. At some point when everyone has an autonomous car, where does insurance fit into that? When people stop lending for cars then they will have to stop worrying about the insurance for the cars. The goal of our product is to make sure that the financial institution is protected if there is an accident. For autonomous cars, it depends on the ownership model. Predictive analytics could provide us with more insight. What type of cars, people, borrowers are a greater risk to the institution? Our client is the institution and that’s who we are trying to protect but there is a whole lot of other things that play into this. We don’t have the perfect insight, but we have the scale of data that can help achieve this.

Meeting Consumers’ Expectations: Not Easy but Essential

There have been successful and unsuccessful stories with the use of big data in the past decade or so. As discussed in the Disruptive Data podcast, a primary example is the clear difference in data integration capabilities across companies like Amazon, Netflix and Domino's, compared to the once promising Blockbuster. Being able to analyze and better understand where consumers’ tastes lie is very important to the financial viability of an organization and the community. Consumers have come to expect excellent, personalized and consistent customer service behavior from the merging companies like Amazon, Netflix and Domino's. Michael Bryan explains:

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Topics: Collaboration, Strategic Partnerships

A Credit Union Industry Data Lake: Gearing Up for the Future

Posted by Alex Beversdorf on Oct 30, 2018 11:07:00 AM

This week’s BIGcast brings in OnApproach’s CEO, Paul Ablack, to talk with John Best about his vision for the credit union industry. The second episode of the Data Lake BIGcast Series, Data Lake: What It Takes, discusses how the creation of an industry-standard data lake could completely revolutionize the credit union industry. Paul goes into depth on the capabilities of a data lake and how this transformation is not only beneficial for credit unions, but the entire community.

Laying the Groundwork for the Future

OnApproach is a credit union service organization (CUSO) that is focused on collaborative analytics for the credit union industry. OnApproach provides credit unions with a middleware solution for efficiently organizing and maximizing the full potential of that data. Paul goes on to explain:

Data is extremely valuable for all the initiatives underway. The big challenge for credit unions is the integration of that data and putting it to the correct use. Our mission is to bring all that data into one place and make it easily accessible for not just credit unions, but the industry as a whole. We call this the CU App Store, where app developers and vendors can come in and create an app that credit unions can use to perform analysis (i.e. models of attrition, outreach, and predictive analytics).

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Topics: Data Lake, Data Standards, Collaboration

Giving Credit Unions a Sustainable Competitive Advantage with an Industry Data Lake

Posted by Alex Beversdorf on Oct 22, 2018 12:03:00 PM

Tips to Secure Your Data Lake is the first of four episodes for the Data Lake BIGcast Series. John Best, the CEO of Best Innovation Group, brings in Rojin Nair, General Manager of Fintech Solutions at Celero to discuss data lakes and how a collaborative credit union data lake could revolutionize the industry. Celero is a well-established Canadian fintech company that provides a wide variety of services to the banking industry. By managing financial transaction processing and offering leading technology solutions, they successfully maintain over 80 credit union banking systems.

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Topics: Data Lake, Collaboration, Podcast

Recent Successes With Analytics Across the Credit Union Industry

Posted by Mark Portz on Oct 17, 2018 10:57:00 AM

As analytics continues to sweep the credit union industry, several OnApproach M360 clients are being recognized for their industry-leading data initiatives. Check out the sources below to see what credit unions around the country are doing to improve their organizations, members' lives, and communities with analytics. 

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Topics: Case Study

Summer Intern Perspective: Sports and Data Analytics

Posted by Nick Kerbeshian on Sep 25, 2018 11:10:00 AM

Being a summer intern here at OnApproach, I’ve had a different view on data analytics. It’s more than just looking at numbers and seeing trends. To be successful in data analytics, it is very important to have a clear vision and understanding of the industry that you are working in. From working in the industries of sports and credit unions I’ve found a commonality between them. The main goal between the two industries is to make the best decisions for their fans (sports), and members (credit unions). For both industries to achieve that, they need data analytics.

Money is not everything to most people, but it is possible that money is the most important asset that we need for our lives. The way you manage money will be a vital part of anyone’s life or business. I’ll take one of the biggest industries out there, sports, as an example.

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Topics: Data Analytics