The Trellance Data Blog

In the first part of this blog, we discussed technology regulation and updates regarding the legislation. With that covered, Part II will focus on what it means for your credit union and how you can prepare for the changes. 

How the CCPA Will Change the Competitive Landscape in the US

The CCPA won’t apply to all companies but will apply to a great majority, especially if one of these three thresholds are met:

• Gross annual revenues in excess of $25 million
• Buys, receives, sells or shares the PII of 50,000 or more consumers, households or devices for the business’s commercial purpose
• 50% or more of the businesses annual revenue comes from selling consumer’s PII

If any of the above conditions are met, the marketers of the effected company have a great deal of work to do. Especially if they have no business tactic or strategy in place to organize all of their customer specific data. To comply with the CCPA, marketers must be able to organize and develop an efficient data scheme that compiles all of their consumer data. Consumers have the right to:

• Know what PII is being collected regarding them
• Know whether that is being sold and to whom
• Say no to the sale of their PII
• Access their own PII
• Equal service and pay from the company, even if they exercise their own privacy rights and it requires more work to be done on the side of the business

We are in the era of digital transformation. A time where data is being collected at exponentially growing rates all around us. A wide variety of businesses and institutions, including credit unions, have been collecting personal data on their customers/members for quite some time now. How can you receive any benefit from all of this available data? The answer lies in the use of Big Data Analytics. Big Data is used to help analyze extremely large data sets to identify patterns, trends and associations in human behavior. This method of analyzing data is very versatile and proving to become one of the most sought-after tools of today.

This has made the overall collection of consumers data much easier and more widespread. This is also commonly referred to as Personally Identifiable Information (PII) and the influx of access to it has raised some concerns. Common questions include: What data is being collected? How has it been collected? What is it being used for? Which third parties have access to it? And, how much control do we have over our own data?

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.

In the 4^th 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.

In the 3^rd 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:

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).

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.