In the first part of this blog, we learned about who Generation Z is. Now that we have a better understanding of who we are talking about and realize that we need to be prepared, let’s look at what it means for your financial institution.
OnApproach CEO, Paul Ablack, and Mike Lawson of CUbroadcast caught up at the NACUSO Network Conference in Orlando to discuss credit union industry trends and the upcoming 2017 AXFI Conference.
The interview covers credit union collaboration, fintech disuption, enterprise data integration, predictive analytics, data pooling, member experience, Identity and other exciting topics relevant to today's financial services industry.
In the sixth and final Data Analytics Series BIGcast, That’s a Wrap, John Best speaks with Paul Ablack, OnApproach CEO, about key themes and trends we are experiencing in the credit union industry, and how credit unions can get started with data and analytics.
There has been a major emphasis on making banking friendly for millennials. Of course, this is a necessity as millennials make up a larger percentage of the workforce and have different expectations for their financial institutions than previous generations. However, there are bigger changes to prepare for. If you are struggling to please millennials, a generation of adults who were impressed by the ability to send text messages and pictures as high schoolers, how will you be able to meet the needs of Generation Z – the generation who has been operating smart phones and tablets (and in some cases coding) before they could walk or talk?
Data continues to prove itself as a necessity for decision-making in financial institutions. For years, major banks and innovative companies such as Google and Amazon have taken advantage of “Big Data” to gain better insights into their customer base and make business decisions to position themselves for the future. The credit union industry is finally beginning to take advantage of their data and utilize new technologies. However, credit unions are much smaller than major banks and simply don’t have the same quantity of data that banks are able to collect from their customers. Fortunately, data pooling serves as a great solution to this problem. Here are 5 reasons your credit union should participate in data pooling:
In the fifth Data Analytics Series BIGcast, Sorting Socks: A Data Automation Conversation with Graham Goble, John Best speaks with Graham Goble of BankBI about financial performance management, reporting, and business intelligence.
The Excel Curse
One primary point of discussion during the podcast is about the use of spreadsheets in comparison to data automation. “The Excel Curse” is certainly not unique to credit unions, but it is absolutely a problem across the industry that requires action. Excel is a powerful tool and serves a number of purposes very well, but advanced analytics for financial institutions is not one of them. Even for a spreadsheet guru, there a several fatal flaws in using such a software as a primary reporting tool in credit unions:
In the fourth Data Analytics Series BIGcast, From Questions to Answers: Becoming a Data-Driven Organization, John Best speaks with Brewster Knowlton of The Knowlton Group about data-driven decisions, data warehousing and successful data integrations.
The Six Characteristics of a Data-Driven Organization
According to Brewster, there are six characteristics to determine whether your organization is really data-driven:
In the third Data Analytics Series BIGcast, The CECL Effect, John Best speaks with Joe Breeden of Deep Future Analytics about CECL, data pooling, and predictive analytics.
The Impact of CECL
As Joe Breeden explains in the podcast, CECL stands for Current Expected Credit Loss, and is the new accounting standard for how financial institutions will set loss reserves. Typically, organizations under $10 Billion assets have utilized moving averages to calculate loss reserves, but this model is backward-looking and will not be acceptable for the new regulations. A moving average model will always set your loss reserves too low moving into a recession and too high moving out of the recession.
When discussing how to meet CECL requirements and create a forward-looking model, Breeden states, “There is a lot of flexibility on how you implement it, but there are two things that are pretty much unavoidable”:
In the second episode of the BIGcast Data Analytics Series, John Best speaks with Blesson Abraham of SavvyIntel about credit union analytics insights, data pooling and optimization. The financial services industry is facing immense pressures, and credit unions cannot rely on the intuition or “how it has always been done” any longer. As John and Blesson discuss, utilizing big data and advanced analytics is no longer an option, but a necessity for credit unions.
You Have to Clean Up your Room
Analytics is a major point of discussion these days for credit unions. However, less frequently discussed, is the organization of the data required to perform analytics. Companies such as SavvyIntel have created incredible models which can be utilized at all credit unions. However, no model is ever going to work properly and provide truly actionable insights if the data isn’t clean and validated.