“[CUSOs] provide a means to an end – allowing credit unions the capability to fulfill the financial needs of their members in a cost effective environment through efficient delivery channels. Plus, they attract the brightest and most innovative minds to the board table, bringing best practices of credit unions across the country, which is a priceless experience.” – Doug Petersen President/CEO of Workers’ Credit Union.
Most credit union leaders are familiar with the concept of Big Data and business intelligence, but many fail to fully understand the significance they have on their credit union and its future. Big Data & Analytics can provide credit unions with the ability to make better decisions that positively affect member relationships and ultimately their top and bottom lines. There are several obstacles in the Big Data & Analytics process that need to be overcome in order to achieve success. These obstacles typically take an extensive amount of time to conquer, especially the first time they’re encountered. Credit union leaders should consider the following challenges before implementing a Big Data & Analytics solution:
The credit union industry is on the cusp of significant challenges with the potential to disrupt the financial services landscape as we know it. Big Data and Analytics is driving a new breed of competitor into what has been a very traditional marketplace. The industry will need to envision and build out the “Next Big Idea” for credit unions to stay competitive and successfully navigate the next 10 years.
Big Data and Analytics lessons for credit unions can come from some unlikely sources. Consider the contest between U.S. and European weather-prediction models. The European Centre for Medium-Range Weather Forecasts (ECMWF) is widely acknowledged to be superior to the U.S. Global Forecast System (GFS). While the GFS has been improved since 2012 when it predicted Hurricane Sandy would not make landfall, the European model is still considered to be the better weather forecasting tool.
OnApproach's Founder and CEO Paul Ablack discusses today's evolution of Big Data and how credit unions can benefit from this increasingly refined information to provide more specific products and services for enhanced value.
Topics: Reporting and Analytics, Analytics, Mobile Banking, Business Intelligence, Big Data, Credit Unions, Mobile Payments, Data warehouse, Data Integration, Marketing, Data Pool, Video, Mobile, Shared Applications, Big data/analytics, predictive analytics, Lending Clubs, Cooperation, Podcast
The Internet of Things (IoT) has gained a considerable amount of hype as the “Next Big Thing” to change the world as we know it. Applications of IoT are thought by some to be limited only by the human imagination. From simply controlling your home (e.g. - lights, thermostat, etc.) with a smartphone, to life saving medical and healthcare systems, IoT is pervasive and growing rapidly.
Millennials are living in a vastly different world than their Baby Boomer parents. They live in a time in which a phone isn’t just a piece of plastic used for making calls, it’s now “smart” and acts as an extension of oneself. A time in which “going shopping” or “depositing a check” no longer requires you to leave home. We are living in a world dominated by the rise of online/mobile and the demise of brick-and-mortar. This changing consumer landscape is being primarily driven by Millennials as they demand more personalized experiences.
Defining Millennials – Millennials (also known as the Millennial Generation or Generation Y) are the demographic cohort following Generation X. There are no precise dates when the generation starts and ends. Researchers and commentators use birth years ranging from the early 1980s to the early 2000s.
McKinsey & Company released a fascinating report in November 2014 titled The Bank of the Future.
The observations of author Somesh Khanna are very relevant to the credit union of the future with a few unique twists.
In order to continue thriving, the credit union industry must launch predictive analytics solutions to impact local initiatives
In his 2007 book, Competing on Analytics, Thomas Davenport explains how analytics solutions have been implemented to give competitive advantage to companies throughout various industries. The astronomical amount of data being collected and analyzed by large companies (financial and non-financial) threatens individual credit unions who currently rely on rearview reporting of historical data. Therefore, a holistic approach to data that leverages predictive analytics is the key to the success of credit unions. With predictive analytics, credit unions will be able to effectively cultivate the abundance of data available (from a variety of sources) to create innovate solutions that capture opportunities within their local community. By utilizing public data (e.g. IMF statistics) along with their private data (e.g. core systems), credit unions will truly be able to “think globally, act locally”.