A common language forms a bond between humans and strengthens their ability to cooperate. Credit unions must agree on common semantics to establish powerful business analytics throughout the industry.
Credit unions need to connect data that will breakdown silos and create a member experience that is beyond anything that currently exists.
But how and who is accomplishing this effectively?
Look no further than the fitness app loaded on your smartphone.
Fitness/health apps have become masterful at creating impactful user experiences that provide detailed and precise information to the company. The end result is predictive and prescriptive product offerings that create personalized user experience that includes trust, retention and advocacy.
Here is an example to demonstrate.
Credit unions are often plagued by their data being locked up in the core processor and other standalone systems. Increasingly, they are adopting data warehouse solutions to free and integrate this data so cutting edge analytical tools can be employed to solve tough business problems.
However, a new problem then becomes apparent. Once business users realize long-imprisoned data is now liberated, they clamor for information. This puts pressure on the IT department to meet the growing demand for reporting and analytics.
It’s clear now: Data can be one of a company’s most valuable assets if properly stored, managed and analyzed. What’s unclear to many however, is what data is the most valuable and how to harness the value of each type of data. There are two main types of data: “Big Data” and “Little Data” or, respectively, unstructured data and structured data. Both types of data can deliver a significant amount of value to a credit union. However, figuring out how to harness each type of data can be a challenge when dealing with the array of different data sources. Finding a healthy balance is key to delivering value without succumbing to analysis paralysis.
1. Your Members are not just Numbers, but Numbers Help You Understand Your Members
The best way to build strong relationships with your member is to know your member. Learn their habits, needs, and patterns. In the same way that you can learn more from a video than a snapshot, you can learn more about a person using data over time and analyzing transactional data behavior.
Topics: AXFI Conference
Collateral Valuations are essential while serving members and maintaining a healthy credit union. However, credit unions are relying on inaccurate valuations of their members’ collateral values because of disintegrated data.
Based on a presentation given by Dr. Joseph Breeden, CEO of Prescient Models LLC., at the 3rd Annual Analytics and Financial Innovation Conference (AXFI).
The Financial Accounting Standards Board (FASB) announced the final standard for the Current Expected Credit Loss, or CECL, on June 16, 2016. Thus far, banks and credit unions have only been required to estimate and report potential losses for the next 12 months on loans and leases. This new regulation, however, will require financial institutions to predict and report potential losses for the entire lifetime of a loan.
What it means for your Credit Union or Bank
The financial services industry is being constantly challenged with new regulations and outside threats. Two important developments in the financial world recently that are worth noting are the immense growth in blockchain technology utilization, as well as the impact of the Financial Accounting Standards Board’s (FASB) recent comments on Current Expected Credit Loss (CECL) guidelines. These are both undoubtedly items to keep top-of-mind, as they are impacting institutions from community banks and credit unions to the large banks.
A common misunderstanding with data analytics is how and when the various “tools” are used. Many think that a great data visualization tool (e.g. Tableau) will solve all of an organization’s problems. Often overlooked, however, are the many steps it takes for an organization to get from data ground zero to becoming completely analytically proficient. When it comes to data management and 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: