How to Manage Data Quality at Credit Unions and Banks
by Arkatechture, on July 15, 2019
Data quality cleanups as a whole have typically been treated as a 'one-time event' when there's a merger or core conversion - but we're helping Credit Unions & Banks turn those "fad diets" into "lifestyle changes" through ongoing accountability and automation.
Business Intelligence adoption is strong in the financial services sector, where approximately 76% of organizations use advanced analytics to improve workflows and enrich the account holder experience. However, a significant number of these companies struggle to manage the massive amounts of data being generated by their members interacting with their accounts to make deposits or take out new loans. This means that many of the banks, credit unions and other financial firms that have implemented business intelligence capabilities into their operations suffer from data accuracy and data quality problems.
These are significant issues that affect every aspect of the business, especially when executives begin to make decisions based on dirty data. Unfortunately, few forward-looking financial services companies have attempted to address such roadblocks due to the monumental task in front of them.
Here are four reasons why banks, credit unions, and other financial services firms should pursue process changes that improve data accuracy and quality at the source:
1. Improved Internal Operations
Operational improvement are one of the most common reasons for big data adoption among businesses in virtually all industries. However, as stated above, hoarding troves of data alone is not enough. Firms must be disciplined in their data management and governance practices to ensure that the information they generate is reliable. In the event that this information is questionable, significant delays and costly errors begin to occur.
Enterprises in the financial services space that emphasize data quality avoid these issues and develop truly streamlined workflows that bolster productivity and value-added activities.
2. Better Customer Service
It's no secret that customers maintain high standards when it comes to their privacy and personal information. Of the 40 percent of account holders who identify as digital-first customers, almost 80 percent are willing to submit personal information in exchange for tailored offerings, analysts for Accenture found.
This trust only extends so far, as virtually all of these customers are willing to take their business elsewhere in the event of data breaches resulting from poor data security. Banks, credit unions and other entities in the industry that do not focus on quality data often suffer from customer service mishaps. Sharing of private account information with the wrong person, for instance, breaks the trust of consumers and catalyzes attrition.
Financial services organizations willing to invest in systems designed to ensure data quality & security will build stronger customer bases in an ever-increasingly competitive landscape.
3. Fewer Compliance Issues
Despite the recent easing of financial regulations here in the U.S., most banks are still ramping up their compliance efforts. Many of these initiatives depend upon data infrastructure designed to reduce the manual labor needed to prepare reports for regulatory bodies here and abroad. Inaccurate data can hamstring organizations embarking on such efforts, leaving them with error-ridden databases that not only slow report preparation practices but also increase the risk of financial penalty. For example, many firms struggle to ensure payment data accuracy, International Banker reported. This puts these entities at great risk due to strengthened regulations related to customer privacy and money laundering prevention.
Banks and other financial institutions can steer clear of serious fines and extra regulatory scrutiny by implementing data management processes that boost information consistency, accuracy, and quality.
4. Better Long-Term Planning
In addition to boosting the business in the short term, proper data infrastructure can aid organizational planning activities and establish the foundation for long-term growth. With access to historical time-series data quantifying customer habits and behaviors over time, decision-makers can take advantage of advanced analytical methods, like predictive modeling or machine learning.
This type of long-term strategic planning can easily go astray if it's based on bad information stemming from poor data management. Organizations in the industry that take the issue of data quality to heart are able to develop company roadmaps that form the basis for future growth. With today's tight margins, having advanced analytical capabilities is becoming an increasingly significant factor and data quality is the foundation for making confident, data-driven business decisions.
Is your organization prepared to capture these benefits by bolstering the accuracy of the information flowing through your company? Connect with us here at Arkatechture for assistance with your future data quality projects.