Increased Reporting, Compliance May Call For Better, Cleaner Data At CUs
The Dodd-Frank Act promises sweeping changes across the financial services industry. Credit unions are waiting in anticipation to see what form these regulations will take and how they will affect an already shaken business model.
Even though the Act was signed into law on July 21, 2010, many of the changes have yet to be implemented, leaving credit unions trying to prepare for what will eventually come.
The purpose of these new regulations is to raise consumer financial protection in the wake of the financial crisis through increased transparency. New commissions have been created to monitor activities and enforce these new changes, but what does this mean for financial organizations?
One modification credit unions can expect is increased reporting in a variety of areas. In order to comply with these new requirements, credit unions will need to dramatically update their IT and database structures. In particular, credit unions will have to improve overall data quality in order to allow organizations to communicate accurately and confidently with regulators. This need for better data is especially true for contact data quality, which exists in every account for every member.
Common Contact Data Quality Errors
In order to prepare for the regulatory changes, credit unions need to improve their contact data quality. This data segments accounts, provides geographic location and supplies household information. One of the main reasons that financial institutions already maintain contact data is to comply with regulations, according to a recent Experian QAS study.
Often, systems are fraught with data errors. In fact, the same study mentioned above found that 90% of financial respondents do not completely trust their contact data in terms of it being completely clean, accurate and up to date.
Contact data should be corrected today so that credit unions can ensure the accuracy of their reporting to stay in compliance in the future. But before credit unions implement solutions to fix data quality problems, they first need to understand what common issues exist within their system.
Human error is the main contributor to contact data quality inaccuracies in a financial institution's database. This can be mis-keyed information or fields that may be left out all together. Frequent consolidation of data is a second factor that negatively impacts credit union's data quality. In the wake of the financial crisis, the industry has been flooded with a new stream of mergers and acquisitions. From a data perspective, this ultimately means that databases and accounts need to be combined, which frequently leads to duplicate information.
Organizations should review data entry points and existing information to see if human error and duplicate information plagues their own database. While every organization is different, these are some common data-quality errors that credit unions can assess as starting point for database projects.
Correcting Identified Problems
To correct the common errors mentioned above, credit unions need to prevent inadequate contact information from entering their systems while consolidating and verifying the account information that already exists. To accomplish these two tasks, organizations should make sure that automatic verification is in place to reduce the risk of human error.
Validation software-based tools should be put in place at each point of capture. These include places like branch locations, websites and call centers. In addition, back-end scrubs should be used on a continual basis to ensure formatting stays consistent and data is kept up to date.
Data consolidation is also a big factor in making sure contact information is accurate. Once the contact data has been properly formatted as mentioned above, unique identifiers used to determine duplicates can be chosen. These identifiers are often the name, address, telephone number, or e-mail address. Contact information is frequently chosen in this process, because it is repeatedly a consistent field across databases. Then software tools can identify duplicates based on the given criteria.
To make sure that these tools are right for each business, IT departments should spend some time analyzing data before selecting a solution. It will permit them to find common data errors and most frequently used information, allowing projects to be prioritized and solutions to be chosen based on proven needs rather than assumptions.
Easing The Regulatory Burden
Correcting contact information is an important step for any organization, but especially for credit unions. On average, financial respondents in the recent survey thought that as much as 21% of their total data might be inaccurate. By ensuring the accuracy of data now, financial institutions will have an easier time complying with new regulations around reporting in the future.
Having accurate, standardized data will allow financial institutions to search their databases more easily and have confidence in their data integrity. It is just one simple step in a long line of changes that will need to be made as the Dodd-Frank Act continues to be rolled out.
Thomas Schutz is SVP, General Manager with Experian QAS.