The Key to Great Analysis for Credit Unions
One of the roadblocks we often see with credit unions is that they do not have quality data that is necessary for the analysis process. The key is to incorporate data management strategies that ensure a credit union has quality data for analysis.
Data management and analysis is not contained in a single job function. As your credit union gets a handle on data management, mining and analysis, you will find that there are opportunities for individuals from varying disciplines to take part. Often, we want to intuitively go to IT for all data and reporting functions, but IT professionals may know very little about what you as a lender or marketer want to get from the data and reports.
Credit unions should develop a “Data Task Force” consisting of lending, marketing, sales, IT and operations individuals. Each member of the task force should come to the team with an idea of what data they are currently collecting and where that data is being stored. This will be essential when it comes to creating a common data warehouse. Most important, there should be an owner of this project in the credit union. That team member should head up the task force and document everyone’s data resources and needs.
The first initiative of the Data Task Force should be to determine what data is being collected today, where the data is being stored, the storage life of the storage facility and the accessibility of the data. For example, many lenders that use a Loan Origination System (LOS) collect information that does not get transferred to its core data processor when a loan is funded. Data stored in the core is often archived and becomes inaccessible after a short period of time. Even if your credit union has a data warehouse facility, it may be difficult for department leaders to access that data from that warehouse.
During the process of determining what data your credit union has available, it should also determine the quality and purity of that data. Using bad or incomplete data will be of little use to your credit union in the future. If data purity issues are discovered once inventory of available data has been completed and mapped, a project should be initiated to purify the data. It may seem time consuming at first, but once the project has been completed, the results will pay off in the long term.
A good example of a data purification project might be with real estate Loan-To-Value (LTV) ratios. Your credit union may not have originally stored each loan’s LTV in electronic form; as a result, when you visit your data storage facility, you’ll find that not all loans will have LTV’s available. A complete analysis is going to require filling in the gaps – even on loans that have already been paid or charged-off. If these values exist in a paper format only, your credit union will need to input them manually.
Once current data resources have been identified and purified, the task force should discuss opportunities for capturing new data types that haven’t been collected in the past. For some credit unions, this may be as simple as documenting an applicant’s time on the job or at their residence, identifying new risk attributes, such as dealer personnel in an indirect environment, or type of job or business for a person’s employment. Increasingly, these external factors have been identified as potential risk factors.
Two issues typically arise when considering new data opportunities: the temptation to collect all possible data; and determining where to put the collected data. When considering whether to collect all possible data, it is recommended that credit unions validate that each data point that they want to collect actually has a potential impact. In deciding where to house the collected data, it is recommended that credit unions avoid relying on the core data processor as the storage facility. Instead, credit unions should consider a simple data warehouse that consists of simple spreadsheets as a good place to start. In fact, storing AIRES files is a great first solution.
The reality is that financial institutions are increasingly relying on internal data to drive business decisions. To maintain a competitive edge, credit unions must employ effective data storage strategies.