Getting High Yield Results from Credit Union Data: Part Two
Part One of our credit union data blog series outlined the value self-service data prep can bring to ATM settlements, accrued interest reports and certificate offerings. This second and final post in our Credit Union Data series continues with some additional use cases and their results.
Daily Teller Reports Many credit unions need to understand and gain insight from their Daily Teller Reports. With the help of a self-service data prep solution, one credit union is able to take a 1,500-page report containing thousands of transactions and summarize it by branch, member, or any other data type. By pulling out the transactions related to internal ATM fees, for example, they are able to easily match up those fees with the corresponding member and the correct branch, giving them very accurate revenue recognition detail.
Internal Auditing One credit union based in the southwest US is very active in car loans, home equity lines, credit cards, business loans and more, and views the internal auditing of its loan portfolio as critically important to the business. They use self-service data prep to make the internal auditing process faster and easier by focusing on analyzing actual risk levels for loans versus expected risk levels. To reach this level of analysis, the organization needs to unlock data trapped in a loan origination report provided by Symitar. (In the past, they were forced to print this report and manually enter the needed details.) By applying the data prep solution to the Symitar reports, they can simply open the report and visually mine the data they need.
Furthermore, they’re able to mine similar data from historical loan origination reports and put that data into a data table to be sorted, filtered or have new calculations applied to it. The credit union then uses the model’s summary capabilities to see subtotals and grand totals with many levels of detail. For example, they export the summaries into Excel spreadsheets and can then identify their top ten auto dealers and their top ten in-house loan products. The organization also can see the number of loans and loan amounts by loan officer, loan term, risk assignment, loan charge-off as bad debt, and more. Prior to using a self-service data prep tool, the credit union would receive some loan files with missing or erroneous data, or data not in compliance with loan standards. They would have to send the files back to the loan origination department for corrections, resulting in a backlog of internal auditing work.
Another credit union relies on a self-service data prep solution to extract data from its Maintenance Journal for auditing purposes. Their branches can flag data for transactions posted against dormant accounts or address changes, allowing the managers to ensure the transactions are valid.
Enhancing Member Service Given the competitive banking environment, many credit unions are focused on enhancing member service. One credit union uses a self-service data prep tool to ensure accurate payroll direct deposits for its Member Groups.
In the instances when payroll is not automatically posted due to changes in the payroll data transmitted to the credit union, the organization can identify the differences by extracting and summarizing the data from its Automated Clearinghouse (ACH) Unposted Item Report. This speeds up the proper posting of payroll to the right accounts, enabling the credit union to provide high-quality service to its customers.
With a self-service data prep solution, credit unions can better serve their customers by identifying anomalies that show up in different reports. One example is how a large credit union uses a data prep model to combine data from its Certegy Card Services reports and its in-house Symitar systems. In one specific case, many of the credit union’s member cards had incorrect expiration dates. Manually identifying which members were impacted by this error would have taken days of tedious searching. Instead, the model extracted the salient data from both reports and quickly produced a cross-referenced correction table to find every single erroneous card.
Given the displeasure many consumers have with the extremely large banks, there is a groundswell of growth in the credit union market. Many credit unions are turning to self-service data prep solutions not only to ensure timely and accurate management of all critical data, but also to maintain and enhance member services that will give them a competitive edge.
Daily Teller Reports Many credit unions need to understand and gain insight from their Daily Teller Reports. With the help of a self-service data prep solution, one credit union is able to take a 1,500-page report containing thousands of transactions and summarize it by branch, member, or any other data type. By pulling out the transactions related to internal ATM fees, for example, they are able to easily match up those fees with the corresponding member and the correct branch, giving them very accurate revenue recognition detail.
Internal Auditing One credit union based in the southwest US is very active in car loans, home equity lines, credit cards, business loans and more, and views the internal auditing of its loan portfolio as critically important to the business. They use self-service data prep to make the internal auditing process faster and easier by focusing on analyzing actual risk levels for loans versus expected risk levels. To reach this level of analysis, the organization needs to unlock data trapped in a loan origination report provided by Symitar. (In the past, they were forced to print this report and manually enter the needed details.) By applying the data prep solution to the Symitar reports, they can simply open the report and visually mine the data they need.
Furthermore, they’re able to mine similar data from historical loan origination reports and put that data into a data table to be sorted, filtered or have new calculations applied to it. The credit union then uses the model’s summary capabilities to see subtotals and grand totals with many levels of detail. For example, they export the summaries into Excel spreadsheets and can then identify their top ten auto dealers and their top ten in-house loan products. The organization also can see the number of loans and loan amounts by loan officer, loan term, risk assignment, loan charge-off as bad debt, and more. Prior to using a self-service data prep tool, the credit union would receive some loan files with missing or erroneous data, or data not in compliance with loan standards. They would have to send the files back to the loan origination department for corrections, resulting in a backlog of internal auditing work.
Another credit union relies on a self-service data prep solution to extract data from its Maintenance Journal for auditing purposes. Their branches can flag data for transactions posted against dormant accounts or address changes, allowing the managers to ensure the transactions are valid.
Enhancing Member Service Given the competitive banking environment, many credit unions are focused on enhancing member service. One credit union uses a self-service data prep tool to ensure accurate payroll direct deposits for its Member Groups.
In the instances when payroll is not automatically posted due to changes in the payroll data transmitted to the credit union, the organization can identify the differences by extracting and summarizing the data from its Automated Clearinghouse (ACH) Unposted Item Report. This speeds up the proper posting of payroll to the right accounts, enabling the credit union to provide high-quality service to its customers.
With a self-service data prep solution, credit unions can better serve their customers by identifying anomalies that show up in different reports. One example is how a large credit union uses a data prep model to combine data from its Certegy Card Services reports and its in-house Symitar systems. In one specific case, many of the credit union’s member cards had incorrect expiration dates. Manually identifying which members were impacted by this error would have taken days of tedious searching. Instead, the model extracted the salient data from both reports and quickly produced a cross-referenced correction table to find every single erroneous card.
Given the displeasure many consumers have with the extremely large banks, there is a groundswell of growth in the credit union market. Many credit unions are turning to self-service data prep solutions not only to ensure timely and accurate management of all critical data, but also to maintain and enhance member services that will give them a competitive edge.
