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with DataArc 360TM powered by Experian Pandora

Overview of Metro 2®

Created by the Consumer Data Industry Association (CDIA), Metro 2 is a standard electronic reporting format that data furnishers must adhere to when submitting consumer credit information to the credit bureaus. It is designed to help data contributors ensure they are providing accurate, quality consumer credit information and complying with the Fair Credit Reporting Act (FCRA).

Whether you’re a bank, credit union, credit card company, retailer, energy company, or other credit furnisher, your organization is obligated to report accurate consumer credit history. To streamline this process, the credit reporting industry has adopted Metro 2 which helps data providers submit a standardized data format, ensure accurate data reporting to credit agencies, and meet FCRA compliance requirements.

About DataArc 360

DataArc 360 is a credit reporting software that will help your organization adhere to the Metro 2 format. Powered by Experian Pandora, DataArc 360 is an innovative data management platform with pre-built rules to help data furnishers minimize disputes and support organizational data management processes.

Designed with the business user in mind, DataArc 360 empowers analysts to profile, transform, and manage consumer credit data quality over time. Data files are checked against the Metro 2 rules and DataArc 360 credit reporting software flags any discrepancies before they are submitted to the bureaus. With customizable dashboards and alerts, data furnishers are able to monitor their data and achieve accuracy in credit reporting. 

In this demonstration, I am going to take you through some of the capabilities of our Data Arc 360 platform powered by Experian Pandora. Often ingesting your metro 2 data we can evaluate these against one hundred and thirty-three pre-built rules provided by Experian. This looks at a proactive approach to understanding where you data quality issues reside before sending it out to the various bureaus. Another thing that we do recommend is running this against your full volume of data.

 

The rules themselves are essentially categorized into two primary categories. One is we look at the integrity of the key fields within your metro 2 file. This looks at the length, the formats, and the appropriate values for some of the key fields within here. The second category looks at logical conditions, taking multiple data elements, performing some type of calculation against it, and then scoring it to say if it is a pass or fail, a true or false.

 

Within the rules dashboard itself, we can set thresholds, so each of the rules themselves can be determined by the percentage that truly fails, truly pass, or ones that you want to keep an eye on. For any of those that do fail, alert systems are in place to send an email out to the data owner to enable them to look at the underlying data and identify where those data quality issues reside and do that root cause analysis.

 

The rules dashboard itself is interactive, so at any point, I can look at the overall scores and I can dig deeper into the rules that fail. One of the ones that come up quite often are centralized around social security numbers. We have a ruling here that looks at the integrity of social security numbers and identifies if there are any that pass or fail. For any rule that we have in here, we can drill down to the underline data and look at the records that have passed, the records that have failed, or we can also view them as changes over time, so as we're implementing more data we can see whether that score is getting better or getting worse and do some root cause analysis against it to find out why.

 

At any point, you can go to this particular rule, such as a social security number and see which rows have actually failed. Navigating to the underline data, we have techniques to quickly go to the fields that we are interested in such as a social security number and see why these have failed. We have things like repeating numbers, sequential numbers, or formats that just don't fit a required social security number.

 

One of the key features within here is that you do not have to start with the overall rules in the dashboard, we can actually work with the data itself and identify where any data quality issues reside. One of the ones that come into play quite often is date of births. So I can search my birth date and work with this field to get a better insight into any outliers that may exist within here, not with just the format but anything that resides outside of the norm for a birth date.

 

In this example, we've got the Metro 2 format of a date and let's say part of my curiosity, I want to see what that looks like as a true date. I can insert a column next to it and bring up our rule builder, search for any date functions that we have and actually perform a conversion. All of this functionality is driving drop functionality and we have over 500 functions to do something with the data. In this case, I want to search for my birth date and covert it to a date type. Once I have this, I can then evaluate whether these are accurate dates by inserting additional columns, I can add a validation to say is this a validating time or because we're dealing with birth dates, I can see whether this person is off the age range we need. Once we get the answers we want, we can quickly profile this information and see what that looks like across the board.

 

In this example, we can see that there are some issues. We have seven records in here that are not truly dates. They seem to have some alphabetic characters within them, but we can also highlight those that are under a certain age such as zero and eleven, or over a certain age such as one hundred and fifty-three. These outliers can be selected and we can drill down to the underlying data itself. Within here, there are a lot of capabilities to add notes to this issue that I have identified and can put a textual description within it. I can then add this as an attachment and assign it to an individual or a group to further investigate. This was a brief demonstration of some of the capabilities around Data Arc 360. Thank you.

Features of DataArc 360

Scalability

Profiles data against Metro 2 standards quickly, allowing you to browse through as many as 2 billion records on screen within seconds. 

Automated and comprehensive

With more than 115 custom, pre-built rules, DataArc 360 analyzes, corrects, and reports on your data automatically. 

Collaborative

Empower both business and technical users to proactively manage credit files with role-based security permissions. 

Issue management

Quickly identify and resolve errors in your data file prior to submission to the credit bureaus. 

Discover more about Metro 2 regulations

 

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