The purpose of this guide is to supply the user with an explanation of 1) what makes this report different from a standard repository infile credit report, 2) what research information is obtained and returned to the user in the report, 3) what the research results mean and 4) how to use the data to underwrite an/or decline an application.

Introduction

Credit research reports have been developed to combat the current growing number of mortgage loans asked to be repurchased or indemnified based on fraudulent or misrepresented information. Out of this group of loans, an alarming percentage become foreclosures, a major loss in mortgage banking. Research reports assist mortgage lenders with identifying these high-risk loans prior to funding. The specific research information in the report can alert the user to fraud or misrepresentation in the credit file or the application. Based on this information, the underwriter has the ability to reject these loans when complying with the Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA).

The primary difference between a traditional infile credit report and a credit research report is in the word "research." Infile credit reports consist of the release of national repository database information that has been compiled using lenders and creditors input data about individual consumers. This release is based on a statistical match between the input applicant inquiry information and the credit file applicant information as reported by the lenders and the creditors. The biggest problem for underwriters is confirming that the file returned is complete and accurate. Due to the way data is held in and released from the repositories, it is possible for individuals to manipulate the data that is returned to a lender based on the applicant information they supply. Even when the underwriter suspects that there may be additional information, or that the information presented is not accurate, what does the underwriter do to clarify the situation? With a credit research report, the underwriter can be assured that every infile has been reviewed for certain file "flags" that indicate additional information may exist it that this information returned is inaccurate. The vast majority of credit research reports do not require additional research because these flags are not present. However, if flags are found, specific research measures are taken and their results incorporated in the report for the underwriters use. Armed with this information, an underwriter is able to identify misrepresentation or fraud in the application and had the data required to reject the loan while complying with ECOA and FCRA laws.

Through our experience with post closing reviews of repurchase requests, indemnification requests, early payment defaults and foreclosures; was have discovered that misrepresented and fraudulent loans have certain credit file characteristics or "flags" in common. These characteristics can be found in the raw credit files or system-to-system files returned by the repositories. Not all loans having these flags are misrepresented or fraudulent, yet by pulling the proper research tools, as done in post closing reviews of suspect loans, the results can be reviewed against the file information to discover loans that contain misrepresentations of fraud. Every research report is reviewed for ht e presence of "flags." If any of the flags are found then the research tools are used and their results along with the underwriter notes are incorporated into the credit research report before being released to the user. Whether the credit report requires research or not, credit research reports give the underwriter information that has been reviewed for problems and allows for more confidence in underwriting the application.

Procedures and Practices

The credit file is examined for the characteristics or the flags that can indicate a problem. If the flags are not present in the file, the credit report can be used as a normal infile credit report to underwrite the applicant’s application file. However, of any of the flags are present, the report will incorporate the tools below.

The three common flags that we have found in problem loans are 1) new credit files, 2) alerts in the national repository file and 3) inconsistent AKA/FKA information in the repository file:

New credit files – generally files which are less than three years old have a higher incidence of fraud and misrepresentation and require additional research.

Alerts in the national repository file – the repository may pass back alerts in the credit file that can give additional information. These alerts can indicate mismatches between the data in the repository file and the information requested by the mortgage company. Other alerts may warn of previous fraud involvement, social security number mismatches or file deviations that require investigation by the underwriter.

AKA and FKA information in the repository file – the repository may list more than one identity associated with the credit file holder returned by the inquiry "Also-Known-As" (AKA) or "Formerly-Known-As" (FKA) indicates that alternate identities may exist for the applicant. Based on the specific nature of the AKA/FKA information, a warning may be issue that additional or undisclosed credit may exist for the applicant.

If flags are found in the file during processing, the following research tolls are employed and their results are incorporated into the credit research report released to the user. These are the specific services accessed as required to complete the report:

SOCIAL SECURITY ISSUANCE DATA BASE (SSID) search – each social security number (SSN) is run against the SSID database and returned to determine the plausibility of the social security number claimed by the applicant. SSID is a database compiled from the Social Security Administration’s own updated records of the dates of issue and the state in which the social security numbers were issued.

TRANS UNION (TU) TRACE – Social Security number search – each subjects Social Security number is screened with a social search. Trans Union’s TRACE service uses the social security number supplied by the user to access TU’s national credit database. This returns what identities have established use of the Socail Security number and may have credit information associated with the subjuct SSN. Up to six identities can be returned. Each SSN has only one legitimate owner, therefore, if multiple distinct identities are using the same number, a problem exists in closing the loan.

TRANS UNION (TU) DEATH INDEX – each social security number is screened to determine if a death benefit claim has been filed with the Social security Administration on that number.

When a file exhibits the characteristic, a series of underwriter notes and/or warnings are returned in the report to give the underwriter guidance in reviewing the file. Examples of the notes and reports will be discussed at the meeting. With these research tools, the user can make lending decisions that benefit the company and decline and/or properly document those loans that will result in losses through the repurchase or indemnification.