The Southbourne Tax Group: IRS Making Strides in Detecting Fraudulent Tax Returns Edit Title

The IRS has improved its identification of fraudulent tax returns that involve identity theft, but the agency needs to be more accurate in its identity theft estimates, according to a new report from the Treasury Inspector General for Tax Administration (TIGTA).

TIGTA performed an audit to determine how effective the IRS is at detecting and preventing identity theft. The watchdog also looked at how the IRS is measuring undetected identity theft and coordinating identity theft information with other agencies and tax industry partners.

TIGTA identified 568,329 undetected potentially bogus tax returns with refunds totaling more than $1.6 billion for tax year 2013. That’s a drop of more than $523 million from the prior year, the report states.

However, the false reporting of wages and withholding accounts for the largest amount of undetected potentially fraudulent refunds at $1.3 billion. TIGTA believes the new Jan. 31 deadline for employers to file their W-2 forms with the Social Security Administration will reduce this type of fraudulent return.

The new Jan. 31 filing deadline also applies to certain Forms 1099-MISC reporting nonemployee compensation, such as payments to independent contractors.

TIGTA also noted that using states’ lead data during tax return processing could improve detection of identity theft.

TIGTA also discovered that the accuracy of the Identity Theft Taxonomy quantification for both protected and unprotected revenue needs improvement. As an example, the IRS’s estimate of protected revenue was overstated by almost $2.4 billion as a result of the wrong calculation of refunds associated with rejected electronically filed tax returns.

The audit resulted in the following six recommendations, which the IRS agreed with:

  1. Expand the use of identity theft models to include all accelerated W-2s to compare with tax returns for possible identity theft.
  2. Develop criteria to identify and evaluate potential fraud in tax returns.
  3. Develop a way to use state lead data to help evaluate tax returns for identity theft.
  4. Use tax return data to find the refund amount associated with electronically filed tax returns that were rejected when computing revenues, leave out rejected returns that don’t claim a refund, and account for tax returns with multiple reasons for rejection.
  5. Review revenues to ensure that duplicate tax returns are omitted.
  6. Tax returns with mismatched income because of amended or duplicate income documents should not be considered for potential identity theft.

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