About us...

Founded in 2002, Automated Auditors, LLC has been providing data mining solutions for companies for several years.  Our staff has worked with telecom giants such as MCI, creating duplicate payment detection algorithms in conjunction with the Internal Auditor.  We harness the power of data mining and apply it to a wide variety of projects, including data cleansing projects, address matching projects, A/P recovery audits, and A/P fraud investigations.

In the news...


  • January, 2007:  Automated Auditors teams with A/P recovery expert Recovery+Solutions (www.recovery-solutions.net) on major audits to offer clients the best in electronic and manual A/P auditing.
  • August, 2006:  "Welcome to a Fuzzy World" article was published in Fraud Magazine August 2006 issue.  Written by president Christy Warner and colleague CFE fraud expert Bruce Dubinsky (www.dubinskyco.com).  Please use the contact us page for a copy of this article.
  • October, 2005: President Christy Warner spoke at the Washington, DC ACFE chapter meeting on October 3. Her presentation titled "10 Ways to Identify A/P Fraud" was delivered to approximately 30 Certified Fraud Examiners that reside in the Washington, DC area. For a copy of the presentation, please use the Contact Us page.
  • October, 2005: President Christy Warner addressed SAS users at the South Central SAS Users Group conference in San Antonio, TX on October 17th. Her presentation was titled "Using SAS to Identify Duplicate Payments, Duplicate Vendors, and A/P Fraud".
  • April, 2005: President Christy Warner recently spoke about detecting Medicare fraud at the SAS Users Group International conference in Phildelphia, PA. For a copy of her paper titled "Automating Predictive Analysis to Detect Medicare Fraud", please use the contact us page.

    How we've helped our clients...

    Data Cleansing

    When the World Bank needed someone to clean and de-dupe their 300,000+ record vendor database, they turned to Automated Auditors, LLC. The Procurement department submitted an RFP for the project, which Automated Auditors won in conjunction with Recovery+Solutions (strategic partner), beating out the large-company competition such as EDS and IBM. For a reasonable price, Automated Auditors identified duplicate vendor records by name, address, phone, bank account number, and delivered all results in an Access database, along with a comprehensive report describing the duplicates found and the reasons behind their formation. This task involved a great deal of fuzzy-matching, because international address often lack the standardization found in U.S. addresses, thereby creating the need for phrase-matching. Our analysts created customized software to identify all duplicate records and link them together in "vendor families", giving the World Bank a database that logically tied all related records together.

    A/P Fraud Detection

    We conducted a duplicate payment audit for a medium-sized health product manufacturer. During the audit, we identified several above-average payments for an employee. After bringing it to the Controller's attention, we learned that several employees had already been fired for stealing checks. What the Controller didn't know was that these checks were written to the employee for over $40,000 each (3 of them + other checks). Our staff was able to identify this fraud because we read in the client's CHECK REGISTER file, not their A/P file. The fraudulent checks had no invoice number, which probably would have been rejected in their A/P system. Because our staff can read "messy" data such as Check Register data, we were able to detect the fraud.

    Duplicate payment detection

    Our analysts helped MCI Telecommunications identify millions in duplicate invoices. We developed a customized software solution for this client so that they could detect duplicate payments on an as-needed basis without referring to a collection agency. Because of the dupe payment detection tool, they were able to keep their profit recovery initiative discreet - a must in this age of accounting scandals.

    We recently conducted a duplicate payment audit for a health product manufacturer and successfully identified $93,000 in duplicate payments.

    Statistical Analysis: Tax Shelter Lawsuit Support

    One of our clients, Dubinsky & Co. (www.dubinskyco.) is a leading forensic accounting firm in Bethesda, Maryland. In support of a high-profile tax shelter lawsuit, we used SAS to determine the probability that customers seeking a legitimate tax shelter would actually see a profit on the tax shelter scheme. The effort involved customized SAS programming and the use of statistical binomial probability calculations.

    Vendor Fraud: Benford's Law Analysis

    We used our proprietary software, A/P Fraud Detector, to identify unusual invoice activity for one of our clients, Investigative and Forensic Accounting Services (www.ifas.cc). Using Benford's Law of Numbers, we identified several vendors with unusual invoice activity. These vendors were then flagged for further investigation.

    Accounting Software: Development

    Our staff developed a Microsoft Access software solution for Sensormatic Security Corporation (SSC) that enabled their Controller to query their Accounts Receivable payment detail files. In addition, our analysts supported SSC in an ongoing lawsuit with their parent company, Tyco, by analyzing SAS programs. Staff analyzed the SAS programs to gain an in-depth understanding of how Tyco was (or was not) paying SSC correctly for sales in the Maryland/Virginia/DC region.

    Prediction Analysis: Loan Consolidation

    As a consultant to Caliber Associates (www.calib.com), our staff completed a study for the Department of Education's Cost Estimation and Analysis Division, the goal of which was to predict when students consolidate their loans. Using SAS's survival analysis module, our staff constructed a database depicting each loan's status for every month during the analysis period. Several influencing factors were considered such as loan type, time in default, interest rates at the time, number of loans the borrower had at the time, total loan amount, and others.
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