Use of Computer Automation in Preference and Fraudulent Conveyance Actions
One of the challenges faced by professionals in a bankruptcy case is the review of a debtor's records to identify potential causes of action for avoidance cases such as preferential or fraudulent transfers. Voluminous records need to be reviewed in a timely, cost-effective manner. Cost-effectiveness is a significant issue because the analysis of a debtor's records may or may not result in an economic benefit to the estate. Conversely, a thorough examination often yields substantial benefits to the estate, its professionals and the estate's creditors.
New technologies can be utilized in the search for preferential and fraudulent transfers. The purpose of this article is to provide an overview of technical, logical applications for preferential and fraudulent transfer matters and their ultimate use to create compelling, dramatic trial exhibits including charts and graphs.
Most preference actions, and some fraudulent conveyance actions, can be identified and prosecuted more effectively if the process of tabulating and analyzing paid vendor history files is automated.
Vendor invoice and related payment information can be tabulated and analyzed with the use of sophisticated computer databases. Once the information is in a database format, various analyses, including analysis graphs,1 can be performed quickly and inexpensively. For many debtors that have computerized accounting systems, much or all of the information is already on magnetic media (disks and tape), so data entry can be minimized or eliminated. Databases with vendor payment information can be used to prepare trial exhibits including trial graphs and new value tables.
Automating Vendor Invoice and Payment History
In order to understand how databases can be utilized in bankruptcy matters, professionals should expand their way of thinking about debtor vendor payment records.
For instance, expand the concept of "documents" to include information on computerized magnetic media generated by the debtor's accounting system. Think of each document as being comprised of distinct pieces of information that can be automated and analyzed efficiently. For example, each paid vendor invoice includes the following discreet pieces of information: vendor name, invoice date, invoice number, invoice amount, check number and check date. Bank statements include information regarding cleared check information. The vendor invoice, check and bank statement information can be combined and eletronically or manually entered in a computerized database for analysis. Automated and manual data entry is discussed in more detail later in this article.
Databases are comprised of "records" and "fields." In the context of vendor history information, each record is comprised of the vendor invoice and related payment information. The fields making up each record include:
- vendor name;
- invoice number;
- invoice date;
- invoice amount;
- check number;
- payment amount;
- check date;
- date check cleared; and
- payment type (computer-generated check, manual check or cashier's check).
From the invoice and check information above, additional fields can be computed for each record, including:
- number of days between invoice date and check date;
- number of days between invoice date and check cleared date; and
- number of days between check cleared date and check date.
Once the database has been completed, countless summary statistics, listings and graphs can be prepared (usually in total, by week and/or by month). Many of the reports can be prepared literally in a matter of minutes, including:
- invoice amounts, by vendor;
- payment amounts, by vendor;
- total unpaid invoices, by vendor;
- average number of days between invoice date and check date;
- average number of days between invoice date and check cleared date;
- average number of days between check cleared date and check date; and
- difference between the check amount according to the debtor's accounting system and the actual check amount according to the bank statement.
Once the database and related analyses have been completed, trial exhibits can be prepared easily, including graphs and schedules. Exhibit A is an example of a graph that was prepared in a preference case. Note the beginning of the preference period indicated by the arrow and text box on the bottom right corner of the graph. Even without a complete understanding of the underlying data, it's intuitively clear from the graph that something dramatic happened during the preference period. The lawyer and expert in this case referred to this graph as their "scud missile."
Methods of Input
There are four methods of entering data in a database, namely:
- electronic data entry (sometimes referred to as importing, exporting or downloading);
- manual input; and
- combination of electronic and manual input.
Electronic data entry involves extracting vendor invoice and payment history information from a debtor's computerized accounting system. Particularly for large amounts of data, electronic data entry is the most efficient method. In cases where the debtor's accounting system is designed to export data to conventional, external databases, electronic data can be done quickly and inexpensively. However, many accounting systems do not include this feature, in which case the data must be converted from its native format to a conventional database format.
Once preference and fraudulent conveyance actions have commenced, automation can be used during the negotiation and trial stages of a case...
File conversions are, in varying degrees, tricky. As a result, professionals should be used with specific experience in database applications (flat file and relational databases), data conversion and bankruptcy consulting. Before embarking on electronic data entry where file conversions are involved, some preliminary work should be done to determine the complexity and cost of electronic data entry, and a comparison of those costs to other forms of data entry (discussed below). Unfortunately, there is a tendency for attorneys and consultants to quickly jump to the conclusion that electronic data entry is faster, less expensive and more accurate than manual data entry, which is not always the case.
If legible, well-organized reports are available, they may be scanned for database input. Most attorneys have heard about document scanning, and some attorneys have used it for large cases. It is important to recognize that there are two kinds of scanning applications: imaging and optical character recognition (OCR). Imaging involves taking an electronic "picture" of a document, which is sometimes referred to as a graphics file. Graphics files are not text searchable. OCR technology involves a process by which each letter in the document is individually recorded on magnetic media in a text file, which is text searchable. WordPerfect documents are an example of one type of text files.
Most document management applications in litigation are imaging applications. However, scanning for the types of databases illustrated in this article requires OCR technology. Currently, OCR is more expensive than imaging. For highly legible documents, OCR technology generally has a low error rate, but it is not 100 percent accurate. As a result, quality control procedures should be carefully considered and implemented to insure the accuracy of the data.
For large amounts of data, manual input is probably the least desirable of the data entry alternatives. Unfortunately, it is the only option in many cases. Personnel should be carefully selected and supervised.
Frequently some, but not all, of the required information is available from a debtor's computerized accounting system. For example, a unique invoice number is required by most modern, computerized accounts payable systems as a control to avoid duplicate payments and clearly identify the source document with its electronic record equivalent. Even when a number does not exist on the vendor invoice, one must be entered. In such cases, companies develop a protocol to assign a unique number to unnumbered documents, such as date/text combinations. Unfortunately, some older accounting systems and increasingly popular "low-end" accounting software systems do not require an invoice number as an internal control to prevent duplicate payments. Ironically, the absence of controls from the low-end packages is one of the features (or more accurately, the absence of features) that make so called "low-end" accounting systems easier to learn and use than full-featured accounting systems.
In cases where the debtor's files on magnetic media are incomplete, careful consideration must be given to whether the missing information is "fatal." If the decision is made that the missing information is required, it must be entered manually from the source documents. This usually involves more than manual data entry. It requires a carefully planned and executed strategy for gathering the data from the source documents and recording it on forms that can be efficiently and accurately key-entered.
Costs and Limitations
If the vendor invoice and payment information can be seamlessly extracted electronically from a debtor's computerized accounting system to generic, mainstream databases, computerized databases can be used in all but the smallest cases. Unfortunately, most current accounting systems are not designed to seamlessly export data to generic, mainstream databases, although this may change as accounting systems continue to evolve.
For now, however, the practical reality is that cost considerations make database applications available to medium and large bankruptcies. Consultant fees can start at about $5,000 for a simple database (including the reports and graphs). Costs will depend upon a number of factors, including the amount of data, completeness and availability of the data, and the condition of the debtor's records.
Computer technology, including databases and graphics, can be an excellent, cost-effective resource for financial professionals and their work in connection with avoidance actions. These resources should be used in the early stages of a case to tabulate and analyze vendor invoice and payment histories so possible preference and fraudulent conveyance targets can be identified. Information compiled can be assessed repeatedly for any number of reasons as well.
Once preference and fraudulent conveyance actions have commenced, automation can be used during the negotiation and trial stages of a case, primarily in the form of data analysis and demonstrative exhibits (tables and graphs), achieving high levels of performance with added flexibility and cost-effectiveness.