Streamlining Financial Disputes and Investigations

In navigating the intricate terrain of financial disputes and investigations, choosing the appropriate software tools marks the pivotal starting point.

In navigating the intricate terrain of financial disputes and investigations, choosing the appropriate software tools marks the pivotal starting point. This choice relies heavily on the diverse types and formats of documents at play, each presenting its own set of challenges.

Outlined in the following seven steps is a roadmap to streamline the financial disputes and investigations process. By facilitating a more efficient data extraction process, these steps empower professionals to channel their time and effort into analysis, ultimately compressing the time required to reach informed opinions.

Step 1: Determine Software Needed Based on Document Type

Hard Copies:

Transforming hard copy documents into digital format requires careful organization to avoid data extraction complexities. Organizing documents by account and statement period before scanning streamlines subsequent procedures. Regardless of quality, the documents need to be digitized. The good news is that most modern printers also offer scanning and copying capabilities and can therefore be used to digitize the hard copies into PDF files.

Scanned Images:

In many instances, PDF documents may already be available, or the financial institution may provide the files to you in a PDF format. Before beginning data extraction, use a PDF or image editor to split the document into separate files with each file containing information for a single account. Be sure to pay attention to pages that may contain data from two different accounts to ensure data extraction is not duplicated.

Native PDF Files:

Native PDF files are the easiest to work with. Many tools exist to extract data and error rates are extremely low. However, even with the ease and organization that comes with the native PDF format, the more organized you can be pre and post extraction, the more time you can spend on high-value analysis activities.

TIFF / Other Document Types:

On occasion, financial institutions may provide transaction lists via XLSX, CSV, or TXT/JSON among others. Excel handles dozens of file types and is very useful. To make integration easier, consider using a software database solution that allows uploads as CSV or other text file formats.

Step 2: Review and Organize All Documents

Thoroughly reviewing and organizing documents provides a clear overview, facilitating subsequent analysis. Listing all accounts and relevant data fields streamlines the process. Consider including the following data fields for each account:

  • Legal Entity
  • Financial Institution
  • Currency
  • Account Number
  • Account Name
  • Account Type
  • Description

Step 3: Load and Extract Data

After your documents are reviewed, organized into files, and accounts are listed, you are ready to begin to load or extract data into a database. The following best practices are designed to help ensure the resulting data is completely accurate before you begin your analysis.

Bank Statements:

Modern solutions that use AI to recognize text strings extracted by OCR technology eliminate the need to develop templates for each statement format. Also, they simplify large single files that contain dozens of accounts and hundreds of periods by identifying the individual account periods and additional fields like balances and period dates. Seek out more advanced software that goes beyond being just a pass-through and helps streamline the process of extracting text from scanned documents or image-based files and integrating it seamlessly into a centralized database or master data set.

Brokerage Statements:

In many cases, analysis includes brokerage accounts which can make data extraction especially complicated. Most specialized OCR tools for statements do not support brokerage accounts as they focus on banking and credit card statements. Implement a strategy that combines manual verification and transcription with advanced OCR tools that support brokerage account statements, particularly those capable of extracting cash in and cash out transactions. Conduct regular manual checks to ensure the accuracy and completeness of extracted data.

Check and Deposit Slip Images:

If required, subpoena wire transaction details, and/or check and deposit slip images from each financial institution. The challenge is that these details will likely come in multiple formats, including data and image files. There are two key capabilities required to integrate these important details with ease:

  • The ability to extract transaction information from both printed and handwritten check and deposit slip images.
  • The ability to integrate details from the extracted check or deposit slip images and wire transaction details with the appropriate bank statement transactions.

Often, this is the most difficult and the most crucial step as wire details and checks contain information about where the money went and where it came from.

Step 4: Reconcile Data

Reconciling data involves comparing and aligning different sets of data to ensure accuracy, consistency, and integrity. This process is essential for identifying discrepancies, errors, or anomalies within the extracted data and resolving any inconsistencies before proceeding with further analysis or investigation.

Identify Account Periods:

Use software solutions that identify individual account periods in PDF/image files and extract period balances and dates in addition to transactions. The right software solution can also check to make sure that the beginning balance (plus inflows minus outflows) equals ending balance for each account period.

Find and Fix Data Errors:

No matter how data gets extracted from documents, errors are likely. Before integrating new data into an existing master file or database, be sure to address all extraction errors and check for duplicate statement files and account periods. This proactive approach helps maintain the accuracy and integrity of your data repository, minimizing errors and streamlining future analysis processes.

Load or Integrate Data with Master Copy:

After extracted data is confirmed as 100% accurate with no duplicate transactions, load the data

into the master database or integrate with the master file. This is also the time to match check

and deposit slip images to relevant bank statement transactions.

Identify Missing Data (and Repeat):

Examine all data and accounts thoroughly to determine the existing data timeline. This is the time to identify any inconsistencies or gaps that may indicate missing data. Use software to map out each account period in a visual timeline that is updated in real time as data is added. This way, all users can visually identify gaps and check data set status in real time.

Step 5: Primary Analysis

At this point, the transaction database is a clean foundation or starting point. Keep in mind, more work may be required before analysis can start, especially if wires, checks, or deposit slips were used to initiate banking transactions.

Categorize and Group Transactions:

Each engagement is different and requires different categories. The amount of transactions can be vast. Reading and categorizing each one can take a long time. A hybrid approach where a master database is integrated with spreadsheet software provides the best of both worlds. Users can leverage the efficiency of data input via a spreadsheet while maintaining a single, version-controlled master database. Look for solutions that have tight integration with spreadsheet products.

Identify Transfers:

Using spreadsheets to find transfers between accounts and legal entities is complicated at best. Considering date ranges and same day, same dollar amount scenarios, it can be almost impossible to get a comprehensive view of all transfers. Software solutions that identify and match transfers and allow users to confirm or reject matched pairs is more effective and efficient than using a spreadsheet.

Identify Undisclosed Accounts (and Repeat):

Undisclosed accounts are easily identified if all transfers have been properly categorized and each transfer between accounts has been identified. However, Integrating various spreadsheets and using VLOOKUPS can be complicated and cumbersome. Results can be difficult to understand, especially if used as a court exhibit. Data visualizations simplify and accelerate the process. See the screen shot example below where transfers out of the dataset are clearly identified.

Step 6: Additional Analytics

Since every case is unique with its own needs, it's important to adapt accordingly. In this section, we'll cover some common types of analysis used for disputes and investigations.

Threshold Analysis:

If the client is cautious about spending or uncertain about the need for a detailed investigation, a threshold analysis can be helpful. This involves creating a table that categorizes transactions based on their amounts and counts. For instance, clients can assist in streamlining the workload by focusing only on transactions above a certain value threshold, making the task more manageable.

Even $ Check Analysis:

Even dollar amount transactions can be interesting to forensic accountants because they may indicate patterns or behaviors that warrant further investigation. In forensic accounting, the key is to identify anomalies or patterns that deviate from normal business practices. Even dollar amount transactions are just one of many potential indicators that forensic accountants consider during their investigations. Analyzing financial data in detail allows them to uncover discrepancies and potential fraudulent activities.

Benford’s Law:

Forensic accountants use Benford's Law as a tool to detect anomalies and potential irregularities in sets of numerical data, including financial data. Benford's Law, also known as the first-digit law, states that in many naturally occurring datasets, the leading digits are not uniformly distributed, but instead, smaller digits (1, 2, 3) appear more frequently as leading digits than larger digits (7, 8, 9). This law is based on mathematical principles and has been found to apply to various datasets, including financial transactions.

It's important to note that while Benford's Law is a powerful tool, its application is not foolproof, and there can be legitimate reasons for deviations from the expected distribution. Therefore, forensic accountants use it as one part of a comprehensive toolkit for financial analysis and investigation, combining it with other techniques and methods to build a more accurate picture of the financial situation.

Step 7: Reporting

Reporting usually falls into one of two categories: preliminary assessments, conducted while the investigation is ongoing, and final exhibits, which serve to substantiate opinions or conclusions reached.

Preliminary Assessment:

Interactive data visualizations aid in real-time data assessment, guiding subsequent investigative steps.

Final Exhibits:

These often take a lot more time and thoughtfulness to develop. Whether using a spreadsheet, a proprietary software platform, or data visualization techniques, meeting the standard for courtroom-ready evidence while communicating a powerful narrative is the key to effectiveness.

A Roadmap to Resolution

From the initial step of determining the required software based on document types to the meticulous review and organization of all pertinent data, these 7 steps provide a comprehensive framework to navigate the complexities of financial investigations and dispute resolutions.

To learn more about these seven steps in even greater detail, download our whitepaper, “The Best Practices Guide to Financial Disputes and Investigations.”

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