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July 12, 2022 Practice Points

An Analytical Approach to Overdraft Fee Litigation

How to leverage complex data analytics to assist in settlement negotiations of overdraft fee class actions.

By Tom Hermanek and Heather Koo

Although some banks and credit unions are eliminating overdraft fees, there are over 10,000 banks and credit unions in the United States, and many of them still charge overdraft fees to their customers. In 2020, consumers paid $12.4 billion in overdraft fees alone. These fees provide customers an opportunity to file class action suits against such institutions alleging breach of the customer’s account contract. Over the past 10 years, plaintiffs have filed various types of class action suits claiming improper overdraft fee practices. In the past few years, many of the suits filed against banks and credit unions involve fees charged on debit card transactions, which are authorized on a positive balance but settle later when the account has a negative available balance. These fees are commonly referred to as APPSN (Approve Positive, Purportedly Settle Negative) fees.

In general, there is a delay between when a debit card transaction is authorized and when the transaction settles to a customer’s account. When these transactions are initiated, the merchant sends an authorization request to the customer’s financial institution. The financial institution subsequently authorizes the transaction based on the customer’s available balance at that time. Typically, a hold is placed on the customer’s account after authorization to sequester the funds for payment to the merchant. The merchant is not paid until the transaction is sent to the financial institution for settlement, which may be up to several days after the authorization. During this “float” time, the customer may have initiated other transactions that overdrew their account. Because financial institutions charge fees based on the account’s balance at posting, the customer may receive an overdraft fee on the original debit card transaction at the time of settlement.

Plaintiffs claim that these fees do not actually overdraw the customer’s account because the customer had available funds at authorization and that APPSN fees breach the customer’s account contract with the financial institution.

To estimate the potential fees at issue in this type of claim, historical transactional data needs to be reviewed for the class period. Depending on the institution’s data retention policy, some banks may only store semi-structured cold storage reports for the class period. These cold storage reports would need to be converted to a tabular format before they can be analyzed. This is where approaches to advanced analytics, such as Python and/or other data manipulation tools, can be utilized to more efficiently parse through both structured and unstructured reports in a systematic way to extract potentially pertinent information needed for analysis. Aside from cold storage reports, these tools can be used to capture information from a wide variety of data sources such as customer statements and third-party reports. As the technology enabling these programs to read electronic files continues to improve, these tools will only become more powerful and data from previously unfeasible sources will become accessible.

Once all necessary reports have been structured in a consistent format, advanced technical techniques can be used to aggregate and link data, subject to limitations, across disparate data sources in preparation for analysis. A customized analytical process can be created from this data to (1) validate the extracted transactional data, (2) systematically attempt to link authorization information to settlement records using certain assumptions regarding timing and transaction amounts, and (3) get an understanding of potential APPSN fees and exposure.

In general, the process outlined above can assist financial institutions in evaluating issues involved in APPSN class actions or to help better understand their data and its limitations. These insights can be leveraged in response to almost any litigation or regulatory request which requires complex data analysis.

Although this is only a brief summary of how these analytical tools and processes can be utilized in overdraft fee-related class actions, these examples provide a glimpse of how advanced analytics can expand data conversion capabilities, more accurately estimate potential exposure, and expedite settlement negotiations.

Thomas Hermanek is a senior director with Ankura in Chicago, Illinois. Heather Koo is a managing director with Ankura in Los Angeles, California.

Ankura is the Litigation Advisory Services Sponsor of the ABA Litigation Section. This article should not be construed as an endorsement by the ABA or ABA Entities.

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