Machine learning (ML) has increasingly been delivering value for large financial institutions as they invest in the technology to attract new customers and streamline operations. Recent advances in the technology underpinning artificial intelligence (AI) have enabled financial institutions to explore the application of ML techniques in their client facing operations.

The Securities and Exchange Commission (SEC) ordered the creation of the Consolidated Audit Trail (CAT) in 2012 after regulators realized that they did not have enough market data to explain the Flash Crash that occurred in May 2010. CAT addresses the audit trail of all transactions, providing information and checks and balances for market activity. In fact, CAT greatly expands on the requirements of the Order Audit Trail System (OATS) regulation, which was adopted in 1998.

The current RegTech landscape spans the technological spectrum from static data processing and reporting solutions to dynamic real-time transaction monitoring platforms. RegTech companies utilize emerging technologies such as machine learning, data analytics, artificial intelligence, cloud computing, biometrics, blockchain, cryptography, and many others to deliver solutions that enable faster, more accurate, and less labor-intensive compliance for various financial regulatory regimes.

Following the collapse of Lehman Brothers in 2008, the Dodd-Frank Act granted the FDIC Orderly Liquidation Authority (OLA) powers to serve as the receiver of a complex financial institution and prevent counterparties from terminating Qualified Financial Contracts (QFCs) with the firm. This paper examines some of the specific reporting rules put in place to enable a rapid reconciliation of QFCs under the OLA.