Post-Trade Analytics Can Help Prevent Fraud

As data analytics become increasingly sophisticated, firms have many advanced new fraud detection methods at their disposal. Traditionally, financial firms have relied on manual processes and employee reporting to detect fraudulent activity. Such methods are by their natures reactive and less than completely reliable. They flag suspicious activities only after the fact and often rely on sampling, which can allow significant amounts of data to go without scrutiny.

Fortunately, advances in data science allow firms today to analyze enormous quantities of data and identify patterns, anomalies, and correlations that could suggest illegal activity. Organizations can now monitor business flows in real-time to rapidly and cost-effectively identify suspicious activity.

Unusual trading patterns may suggest all is not as it should be

One significant benefit of trade analysis software is that it can identify and monitor trading patterns. For example, artificial intelligence (AI) can be used to track trading activity over time. Managers, traders, CTAs, and other professionals will build a track record of daily and weekly trading volume that will become predictable. When an employee demonstrates activity beyond the predicted level or suspicious correlations, the unusual activity will raise a red flag to trigger an investigation. In this way, management will be able to understand when a trader is in trouble or about to fail. Had this type of technology been available in 1995, Barings Bank might have identified Nick Leeson’s trading activities and avoided bankruptcy.

While unusual volume and/or frequency is highly suggestive of fraudulent activities, these aren’t the only trading irregularities that should raise concerns. Post-trade technology can also identify other concerning behavior, such as equity trading on a fixed income desk or commodities trading by equity or fixed income traders.

Trade matching can identify concerning anomalies

In today’s market, every firm should be engaged in trade matching, or affirming trades with their counterparties in transactions. Artificial intelligence can also predict how trades should be booked or matched. One sign of trouble, for example, is when trades between a specific trader and counterparty regularly fail to match until later in the day, compared to their other activity. While there is any number of reasons that trades may fail or deviate from typical patterns, fraud is certainly one of them, hence the cause for concern.

Anomalous returns can also signal trouble

One of the various metrics generated as part of the post-trade analysis is tracking error or the difference between the price of a position or portfolio and the price of a benchmark. If multiple traders are following the same strategy by the same manager and demonstrate differing returns, there is likely an issue with unscrupulous allocation. The trader could be allocating the best trades to their account while leaving investors in the lurch. Trade flow analysis and checks on the trade flow systems are the best ways for all parties to ensure that there is no fraud and managers are accountable for their behavior.

Trade logs can also help detect unusual trading patterns

Trade logs offer reports on daily execution, including transactions completed in after-hours trading. The logs incorporate trade rules, and users can detect unusual trading patterns with the assistance of artificial intelligence that is informed by these rules. Without the benefit of post-trade analytics, users can only react to the information presented. By leveraging post-trade analytics, users can be pro-active in comprehensively reviewing trade logs against trade rules.

Prevent money laundering

In the same way that post-trade analytics software can generate trading activity and trade matching reports, it can also be used to exam patterns in the movement of funds between accounts, or between accounts and external counterparties. It can also be deployed to monitor patterns in back-office wire transfers as well. Given this ability to monitor and analyze the movement of funds, this type of software can be deployed in the fight against money laundering.

Fraud and money laundering are becoming both more sophisticated and more prevalent. Both create significant risks for financial services firms and can lead to financial and reputational losses as well as legal liability in some cases. In the never-ending battle against invisible enemies, post-trade analytics software can offer a valuable level of protection to firms, clients, and investors.


About the Author

Rebecca Baldridge, CFA, is an investment professional and financial writer with more than 20 years of experience in creating content and research for asset managers, investment banks, brokers and other financial services clients. She’s worked for some of the biggest names in the industry, including Merrill Lynch Asset Management, JP Morgan Asset Management, BNY Mellon and Franklin Templeton. Rebecca also spent 9 years as an analyst and director of equity research in Moscow, working for several Russian banks. In late 2019, she founded Quartet Communications, a boutique communications firm serving financial services clients. Her writing has been published in outlets including Pensions & Investments,, Inc. magazine, and She holds a B.A. in Russian from Purdue University and an M.S. in Finance from the Krannert Graduate School of Management at Purdue.

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