How compliance teams are improving with artificial intelligence (AI)

Regulators, compliance officers, and bank executives are constantly looking for effective tools to combat the growing sophistication of rogue organizations, whose wrongdoings often result in billions of dollars in financial losses. They seek to more accurately and quickly identify criminal actions such as insider trading, market manipulation, money laundering, violations of sanctions and export controls and transactions on the accounts of others .

Identifying suspicious activity requires appropriate monitoring capabilities, especially given the large volume of communications and transactions that must be assessed.

Although infrequent, the consequences for banks in terms of fines for breaching compliance rules are steep. In 2020 alone, $15 billion in fines worldwide were inflicted on banks for such violations.

Artificial intelligence is joining the fight for trade compliance.

Artificial intelligence (AI) is increasingly being used to fight financial crime. AI and associated machine learning (ML) or deep learning models provide regulators and compliance officers with new capabilities. These models can process different types of data, run a series of advanced analyses, and provide a range of results to help eliminate fraud in international trade.

Deep learning language models take a generational leap forward

Some types of fraud cannot be discovered using transaction ledgers, financial records, and other tabular data alone. As an example of scale, Citi processed 9.4 million transactions in 2018, for approximately $1 trillion in trade, giving the bank a massive dataset of 25 million pages.

In many cases, fraud occurs outside of these systems, in processes involving unstructured data communications such as audio, images, and chats. In this case, the final source of registration data contains minimal markers for identification, so advanced analytics are required to uncover discrepancies.

No team is able to effectively read, interpret, and report potential wrongdoing in a dataset of this size in a realistic timeframe. Hence the need for deep learning models and the accelerated computing infrastructure that enables computers to support trade compliance.

Before accelerated computing, training linguistic and unstructured/semi-structured models took weeks or months. Now, language and vision models can be trained in hours or days, and their results can be delivered in seconds.

Since money moves in real time, models must be able to run in milliseconds to prevent financial crimes. Real-time fraud prevention requires an understanding of spoken language. Not just one language, but many, in real time, with the ability to understand context, describe sentiment, identify entities (companies, people, etc.) and integrate all of this complex data into a fraud rating.

As data size grows exponentially, more sophisticated models are trained, requiring more advanced accelerated computing frameworks to effectively manage trade compliance. In addition to unstructured data, tabular data can be analyzed to detect activities such as upstream trading, insider trading, and collusion.

With the expansion of international trade, the use of AI must follow

The level of international trade will continue to grow in the long term as supply chains strengthen and the pandemic abates. As financial flows increase, the number of bad actors seeking to defraud the system for their own financial gain will also increase.

Financial regulators, compliance officers and banking managers must prioritize investing in AI, the first tool capable of analyzing all the data (structured and unstructured) that feeds the financial markets.
When fraudulent activity is detected, compliance must ensure that a fine balance is maintained and that business units can continue to perform trading functions that generate legitimate profits. AI techniques can sift through large amounts of data and identify activities/data with advanced algorithms that require deeper analysis.

Legacy systems are often inefficient or incorrectly report large amounts of data from legacy systems, known as excessive false positives. Reducing false positives alone will yield huge efficiencies and cost savings. It will allow financial entities to analyze in real time reams of communications, commercial data and millions of inputs from thousands of sources. In the meantime, companies will be able to continue trading and earning profits, taking advantage of genuine arbitrage opportunities – a benefit to all market participants.

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How compliance teams are improving with artificial intelligence (AI)

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