SARs Reimagined: How Generative AI is Shaping Financial Intelligence

The blog highlights how generative AI and copilots are transforming Suspicious Activity Reporting, making it more consistent and efficient, and generating higher-quality reports.

Francisco Mainez
4 min
To be suspicious is not a fault. To be suspicious all the time without coming to a conclusion is the defect
— Lu Xun

A Suspicious Activity Report (SAR) is a formal report submitted to a nation's Financial Intelligence Unit (FIU). Some jurisdictions refer to SARs as Suspicious Transaction Reports (STR). While predominantly filed by Financial Institutions (FIs), certain "Designated Non-Financial Businesses and Professions" (DNFBPs), like lawyers and real estate agencies, also contribute SARs. These non-financial sectors are included due to their vulnerability to money laundering and terrorist financing activities.

The SAR process typically begins when an investigator observes a suspicious transaction or set of transactions. If these activities warrant further examination, the institution's Money Laundering Reporting Officer (MLRO) will sign off and escalate the report. The MLRO is formally responsible to the Board for producing such reports.

Since there is no official definition of what constitutes suspicious activity, finding an actual money laundering case from a SAR can be compared to finding a needle in a haystack. For instance, the UK's FIU National Crime Agency received over 901,000 SARs in 2022, a 21% uptick from the prior year. Globally, SAR volumes are estimated to be around 34.6 million, according to the Egmont Group.

SAR: A Needle in a Million Haystacks

The current system of filing Suspicious Activity Reports (SARs) is often criticized for needing to be revised, mainly because of the overwhelming number of reports filed. Reporting units that submit SARs usually don't get feedback about the quality of the reports they submit. Financial Intelligence Units (FIUs) and law enforcement argue that giving constant updates risks leaking sensitive information and potentially alerting criminals to change their activities.

So, what's the real benefit of having so many reports? FIUs serve as the middlemen, funneling these SARs to law enforcement and other investigative agencies. The value lies in making these reports as actionable as possible for those who will further investigate them.

For SARs to be truly effective, they must be clear and detailed. They should identify who is involved, what suspicious activities they are engaged in, and how they're doing it—be it through specific locations, services, or currencies. The challenge here is that these reports are filed by people with different levels of training and experience, leading to inconsistency. Hence, there's a need for more uniform criteria to make SARs as useful as possible for those responsible for taking the next investigative steps.

Consistency Over Perfection: The Key to Effective Reporting

The value of intelligence hinges on the quality of the reports that feed into it. A high-quality report will lay out the facts in a clear, structured manner, pulling in all necessary sources of information. This clarity helps those receiving the intelligence to quickly identify the key elements—like the people involved, relevant timelines, and jurisdictions. It can either help categorize the case into a known type or flag it as a potential new kind of activity.

Achieving this level of consistency often comes down to a mix of training and experience. However, maintaining consistent quality can be challenging in an industry with high turnover. Financial Institutions (FIs) and Financial Intelligence Units (FIUs) often operate under resource constraints, even as the volume of SARs continues to grow with advancements in financial crime detection.

More consistent and higher-quality SARs would streamline the FIUs' role as intermediaries with law enforcement agencies. This speeds up investigations and provides the essential pieces needed to complete the puzzle—or, as some experts put it, find the "other half of the needle."

The Rise of GenAI Copilots in Compliance and Reporting

Advancements in natural language generation powered by artificial intelligence, like OpenAI's Chat GPT, have ignited conversations across various sectors, including Regulatory Compliance. Regardless of differing opinions, the fact is that generative AI (GenAI) isn't going anywhere. Its broad application across industries is challenging traditional human-centric processes, including crafting Suspicious Activity Reports (SARs).

Pioneers in financial crime prevention, such as Lucinity with its Luci Copilot, have swiftly integrated GenAI into their toolkits.

These AI copilots significantly amplify the effectiveness of investigative work and SAR narrative creation. While they offer robust coaching to staff, they also ensure that vital expertise and decision-making remain human-led. The result is a transformative leap in productivity, consistency, and quality. Additionally, these copilots standardize how data and facts are sequenced and presented in SARs, making the reports far more actionable and useful for their target audience.

The incorporation of cutting-edge technology presents several questions and challenges:

  • Technological Imbalance: As private financial institutions increasingly adopt advanced tech solutions to enhance efficiency and reporting, public sector entities like FIUs and law enforcement may need to catch up. This misalignment could overwhelm public institutions with an influx of reports. To balance this, development in Natural Language Processing should be pursued to handle the surge in reporting volumes.
  • Quality Over Quantity: While boosting productivity is essential, it shouldn't come at the cost of report quality. Financial Institutions and other GenAI users must establish robust auditing systems to ensure the integrity of SARs. Specialized teams focusing on AI oversight will be critical in the coming years.
  • Risk of Inaccuracy: Errors or biases in AI-generated SARs could send investigators off course. Periodic evaluations and adjustments of language models are vital to mitigate these risks.
  • Regulatory Gaps: The absence of global GenAI regulation poses challenges for institutions operating across borders. Ensuring that GenAI models meet universal standards for Security, Compliance, Privacy, and Responsible Use while complying with local laws is critical.

The trajectory of GenAI in Compliance remains uncertain, but what's clear is that Financial Institutions and other regulated entities are keen on exploring these advanced solutions for compliance and efficiency. A multi-sectoral approach involving public, private, and tech players will be essential to maximize the potential benefits and avoid criminal exploitation of these technologies.

Learn more about the Luci copilot:

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