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Thanks to the availability of advanced technology, financial crimes are becoming increasingly sophisticated and complex. From trade-based money laundering and human trafficking to GenAI-assisted scams, the scale of financial crime today is unprecedented. Fortunately, artificial intelligence (AI), when used responsibly and ethically, can revolutionize the fight against these financial crimes.
Of course, AI is not a panacea. While it offers significant opportunities for organizations, the technology must be carefully managed. Companies must balance AI’s potential to combat financial crimes with ethical responsibility, transparency and governance.
Central to AI’s ability to detect financial crime is its ability to process and analyze massive amounts of data faster and more efficiently than any human operator. Traditional fraud detection and anti-money laundering (AML) methods often rely on strict maintenance rules and time-consuming manual processes that could otherwise be used for tasks requiring human decisions. In turn, AI enables institutions to effectively identify suspicious and complex patterns of financial crime and proactively protect consumers through early warnings.
For example, AI-powered tools can dynamically segment customer profiles, detect anomalies in transactions, and even identify hidden relationships within complex networks. These capabilities help expose schemes such as trade-based money laundering and transnational criminal activity. Furthermore, the integration of additional elements such as entity resolution and network analysis allows financial institutions to gain a more comprehensive view of the risk landscape. This allows them to detect threats and systemic weaknesses more effectively.
AI also offers significant improvements in operational efficiency. By automating repetitive tasks such as alert scoring and triage, companies can reduce the number of false positives and ensure investigators focus on the highest-risk activities. This frees up internal resources to focus on delivering more strategic value to complex cases. By combining efficiency and accuracy, financial institutions are now only transforming the way they manage risk, but also improving their ability to collaborate with peers and law enforcement agencies to reduce financial crimes.
One of the biggest barriers to effective investigation of financial crimes is insufficient data. Many companies struggle with incomplete data sets. This limits their ability to extensively train AI models. It is here where synthetic data has become a game changer.
Synthetic data mimics real-world data without exposing sensitive information. It allows institutions to test and optimize their financial crime control systems while safeguarding privacy. For example, synthetic data can be used for pen testing, so that AI systems are resilient to emerging threats. By using advanced tools, institutions can generate synthetic datasets that closely resemble real-world scenarios, helping them improve model accuracy and performance.
However, AI systems are only as good as the data they are trained on. We have seen biases or inaccuracies in the data that have led to unfair outcomes. Therefore, transparency and explainability have become critical to ensuring that AI-driven decisions are ethical and defensible.
It goes without saying that AI can also be weaponized by bad actors. For example, synthetic financial data can be manipulated to obscure audit trails. Malicious actors can input misleading data into AI systems to influence decision-making. This underlines the importance of comprehensive governance frameworks that mitigate these risks while underscoring the need for ongoing monitoring.
Companies must therefore adopt reliable AI practices that align with regulatory expectations and societal values. This includes integrating human oversight into the decision-making process to maintain accountability and trust.
The fight against financial crimes cannot be won in isolation. Collaboration is essential. These can be teams within companies, in the public and private sectors, or through strategic partnerships with regulators and technology providers.
For example, public-private partnerships can facilitate the sharing of insights, data and best practices. In doing so, stakeholders have established a united front against criminal networks. Meanwhile, collaborative frameworks between human analysts and AI systems can ensure that technology enhances rather than replaces human judgment.
As financial crimes become more sophisticated, organizations must invest in continuous learning and innovation. This allows them to stay one step ahead of emerging threats and embrace a culture of collaboration to build resilience against modern financial crimes.
AI is certainly transforming the fight against financial crimes. It gives companies the tools to detect and prevent threats faster than ever. But it does require a holistic approach that combines data management, ethical practices and meaningful collaboration.
// Through Stephanie Ora, Global Lead for Financial Crimes Analytics, SAS
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