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Fighting Fraud and Financial Crimes in the Generative AI age

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In today’s increasingly digital world, fraud and financial crime have become significant concerns. Global research shows that 2022 witnessed a 72% increase in fraudulent activity, with almost a quarter of survey respondents expecting a significant budget increase for anti-fraud technology through 2025. Given the transformative impact of artificial intelligence (AI) across all industry sectors, the challenge of combating financial crime has grown more complex and multifaceted.

Generative AI has emerged as a groundbreaking technology capable of creating realistic data and media, opening significant new avenues for financial crimes. The escalating sophistication of fraud techniques, including deepfakes and synthetic identities, necessitates advanced detection and prevention strategies.

The world stands at the threshold of what can be termed a “Dark Age of Fraud”. Financial services sectors are rushing to deploy AI solutions to counteract sophisticated fraud strategies. The scope for positive use cases for generative AI is considerable. Banks are poised to invest in new technologies to combat authorized push payment scams, driven by regulators demanding greater liability. Insurers are increasingly integrating AI into their claims processes and fraud detection efforts.

Generative AI also holds the potential to transform fraud and financial crime compliance. By incorporating machine learning and network analytics into anti-fraud and anti-money laundering systems, organizations can significantly reduce the number of false negatives and positives, thereby enhancing transaction monitoring efficiency.

To mitigate against the risk of generative AI abuse for fraud perpetration, AI and machine learning must enhance anti-financial crime programs. Organizations can consider several strategies to fundamentally change their approach to fraud detection.

At its most basic level, they can leverage AI and machine learning to enhance fraud detection accuracy and efficiency. Supervised machine learning algorithms can self-learn from target variables within the data, flag anomalies, and apply this knowledge to new data. Unsupervised machine learning uncovers potentially suspicious risks organizations might overlook. It works without a target, searching for anomalies in the data. Additionally, entity resolution and network analytics can help identify suspicious communities and organized crime rings.

A second strategy involves fortifying and expediting authentication processes to validate customers in the digital realm. Leveraging multiple data sources related to device intelligence, behavioral biometrics, and information trustworthiness shared by customers can help identify real customers, fraudsters, or bots. This not only enhances fraud detection but also reduces customer friction. Organizations can also use robotic process automation (RPA) to automate third-party data searches and queries during enhanced due diligence processes.

A third aspect to consider is coordinating and operationalizing fraud, anti-money laundering, and cyber events. Given financial services organizations’ use of big data analytics to consolidate data across siloed functions, combining these for a holistic risk view (termed FRAML) makes sense. Similar data and technology present an opportunity to reduce operational costs and enhance efficiencies.

A fourth strategy involves using AI to improve investigation efficiency with intelligent case management. An advanced analytics-driven alert and case management solution can prioritize cases, recommend investigative steps, and expedite straightforward cases. It can intelligently retrieve case data from internal databases or third-party data providers while presenting data in easy-to-understand visualizations on a single screen.

When it comes to financial crime prevention, ethical considerations around AI must be paramount. Financial services organizations should not only focus on technological prowess but also on the ethical framework underpinning this technology.

Ensuring data privacy, securing informed consent where necessary, and preventing biases leading to unfair or discriminatory outcomes are crucial. Transparency in AI decision-making processes allows for auditability and explainability of AI-driven actions.

Next-generation anti-fraud and anti-money laundering technology have become imperative as bad actors increasingly use generative AI for fraudulent activities. As technology advances, the barrier to entry has lowered, making it accessible to smaller institutions. Today, organizations need not maintain a team of data scientists but can adopt packaged advanced fraud and financial crimes data science solutions to automate repetitive manual processes and more accurately detect suspicious activity.

ByMarcin Nadolny, Head of EMEA Fraud, Fincrime & Data Science at SAS

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