COMBATING FRAUD AND FINANCIAL CRIMES IN THE GENERATIVE AI AGE

In the contemporary digital landscape, fraud and financial crime pose significant challenges. International studies reveal a 72% surge in fraudulent activities in 2022, with nearly 25% of survey participants anticipating a substantial budget rise for anti-fraud technology until 2025.

Generative artificial intelligence (AI) has significantly impacted various industries, intensifying the complexity of combating financial crimes. This revolutionary technology, adept at producing realistic data and media, has concurrently created new avenues for fraudulent activities, particularly with the emergence of advanced techniques like deepfakes and synthetic identities.

The escalating sophistication of fraud tactics necessitates advanced detection and prevention methods. The financial services sector is poised to confront what can be termed a “Dark Age of Fraud,” prompting a rush to implement AI solutions to counteract evolving fraudulent strategies.

Generative AI offers positive applications, with banks considering investments in technologies to combat authorized push payment scams, driven by regulatory pressure for increased accountability. Insurers are also incorporating this AI in their claims processes and fraud detection.

The potential of generative AI extends to transforming fraud and financial crime compliance. By integrating machine learning and network analytics into anti-fraud and anti-money laundering systems, organizations can significantly reduce false negatives and positives, enhancing the efficiency of transaction monitoring.

To counter the risk of generative AI abuse for fraud, AI and machine learning should be harnessed to enhance anti-financial crime programs. Organizations can adopt strategies such as leveraging supervised and unsupervised machine learning for improved fraud detection accuracy and efficiency. Entity resolution and network analytics can aid in identifying suspicious communities and organized crime rings.

Another strategy involves strengthening and expediting authentication processes in the digital realm through the use of multiple data sources related to device intelligence and behavioral biometrics. This approach aims to verify the legitimacy of customers, identify fraudsters, or detect automated bots, simultaneously improving fraud detection and reducing customer friction.

Additionally, organizations can consider employing robotic process automation (RPA) to automate searches and queries of third-party data during enhanced due diligence processes.

Coordinating and operationalizing fraud, anti-money laundering, and cyber events is a third aspect. Combining big data analytics to consolidate data across functions can provide a more holistic view of risk, often referred to as FRAML (Fraud, Anti-Money Laundering, and Cyber). This integration presents opportunities to reduce operational costs and enhance overall efficiencies.

A fourth strategy involves using AI to enhance investigation efficiency with intelligent case management. Advanced analytics-driven alert and case management solutions can prioritize cases, recommend investigative steps, and expedite straightforward cases. Such solutions can pull data from internal databases or third-party providers, presenting it in comprehensible visualizations on a single screen.

Emphasizing ethical considerations is crucial in financial crime prevention using AI. The complexity of generative AI necessitates a focus not only on technological prowess but also on ethical principles. Data privacy, securing informed consent, and preventing biases leading to unfair outcomes are paramount. Transparency in AI decision-making processes is essential for auditability and explainability.

As bad actors increasingly use generative AI for fraudulent activities, next-generation anti-fraud and anti-money laundering technology become imperative. The advancing technology has lowered entry barriers, making it accessible to smaller institutions without requiring extensive in-house data science expertise. Today, organizations can embrace packaged advanced fraud and financial crimes data science solutions to automate manual processes and improve the accuracy of detecting suspicious activities.

About Marcin Nadolny Role:

In this role, he likely oversees and leads initiatives related to fraud prevention, financial crime detection, and the application of data science within the SAS organization across the Europe, Middle East, and Africa (EMEA) region. His responsibilities may include developing strategies, implementing advanced analytics solutions, and addressing emerging challenges in the realm of fraud and financial crime using SAS technologies.

As a leader in this field, Marcin Nadolny is likely instrumental in guiding SAS’s efforts to stay ahead of evolving fraud techniques, incorporate cutting-edge data science methodologies, and provide effective solutions to organizations grappling with the complex landscape of financial crimes in the EMEA region. His expertise may also contribute to the development of ethical and responsible approaches to utilizing artificial intelligence and machine learning in the fight against fraud.

Writer: Marcin Nadolny serves as the Head of EMEA Fraud, Fincrime & Data Science at SAS.

News Source: Techeconomy