TL;DR:
- Artificial intelligence (AI) encompasses various methods that enable machines to imitate human cognition, while machine learning (ML) is a subtype of AI.
- Three types of machine learning: supervised learning (interpreting data based on examples), unsupervised learning (discovering data points with similar characteristics), and reinforcement learning (problem-solving through trial and error).
- AI and ML offer benefits in fraud detection, including improved data analysis, anomaly detection, and efficient transaction monitoring.
- Best practices for responsible AI and ML implementation in fraud management: legitimate purpose, proportionate use, design and technical expertise, accountability and oversight, and openness and transparency.
- Explainability is crucial to meet regulatory expectations and enable informed decision-making.
- Organizations can consider outsourced AI solutions for efficient and specialized fraud management tools.
- Purpose-built AI (PBAI) can enhance existing systems, improving efficiency and risk management infrastructure.
- AI and ML complement human expertise rather than replacing it, freeing up time for higher-value tasks and improving overall AML/CFT risk management.
Main AI News:
In the realm of business, the terms artificial intelligence (AI) and machine learning (ML) often go hand in hand. While they are related concepts, it is important to recognize that they have distinct definitions and applications. AI serves as an overarching category encompassing various methods through which machines simulate human cognition, such as decision-making, data analysis, and problem-solving. On the other hand, ML represents a subtype of AI and is commonly used as a synonym for AI. The Massachusetts Institute of Technology (MIT) defines machine learning as a form of alternative programming that enables computers to learn and improve through experience.
When it comes to machine learning, there are three primary types to consider. The first is supervised learning, where a system learns to interpret data based on examples provided by humans. By utilizing labeled data samples, such as images of adult and child faces, the system endeavors to accurately identify new incoming data. Feedback from humans helps improve the system’s accuracy when it makes mistakes.
Next, we have unsupervised learning, which employs algorithms to discover data points with similar characteristics. Through a series of calibrations, the system identifies groups or clusters based on these similarities. For example, it may detect a significant group of new customers aged between 97 and 100. Unsupervised learning allows for the identification of unexpected similarities that humans may overlook when using pre-defined search parameters.
Lastly, reinforcement learning involves a system, referred to as an agent, learning problem-solving through trial and error. Human feedback is provided based on the success or failure of the agent’s actions, with the most efficient actions being reinforced. This process can aid humans in discovering new and more effective solutions to problems they may be struggling to solve independently.
AI and machine learning offers significant benefits in the realm of fraud detection. By harnessing the power of these technologies, human fraud teams can enhance their efficiency in a cost-effective manner. In a 2021 publication, the FATF (Financial Action Task Force) explored how AI can assist firms in analyzing and responding to criminal threats.
Automated speed and accuracy enable firms to categorize and organize relevant risk data. Machine learning, with its ability to detect anomalies and outliers, proves invaluable in improving data quality and analysis. Deep learning algorithms within machine learning-enabled tools can repeatedly perform tasks, learning from the outcomes to make accurate decisions in the future. The implementation of AI and machine learning tools encompasses transaction monitoring and automated data reporting.
The applications of AI in fraud detection are numerous. For instance, firms can utilize AI to intuitively establish fraud transaction monitoring thresholds by analyzing risk data. When a customer approaches or surpasses a pre-defined threshold, machine learning tools can decide whether to trigger a fraud alert based on the customer’s profile or financial situation. Furthermore, AI can help detect groups of customers exhibiting characteristics that indicate a higher risk of either being victims or perpetrators of fraud.
Natural language processing (NLP) aids in uncovering instances of fraud in adverse media searches. AI-driven alert prioritization ensures higher-risk alerts receive immediate attention, reducing time wasted on false positives. AI’s enhanced anomaly detection goes beyond individual rules, providing comprehensive data analysis to identify atypical or abnormal behaviors that signify a higher risk. Human analysts can then delve deeper into these findings and decide on further actions regarding the customer’s activities.
When it comes to employing AI and ML in fraud management, there are specific best practices that organizations should follow. In 2022, the Wolfsberg Group outlined five key practices to ensure the responsible use of AI and ML in managing financial crime risk. Firstly, firms should clearly define the scope of AI tools and develop a governance plan that considers the risks associated with their potential misuse.
Risk assessments should account for data misappropriation and algorithmic bias. Proportionate use of AI and ML is crucial, with firms responsible for managing risks and ensuring they remain proportional to the benefits obtained in combating financial crime. Teams utilizing and overseeing AI should possess the necessary design and technical expertise to understand the technology’s functions, explain its outputs, and control for any limitations or biases.
Accountability and oversight are paramount, with governance frameworks covering the entire lifecycle of AI. Firms retain responsibility even when utilizing vendor or partner-provided AI solutions. Lastly, openness and transparency should be maintained, striking a balance between regulators’ transparency expectations and the need to protect confidential information. Clear communication with regulators and customers is essential, as is providing documented reasons for AI’s risk detection decisions to facilitate ongoing research and establish an audit trail.
To adhere to these best practices, firms must prioritize AI explainability in their chosen risk management solutions. This avoids the use of “black box” systems where decisions are made without clear understanding. Explainability refers to the capability of technology-based solutions or systems to be explained, understood, and accounted for. By incorporating an ensemble approach, which layers multiple smaller AI functionalities together, firms can achieve explainability. This approach allows for the clear segmentation and explanation of each component’s contribution to the overall decision-making process.
When considering fraud solutions, firms often contemplate the choice between developing an in-house program or outsourcing to a vendor. While some may prefer the control and familiarity offered by an in-house solution, the resources and effort invested in building fraud solutions may not yield optimal results. Thanks to the rise of efficient, specialized, and cost-effective solutions utilizing AI and ML, external vendors can provide tailored risk management tools that meet firms’ unique needs, risk profiles, and business practices.
Automation of fraud compliance processes, such as onboarding and identity verification, screening and monitoring, and transaction monitoring, can be achieved through these solutions. For firms seeking minimal disruption to established in-house systems, hybrid solutions offer a viable option. Purpose-built AI (PBAI) can be integrated into existing transaction monitoring systems, enhancing their capabilities without requiring a complete overhaul. PBAI, with its explainable ensemble model, enables firms to upgrade their legacy tools, resulting in improved efficiency, faster decision-making, and a more comprehensive risk management infrastructure.
In summary, artificial intelligence and machine learning possess the ability to perform certain tasks with greater efficiency than even the most experienced human analysts. However, it is essential to remember that technology should serve as an enabler and supplement to human expertise rather than a complete replacement. Humans remain legally accountable for decisions informed by AI, and any errors conflicting with human rights should be rectified.
AI and ML empower human teams to engage in higher-value work that technology alone cannot accomplish, such as conducting complex investigations into high-risk activities or determining the best course of action for a risky alert. As with any powerful tool, the implementation of artificial intelligence and machine learning should follow best practices from the outset. By doing so, firms can equip themselves to effectively navigate the ever-evolving landscape of AML/CFT (Anti-Money Laundering and Combating the Financing of Terrorism) risks.
Conclusion:
The integration of artificial intelligence and machine learning in business fraud detection provides significant advantages. By leveraging these technologies, organizations can enhance their data analysis capabilities, detect anomalies efficiently, and streamline transaction monitoring. Adhering to best practices ensures responsible implementation, with explainability playing a crucial role in meeting regulatory expectations and enabling informed decision-making.
The availability of outsourced AI solutions and purpose-built AI offers cost-effective options for firms seeking efficient and specialized fraud management tools. This synergy between technology and human expertise empowers organizations to navigate the dynamic landscape of AML/CFT risks and strengthen their overall market position.
