Introduction

Several artificial intelligence (AI) technologies have been developed in recent years to enhance security and prevent fraud. The flip side of that coin, however, is that AI is also being exploited by malicious actors to perpetrate fraud. AI is being leveraged to merge authentic and fabricated data, create highly believable fake identities, forge documents, and disseminate misleading information. As fraudsters consistently adopt innovative technologies and tactics, keeping abreast of these evolving threats is an ongoing challenge.

Furthermore, the significant growth we are witnessing in the volume and complexity of data has transformed the way in which financial investigations are carried out.

Against this backdrop, AI has become an integral part of investigations, offering advanced tools and techniques to analyse vast amounts of data efficiently. The use of AI in monitoring and analysing structured datasets, such as accounting and transactional data, has become well established in recent years. Moreover, AI-enhanced workflows have also now been integrated into prominent document review platforms for unstructured data, such as Reveal, Relativity, and Nuix Discover. These AI enhancements have resulted in detection methodologies that consistently surpass manual methods both in terms of improving time efficiency and decreasing document and transaction volumes requiring review.

The growing capabilities of AI, in a field that prioritizes accountability and reliability of information, also raise the importance of explainable AI (XAI) systems, which aim to provide insights into how AI models arrive at outcomes.

Investigative Applications

In the realm of financial investigations, where the effective analysis of structured and unstructured data is key, proper deployment of AI can have a profound impact on the investigator’s ability to rapidly detect complex fraud schemes, revealing instances of collusion that perpetrators employ to circumvent established controls. Specifically, AI systems excel in analysing vast datasets across systems, recognising patterns, and detecting anomalies that may elude traditional approaches.

In cases of collusion, where individuals work together to manipulate or override existing controls, the advanced algorithms and machine learning capabilities of AI enable the identification of subtle and coordinated efforts to deceive systems and exploit vulnerabilities. It does so by analysing data to identify a baseline of expected transactions/behaviour that then allows it to recognise instances of deviation.
 
Furthermore, AI’s ability to adapt and learn from emerging patterns of fraudulent behaviour by employing unsupervised learning methods, such as clustering and anomaly detection, contributes to its effectiveness in detecting new and evolving types of fraud across large and complex data sets even across systems.

AI’s ability extends beyond structured data such as transactional information and accounting records. Natural Language Processing (NLP) is used to analyse data such as emails, reports, chat logs and social media content to identify potential misconduct, manipulation or insider trading. It also has the capacity to incorporate external data sources, including open-source intelligence (OSINT) databases, to enhance the contextualisation of transactions and relationships, which may not be readily apparent. Such sentiment analysis can provide investigators the capability to meticulously evaluate market sentiment surrounding specific companies or assets. This analytical approach can prove invaluable in discerning potential instances of market abuse or insider trading. By leveraging advanced algorithms and machine learning techniques, AI-powered sentiment analysis can distil thousands of data points in a concise and effective way.

Furthermore, by harnessing advanced analytical tools and machine learning algorithms, organisations can proactively discern patterns and anomalies indicative of potentially fraudulent activities or high-risk behaviours. This predictive capability not only aids in the early detection of irregularities but also serves as a strategic approach to optimising the allocation of resources for investigations.

Preventative Applications

Alongside investigative applications, AI is playing a pivotal role in assisting transaction monitoring systems. These systems are designed to continuously scrutinise and analyse financial transactions, seeking patterns or anomalies that may indicate potential risks or fraudulent activities. The integration of AI enhances the efficiency, accuracy, and adaptability of these systems.

AI introduces advanced analytics and machine learning algorithms into transaction monitoring. These technologies excel in anomaly detection by learning from historical data, recognising patterns, and adapt- ing to evolving trends. Unlike rule-based systems, AI can identify irregularities that may not be explicitly defined in pre-existing rules, including the ability to establish connections between seemingly unrelated entities by analysis patterns in transaction data.

AI can also employ effective feature engineering which involves selecting and transforming relevant variables in the dataset. By incorporating meaningful features, AI models can better discriminate between legitimate and suspicious transactions. This can include analysis of behavioural patterns of individuals or entities over time. Such an approach helps establish a baseline for normal behaviour, making it easier to identify anomalies that may trigger alerts.

A large part of any transaction monitoring workflow is the identification and assessment of false positives. AI reduces false positives in transaction monitoring systems through its ability to analyse vast amounts of data with precision and identify complex patterns. Traditional rule-based systems often generate false positives due to their rigid criteria, triggering alerts for legitimate transactions that may share similarities with known fraudulent patterns.

By contrast, AI technologies, particularly machine learning algorithms, learn from historical data to understand the normal behaviour of transactions and adapt to evolving patterns. By considering a multitude of factors simultaneously, AI models can distinguish between normal and suspicious activities more accurately. These models excel at recognising subtle anomalies and non-linear relationships, minimising the chances of false positives.

Furthermore, AI’s real-time processing capabilities enable it to assess transactions as they occur, allowing for immediate context-aware analysis. This agility ensures that alerts are triggered based on a comprehensive understanding of the transaction context, reducing the likelihood of false positives and allowing investigators to focus their efforts on genuinely suspicious activities. Ultimately, AI’s capacity to learn and evolve enhances the efficiency and accuracy of transaction monitoring systems, contributing to more effective fraud detection and prevention.

Explainable AI (XAI)

When harnessing AI in this way, it is imperative for organisations to provide understandable and transparent explanations regarding their decision-making processes.

Explainable AI (XAI) refers to the set of methods, techniques, and tools designed to make the decision- making processes of AI systems more understandable and interpretable for humans. The goal of XAI is to provide insights into how AI models arrive at specific outcomes.

Traditional machine learning models, such as deep neural networks, are often considered “black boxes”, because understanding how they make decisions can be challenging. XAI addresses this challenge by offering methods to interpret and explain the predictions or classifications made by AI systems.

In the context of financial investigations, XAI becomes particularly crucial due to the need for accountability, interpretability, and trust in the decisions made by AI models. Financial investigations often involve complex data, and understanding how AI arrives at specific conclusions or recommendations is essential for regulatory compliance, risk management, and legal purposes.

Not only does XAI enhance transparency, it also facilitates the validation and debugging of AI models. Investigating financial transactions involves dealing with large datasets and complex relationships. These tools help identify potential biases, errors, or misinterpretations in the model’s output ensuring accuracy. XAI encourages collaboration between AI systems and human experts in a way that humans can leverage the insights provided by AI models while retaining the ability to question or override decisions when necessary. This collaborative approach enhances the overall investigative process, helps withstand scrutiny and aids in communicating the findings of AI models to non-technical stakeholders. This is crucial in instances where the results need to be conveyed to individuals who may not have a deep understanding of AI technology.

This is particularly important in sensitive areas like financial investigations, where the consequences of decisions can have a significant impact on individuals and institutions, and where decisions may need to be explained to regulators or court.

Concluding Remarks

The future of AI in financial investigations holds tremendous potential for transformative impact. As technology continues to advance, AI’s role in this domain is poised to evolve, offering enhanced capabilities in fraud detection, risk assessment, and regulatory compliance. AI-driven tools, equipped with sophisticated algorithms and machine learning, will increasingly streamline and expedite the analysis of vast datasets, facilitating quicker identification of suspicious activities. Furthermore, the integration of XAI will contribute to the transparency and interpretability of AI-generated insights, addressing concerns related to accountability and ethical use.

AI’s adaptive nature will also empower financial investigators to stay ahead of emerging threats, as these systems continually learn and adjust to evolving patterns of financial crime. Collaboration between AI and humans will likely become more seamless, with AI providing valuable support in data analysis, anomaly detection, and predictive modelling.

Despite the numerous benefits of AI, however, challenges remain in data quality (garbage in and garbage out), regulatory compliance and human oversight. Addressing these challenges is crucial in ensuring that the use of AI will lead to a dynamic and responsive approach to financial investigations in the future, while fostering a more efficient and accountable investigative process.

* This report was first published in Mealey’s Litigation Report: Artificial Intelligence.

[Editor’s Note: Keith Williamson is a Managing Director and Robert Cruse is a Director with Alvarez & Marsal’s Disputes and Investigations team in London. Any commentary or opinions do not reflect the opinions of Alvarez & Marsal or LexisNexis®, Mealey Publications™. Copyright © 2023 by Keith Williamson and Robert Cruse. Responses are welcome.]