Leveraging Generative AI in fraud and financial crime investigations: efficiency, accuracy, and compliance

In the fast-evolving world of financial crime and compliance, Glenn Fratangelo from NICE Actimize agrees that Generative AI (GenAI) is becoming a game-changer.

Financial institutions (FIs) are grappling with various perspectives on adopting this technology—driven by both excitement and caution. This blog explores why embracing GenAI is essential and addresses the key questions and concerns shaping its implementation.

Financial institutions are under constant pressure to enhance their financial crime operations while ensuring regulatory compliance and operational efficiency. GenAI aligns perfectly with these goals by offering consistency in investigation processes and amplifying the effectiveness of human resources. As organisations grow rapidly, maintaining best-in-class cost efficiency and scalability becomes paramount. GenAI provides the tools needed to achieve these objectives, transforming hours of manual work into minutes, and enabling more strategic use of resources.

The push for GenAI adoption often comes from both top-down and bottom-up initiatives within FIs. Leadership drives the need for automation to decrease operational costs and create scalability, while operational teams seek to optimise existing resources. By assessing current systems and identifying areas for AI enhancement, institutions set clear objectives, like reducing false positives, improving detection accuracy, and ensuring regulatory compliance. This dual approach ensures that GenAI initiatives are prioritised and implemented effectively.

Leadership within financial institutions, are eager to harness the efficiency gains that GenAI promises. Leaders see GenAI as a way to amplify the effectiveness of their human resources, maintain cost efficiency, and accelerate operational scalability. This top-down push ensures that FIs are not just adopting technology for the sake of innovation but are strategically integrating GenAI to meet their long-term goals. However, it's crucial for FIs to prioritise strategic vendor partnerships that align with their objectives and regulatory requirements.

Generative AI significantly enhances both efficiency and accuracy in fraud and financial crime investigations through several key mechanisms:

Automation of repetitive tasks: By automating routine tasks such as data collection, report generation, and initial analysis, AI frees up investigators to focus on more complex and nuanced aspects of their work. This not only speeds up the investigation process but also reduces the risk of human error.

Advanced data analysis: AI systems can process and analyse vast amounts of data at high speed, identifying patterns and correlations that might be missed by human analysts. This leads to more accurate and comprehensive investigations.

Reduction in false positives: One of the challenges in fraud detection is the high rate of false positives, which can overwhelm investigators. Generative AI refines detection algorithms to reduce false positives, ensuring that only genuine threats are flagged for investigation.

Consistency: AI ensures the consistent application of rules and procedures across all investigations. This minimises human biases and errors, leading to more reliable outcomes.

Financial crime leaders are increasingly advocating for the integration of GenAI, driven by the necessity to keep pace with the evolving landscape of fraud and compliance. The need for operational scalability is critical, especially in the wake of significant business growth. GenAI can dramatically reduce the time taken for manual processes, enhance detection capabilities, and improve employee morale by shifting focus from mundane tasks to more complex investigations. These leaders encourage their institutions to embrace GenAI, recognising that the risk of not adopting it far outweighs the minimal risks associated with its implementation.

Let’s delve into the most impactful use cases for generative AI:

Case prioritisation: By evaluating the risk level of different cases, generative AI helps prioritise investigations, ensuring that the most critical cases receive immediate attention. This improves the overall efficiency of the investigative process.

Adverse media monitoring: 

Generative AI can scan and analyse vast amounts of news articles, social media posts, and other media sources to detect adverse information related to individuals or entities under investigation. It isn’t just the continuous monitoring of these sources, with generative AI the specific instance of adverse media can be reviewed, summarised, and analysed in context.

Summarising cases and alerts:

Generative AI can automatically summarize vast amounts of information associated with a alert or case. This includes extracting key points from detailed reports, consolidating data from various sources, and presenting investigators with concise, relevant summaries. This capability significantly reduces the time spent on reviewing and compiling information, allowing investigators to quickly grasp the essential aspects of a case and make informed decisions more rapidly.

Automated report generation: 

One of the most time-consuming tasks in financial investigations is the creation of Suspicious Activity Reports (SARs) and alert dispositions. Generative AI can automate these processes, significantly reducing the administrative burden on investigators and allowing them to focus on more critical tasks.

Despite the clear benefits, some FIs remain risk-averse, prioritising the stability of current systems over potential advancements. This conservative stance stems from concerns about data privacy, regulatory compliance, and the reliability of GenAI outputs. Ensuring that GenAI's outputs are accurate and defendable during audits is crucial. Moreover, regulatory bodies must be engaged early to address concerns and create a collaborative path forward. Overcoming this resistance involves robust risk management frameworks, transparent AI models, and continuous monitoring to build confidence in GenAI’s capabilities.

Understanding key terms related to generative AI is crucial for organisational readiness and as you explore solutions with this new technology:

Generative AI: A type of artificial intelligence that can generate new data based on the patterns it has learned from existing data. This capability is particularly useful in creating reports and identifying anomalies in financial transactions.

Foundation models: Large AI models trained on a diverse range of data sources that serve as the basis for specialised applications. These models can be fine-tuned for specific tasks in fraud detection and investigation.

Knowledge bases: Databases that store essential regulatory and compliance information, which AI can reference to ensure accurate outputs. These bases provide context and guidelines that help AI systems make informed decisions.

Retrieval-Augmented Generation (RAG): A methodology where AI models retrieve relevant information from a knowledge base to generate more accurate and contextually relevant outputs. This approach ensures that AI systems have access to up-to-date and comprehensive data.

Despite the potential benefits, some financial institutions remain cautious about adopting GenAI, primarily due to concerns about data privacy, model transparency, and the reliability of AI outputs. Ensuring that AI models are defendable in audits and complying with stringent regulatory frameworks are critical challenges. However, these concerns can be addressed through robust risk management frameworks, transparent AI models, and continuous monitoring.

To gain regulatory and internal support, FIs must engage with regulators early and often, addressing their concerns and demonstrating the value of GenAI in enhancing financial crime and compliance operations. Collaboration with trusted partners and internal IT teams is also crucial to ensure the safe and effective implementation of GenAI.

GenAI solutions must be equipped with guardrails for Foundation Models, which adhere to privacy laws and enforce your financial institution’s data governance and responsible AI policies. These guardrails manage how AI interacts with sensitive data, applying strict protocols to ensure responsible use. Guardrails allow for custom configurations, such as redacting personal identifiable information (PII) to meet specific jurisdictional regulatory requirements. Financial institutions must be in control of their data. Their customised models and inputs are exclusively theirs — never used for model improvements or shared with other customers. Data must be safeguard with encryption, ensuring it remains secure within your financial ecosystem, and data is never shared externally, maintaining a secure and private operational environment.

Implementing GenAI strategically involves focusing on key use cases that can deliver immediate benefits. For instance, automating the generation of Suspicious Activity Reports (SARs) and alert dispositions can significantly reduce the workload on investigators, allowing them to focus on more complex cases. 

GenAI also offers significant opportunities for improving employee morale and job satisfaction. By eliminating mundane tasks and enabling investigators to engage in more interesting and complex work, FIs can enhance the overall employee experience and attract top talent in the field.

As with any new technology, the implementation of GenAI comes with its challenges. These include ensuring staff willingness to embrace change, defending AI models during audits, and maintaining data privacy and security. However, the opportunities presented by GenAI far outweigh these challenges. Standardising work products, elevating job roles, and enhancing risk detection precision are just a few of the benefits that GenAI can bring to financial institutions.

Looking forward, the adoption of GenAI in financial crime and compliance is inevitable. Within the next 1-3 years, we can expect GenAI to become an integral part of investigation processes, with broader applications in detection and anomaly recognition becoming mainstream shortly after. Regulatory flexibility and industry collaboration will be essential to fully realise these advancements.

Generative AI is set to revolutionise financial crime and compliance operations, offering unparalleled efficiency, accuracy, and innovation. Financial institutions must balance the urgency of adopting GenAI with a strategic approach that addresses potential risks and leverages the technology's full potential. By doing so, they can better protect themselves and their customers in an increasingly complex threat landscape. Embracing GenAI is not just about staying ahead of the curve—it's about fundamentally transforming the way financial crime is detected and managed, ensuring a safer and more efficient financial ecosystem for all.

About Glenn Fratangelo

Glenn Fratangelo is the Head of Strategy and Marketing for Enterprise Risk Case Management at NICE Actimize. Glenn is a marketing leader with a deep understanding of technology markets, building and launching technology products, services, and alliances. His global experience spans 15 years across all disciplines of marketing and communications with a career spanning both emerging companies and Fortune 500 corporations that provide B2B solutions with advanced technologies to solve their clients' most complex problems. Throughout his career, Glenn has led transformation and innovation.

About NICE Actimize

NICE Actimize, the leading provider of financial crime solutions, offers innovative technology to protect global financial institutions and regulators. Specializing in anti-money laundering, real-time fraud prevention, and trading surveillance, the company addresses concerns like payment fraud, cybercrime, sanctions monitoring, and insider trading. Explore more at www.niceactimize.com or Nasdaq: NICE.