Effectively Leveraging AI in Compliance Strategy

Navigating a constantly reshaping threat climate  

Critical mass feels like an understatement when qualifying the explosive growth and global impacts of financial crime. The cost of financial crime to the global economy is estimated to be $1.4 trillion¹. Contending against the pace of financial crime proliferation and the wide reach of well-organized transnational criminal organizations demands a real-time and constantly evolving defense. Rapidly shifting geopolitical tensions fueling increasingly stringent sanctions regimes and ever-advancing technologies add to the dynamic complexities driving up the cost and operations burden of financial crime compliance. Financial institutions estimated spending $34.7 billion on financial crime compliance technology and $155.3 billion on compliance operations in 2024². It is imperative to master agile, more effective compliance measures and capture greater process and cost efficiencies to keep compliance strategies manageable and scalable.  

The frontlines in the fight against financial crimes are being quickly redefined by AI-enabled deepfakes and other easily accessible and affordable AI-based tools. Eighty-five percent of security professionals attribute the rise in cyberattacks to the use of generative AI by bad actors³. Recent stats point to AI-based financial crime causing 51% of surveyed organizations to lose between $5 million and $25 million to AI-based or AI-driven threats⁴. Future predictions indicate fraud losses tied to generative AI could reach $40 billion in the United States by 2027⁵. The ubiquity and unrelenting pace of AI-based threat typologies exponentially increases the operations and cost pressures on already-constrained compliance teams.  
 

Improving compliance outcomes by intentionally incorporating AI  

The market-moving benefits of AI exist right alongside the ominous headlines and threat obstacles. AI is fueling significant opportunities for future-forward financial crime compliance programs to realize measurable efficiency and efficacy gains. A recent study shows 70% of organizations believe AI will drive more revenue⁶ and 50% of financial institutions already use or are planning to use AI⁷. Intentionally incorporating AI-based tools into compliance workflows delivers the competitive advantages of expediting decisions, reducing false positives, surfacing risk faster and concentrating resources on high value activities. Potential applications of AI in financial crime compliance programs include:  

• False Positive Reduction – comparing alert similarity to historic alerts; enriching and structuring input data for more accurate screening matches
• Enhanced Risk Detection – analyzing large volumes of data to detect patterns indicative of risk
• Operational Automation – automating the extraction of relevant information from structured and unstructured data sources; automating report generation; guiding workflows with recommended investigation actions.

AI tools automate manual tasks and quickly analyze vast amounts of information and complex behaviors which allows compliance teams to operate with greater efficiency, consistency and effectiveness across larger volumes of work.  

Realizing the benefits of AI while respecting regulatory guidance

Regulatory requirements around utilizing AI for financial crime compliance and other business functions are still taking shape in several regions. Many regulatory bodies around the world recognize that the adoption of advanced and innovative technologies like AI is key to making compliance functions more effective. The Financial Conduct Authority in the UK, the Hong Kong Monetary Authority and the Monetary Authority of Singapore have all published papers on the advantages of leveraging AI in financial crime prevention efforts, while also emphasizing the importance of safe, responsible AI implementation. 

The EU AI Act of 2024 currently stands as one of the most formalized regulatory frameworks around utilizing AI within financial crime compliance programs. This legislation will likely serve as a template as other regions move toward standardizing regulatory oversight and limitations around implementing AI tools to support financial crime compliance processes. The EU AI Act places a strong emphasis on the type of AI technology a business is using and the use case or application that the AI technology is used to fulfill. Core tenets concerning AI that we are consistently seeing across the global regulatory landscape center around:  

• Transparency and Explainability: Ensuring the inputs and outputs of AI systems are defined and understood
• Governance: Maintaining human oversight of key decisions and actions and thoroughly documenting processes and outcomes
• Human Well-Being: Mitigating risks of negative impact to people by addressing bias, reliability and limitations of AI applications
• Privacy and Security: Protecting personal and sensitive data by understanding how AI models use and store data

Utilizing AI in the financial crime compliance use case inherently demands a higher level of care, concern and transparent controls since decisions determining access to financial services and the financial system materially impact human life and experience as defined by the EU AI Act. Companies building out an AI strategy need to stay cognizant of regulatory developments to ensure the AI tools and use cases they implement optimally serve their business by advancing performance while also aligning with regulatory expectations.

Adding the advantages of AI with purpose-fit approach

There are so many pathways for AI to elevate the performance of a financial crime compliance program: accelerating onboarding and identity verification, optimizing alert efficiency, reducing false positives and automating data collation, synthesis and summarization to inform investigative tasks and enhanced due diligence. Finding the ideal place for AI in your strategy starts with evaluating the end-to-end compliance workflow to identify the biggest pain points and define the risk appetite for solving them. Specifying the size and complexity of your requirements in relationship to your risk appetite can help guide a purpose-fit based decision between a predictive, generative or agentic AI solution path that is proportionate to the problem. Key factors to keep at the forefront of your AI solution decision process include:

• Complete extensive vendor due diligence: Does your vendor meet security and quality benchmarks such as SOC certified and ISO certified?
• Understand model validation and model governance: Outline the data lineage and input data used to developing a model, learn how it is implemented and identify the plans to monitor model performance and model drift.
• Determine if and how the AI solution learns from your data and delineate clear data protection parameters by utilizing a private instance of the solution.
• Consider explainability and transparency at the outset: Establish a clear structure around the information your business requires to document and defend the model outputs.
• Schedule an incremental implementation: Set a deployment pace that leaves space for ongoing assessment and fine tuning to ensure the solution is functioning as intended.
• Plan for ongoing reviews and performance validation: Define benchmarks, controls and metrics to ensure the solution is effectively meeting the specific purpose and business needs.

Selecting the right-sized, and often the smallest, AI solution model that effectively addresses your pain points is an approach that respects considerations around cost controls, ease of implementation and scalability. Implementing AI where it offers purpose-fit applicability, delivers measurable impact and maintains strong alignment with your strategy is a good starting place for adding the advantages of AI into your financial crime compliance program.

Ready to take the next step on your AI journey? Make plans to attend our upcoming webinar where we will explore how to effectively leverage AI within compliance processes and our guest experts will present real life use-cases to help you get started!

References:

1. ma-kyc-chartis-financial-crime-and-compliance50-2024.pdf
2. IT and Operational Spending on Financial Crime Compliance: 2024 Edition | Celent
3. Impact of Artificial Intelligence on Criminal and Illicit Activities
4. https://www.bankingexchange.com/news-feed/item/9974-ai-financial-crime-activity-on-the-rise
5. Deepfake banking and AI fraud risk | Deloitte Insights
6. https://www.bankingexchange.com/news-feed/item/9974-ai-financial-crime-activity-on-the-rise
7. https://www.kroll.com/en/insights/publications/ai-financial-crime-prevention