The Build vs. Buy Reality: Why Banks Need Specialized AI Partners in AML

Build or Buy AI Augmentations

Build AI augmentations in-house or partner with specialized vendors like Hawk? That's the question that banks with data science teams need to answer as they look to enhance their anti-money laundering (AML) capabilities with artificial intelligence (AI). 

While leveraging internal data science teams may seem logical – after all, they are already in place and understand your organization – the reality of building AI for AML detection involves industry-specific complexities that go beyond general data science expertise. These complexities need to be considered end-to-end during AI development, training, governance approval, and operationalization.

Complexities in Developing AI Augmentations for AML


Building high-performing AI solutions for AML requires more than just skilled data scientists. To ensure AI augmentations deliver performance boosts that are accurate, compliant, and efficient, banks need to address the following important questions when developing AI for AML:

  • How long will it take to develop accurate AI models?
  • Will the solution meet internal and regulatory governance requirements?
  • Does it easily integrate with existing AML systems?
  • Will the developed AI solution perform strongly once put into action?

Banks that decide to use in-house resources to deliver AI augmentations can face one or more of the following challenges:


Inaccurate AI Models


Money laundering patterns evolve constantly, and training AI models on unfiltered, historical AML decisions can lead to errors and serious flaws in model performance. Transaction patterns from the COVID-19 period, for instance, may no longer reflect current criminal behavior. Typically, data science teams don't have the domain expertise to spot these AML-specific data nuances, which are fundamental to training AI models properly.


Another example: AML expertise is also needed to ensure data quality and quantity is adequate enough to spot differences in behavior between customer segments, such as corporate customers acting differently from individual clients.


It cannot be overstated: the quality of your AI model development, training and maintenance is critical to success. If the models are not trained correctly, the results will not be accurate. When tackled in-house, it can take considerable time to determine all the industry-specific data requirements needed to build accurate AI models in AML and distinguish between correlation and causation. This has to be borne in mind.

 

Model Governance and Explainability Challenges


AI solutions built for data-sensitive use cases, like AML, require extensive documentation to meet regulatory standards and internal model governance (e.g., bias audits, ethical considerations, explainability). Poor documentation can significantly delay model governance approval, prevent a model from being put into production, or even lead to compliance breaches when approval processes aren’t robust.


While it's possible for in-house teams to build AI augmentations that guarantee model governance and regulatory acceptance, getting there is challenging and often includes extended development timelines, multiple setbacks, and substantial time and resources.

 

Complex and Lengthy Integrations


Banks usually already have AML technology in place, often using multiple systems for rule building, detection of different use cases, case management and investigations. Any AI solution introduced into this tech stack needs to integrate and communicate with each endpoint in this setup – otherwise, even the best-trained AI models won't deliver any performance boost.


It can take considerable amounts of time to develop the right architecture around AI augmentations, as it can be extremely challenging and often requires strong IT support, which increases the complexity and duration of AML in-house AI development. This could mean waiting for years rather than months while AI models are integrated with existing AML systems and even then there’s a risk that the models don’t perform outside the lab.

 

Operational Realities


Laboratory success doesn't always translate into real-world performance. Hawk has seen many in-house AI projects struggle with:

  • Fast data processing demands
  • Scalability across different banking segments and customer populations
  • Model maintenance and retraining cycles

At this stage, banks usually have already invested tremendous amounts of resources into their AI developments – but actual success is still not guaranteed. The risk of not being able to operationalize the solution remains real, leading to the project failing close to the finish line.

What Banks Need from Vendors for AI in AML


While building AI solutions in-house can be attractive, partnering with a specialized vendor can help banks solve the previously mentioned challenges. An AI project is more likely to succeed if banks have access to:

  • Data science expertise combined with AML domain knowledge to deliver strong-performing AI models
  • Integration capabilities that have been specifically designed to manage the complexities of existing AML tech stacks for fast implementation
  • Proven technology that scales and remains flexible for fast data processing, model maintenance, and solution improvements
  • Alignment with internal model governance requirements and full transparency and AI explainability to users, auditors, and regulators worldwide

The Hawk Advantage: The AML AI Overlay


Hawk's AML AI Overlay has been built with these enterprise requirements in mind and offers banks a safe and effective way to see results from AI quickly. Here are key points that financial institutions appreciate about the overlay approach:

  • AI built for AML: An AI detection system tailored to financial crime and industrialized for fast model training, internal approval, and strong results
  • Real performance boosts: Operational workloads reduced by up to 50%, detection accuracy rates proven to exceed 85%, and false positives reduced to as low as 15% in previous projects
  • Seamless integration: Preconfigured connectors/APIs that streamline integration with existing AML systems – no need for replacements
  • Regulator-ready detection: Built with AI that is fully transparent and explainable in human language at the AML case level and within model documentation
  • Predictable outcomes: Streamlined workflows for feature selection, model refinement, and testing guarantee performance

Making the Strategic Choice With the AML AI Overlay


Hawk's AML AI Overlay has proven to be faster and more cost-effective than in-house AI optimization efforts for AML systems. This is reinforced by Hawk's experience in AML detection combined with leading data science approaches that address the complexities of developing compliant and high-performing AI solutions for AML. 

The AML AI Overlay offers banks that want to see rapid AML improvements a tailored gateway into the world of AI detection without the need to replace existing technology. 

Please visit the AML AI Overlay page or contact us today to learn more.
 

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