What It Takes to Train High-Performance AI Models for Compliance

In the complex and fast-evolving world of financial crime compliance, artificial intelligence offers a path to greater accuracy, efficiency, and scalability. Yet building and maintaining an effective AI model to help detect and prevent financial crime is far from straightforward. It demands more than just cutting-edge algorithms — it requires deep domain expertise, large volumes of data to train on and control over quality, and a relentless focus on transparency.

To understand what it takes to build an AI model capable of performing reliably in demanding, high-performance scenarios like financial crime detection, let’s take a look at the key characteristics of effective AI models.

Data Quality: the Foundation of Reliable AI

It’s a well-established truth in the field of artificial intelligence that the quality of training data is one of the most decisive factors in determining a model’s effectiveness. Clean, relevant, and well-labelled data is not just a technical requirement —  it’s the foundation for building AI systems that are accurate, unbiased, and reliable.

In the case of AI models built for specific work in regulated domains, such as financial crime compliance, the type, structure, and quality of data are critical not only to performance but to ensure regulatory acceptance as well.

According to Patrick Kirwin, Head of Product Management at Silent Eight, not only is data on the history of alerts and their resolution needed, ‘That data also needs to be labelled so the model can learn from it and predict similar outcomes again in the future’. 

Labelled data includes cases where outcomes are known, such as whether a flagged alert was a true risk or a false positive. This allows the model to learn from past decisions and refine its ability to distinguish between legitimate threats and benign anomalies. In an industry with an average false positive alert rate of over 95%, it is equally important to ensure there are sufficient true positive examples for the model to learn from. A failure to include high-quality, labelled data leads to poor generalisation and decreased effectiveness, ultimately eroding trust in the system.

Out-of-the-box Intelligence: A Global Advantage

Training AI from scratch is not only labor-intensive but also time-consuming —  especially when models must function effectively across different jurisdictions and languages.  

‘What allows us to deploy very quickly and especially to demonstrate proof of value very quickly is that our models have been trained on global data sets that are representative for most of the banks we work with across the globe,’ Kirwin explains. This foundation allows new clients to achieve high performance immediately, with only minor fine-tuning required to meet specific risk tolerances or data nuances.

‘We have trained our models on representative data across global banks. In the case of name screening, we have learned from millions of alerts at institutions globally in every jurisdiction across the world including the most challenging languages.’
Patrick Kirwin, Head of Product Management

Navigating Complexity in Name Screening

Name screening remains a challenging aspect of financial crime detection, especially given the global nature of banking. Many systems struggle with non-Latin scripts or culturally unique naming conventions. Silent Eight’s AI models are specifically designed to handle this complexity by identifying the linguistic family of a name and applying tailored models.

This approach enables accurate matching across languages and scripts — be it Arabic, Chinese, or Cyrillic —  while minimising the likelihood of both false positives and overlooked threats. It’s a critical capability in an era where sanctions lists evolve rapidly and new names are added constantly.

Addressing Bias and Ensuring Performance

No discussion about ethical AI is complete without acknowledging bias. In financial crime detection, biased models can result in unfair treatment of customers or unnecessary scrutiny based on geographic or demographic markers. These risks are heightened because the models are trained on outcomes from human investigators — outcomes that may themselves be subject to unconscious biases.

How can bias be mitigated in AI models? Kirwin explains that the key is in selectively choosing which attributes are included during training. Attributes that do not influence the outcome meaningfully, or which are prone to introducing bias, are excluded. In the case of Silent Eight, moreover, proprietary toolkits are used during model development to monitor and evaluate bias, and these findings are transparently documented in model risk management reports shared with clients.

Explainability: A Non-Negotiable in Compliance

Perhaps the most important differentiator in AI for financial crime detection is explainability. In heavily regulated industries, black-box models — those that offer results without justification —  are unacceptable. Financial institutions must be able to defend every decision to regulators, auditors, and internal stakeholders.

Silent Eight’s models are deterministic, not probabilistic. Rather than offering a confidence score (e.g., a 98% likelihood of a true match), they deliver definitive outcomes —  true match, false positive, or uncertain —  with written justifications that any analyst can understand. This clarity empowers analysts, enhances quality assurance, and ensures smooth regulatory audits.

Explainability is important not only to the relationship with regulators, but also essential for internal teams to understand AI decisions. 

‘Having explainability in writing is really valuable not only for compliance analysts, but for the bank’s quality assurance teams as well. It makes their job immensely easier and faster because they read the explanation and decide if they agree or not. If what they’re getting is a black box or a simple score, they have to conduct the entire investigation themselves to verify if the machine made the right call’ explains Kirwin.

‘We offer explainability at every level in the value chain. An analyst can pick up any decision from us, read exactly why we made that decision, understand it and then move on.’
Patrick Kirwin, Head of Product Management

Monitoring Model Drift and Staying Ahead of Change

The best AI models are designed to evolve. Data changes. New transaction types emerge. Sanctions regimes shift overnight. Silent Eight tackles these challenges through real-time performance monitoring, backed by intuitive dashboards that track key metrics such as solve rate —  the percentage of alerts resolved without human review.

In the event the outcomes of a high performing AI model falter, full transparency allows clients to quickly investigate whether changes in upstream data quality, new customer behaviours, or missing data fields are at fault. Silent Eight provides dedicated tools to help pinpoint the exact source of issues, enabling rapid remediation. The results of those processes are then fed into the model, so it continuously improves over time. 

Purpose-Built AI to Combat Financial Crime

As financial institutions face growing volumes of data and tightening regulations, a tailored, transparent, and continuously learning AI system to help detect and prevent financial crime is no longer a luxury —  it’s a necessity. 

Silent Eight was purpose-built for a singular focus of helping fight financial crime. By combining rigorous data stewardship, robust monitoring, and explainability at every level, it offers a path to efficient financial crime detection at scale.

Table: Key Data Types for FinCrime AI Models