An algorithm is a set of well-defined instructions or rules designed to solve a problem or perform a task. In computer science, algorithms are the backbone of any software system, they define how input is processed to produce output.

In modern compliance platforms, algorithms are used to power everything from transaction monitoring and adverse media screening to sanctions list matching. The accuracy, fairness, and efficiency of these processes depend heavily on the quality and transparency of the underlying algorithms.

Algorithms in AI and Machine Learning

When used in artificial intelligence, algorithms do more than follow predefined steps, they learn from data. Machine learning algorithms identify patterns and improve predictions over time, allowing systems like FacctShield to flag suspicious transactions or unusual behavior automatically.

For example, algorithms based on decision trees, neural networks, or support vector machines are used in AI Model Validation and AI in Compliance to evaluate risk, score alerts, and prioritize investigations.

These algorithms must be:

• Trained on high-quality, representative data

• Regularly validated and monitored for drift

• Explainable to regulators and internal teams 

More on the importance of fairness and bias prevention in AI algorithms can be found in this ResearchGate study on algorithmic bias in compliance.

Types of Algorithms Used in Compliance

In compliance, different types of algorithms are used to detect, monitor, and manage financial crime risks. These algorithms range from basic rule-based systems to advanced artificial intelligence models, each serving a specific purpose within the compliance workflow.

While legacy systems often rely on deterministic rules, modern platforms increasingly incorporate machine learning and natural language processing to improve accuracy and adaptability. By selecting the right mix of algorithms, organizations can enhance their ability to identify suspicious activity, reduce false positives, and maintain regulatory alignment across jurisdictions.

Rule-Based Algorithms

These follow predefined if-then rules. They're common in legacy AML systems, such as AML Transaction Rules, where a transaction might be flagged if it exceeds a threshold or originates from a high-risk country.

Machine Learning Algorithms

These include supervised, unsupervised, and reinforcement learning methods. They’re used in adaptive models that improve over time, especially in solutions like FacctView or FacctList, which screen customer data for risk indicators.

Natural Language Processing (NLP) Algorithms

NLP algorithms are essential for analysing unstructured data, such as adverse media or customer reviews. Learn more in our entry on Natural Language Processing (NLP).

Why Algorithmic Transparency Is Essential

Transparency is not just a technical issue, it’s a compliance requirement. Regulators increasingly expect firms to explain how decisions are made by their systems.

This is especially true when algorithms are used for:

• Customer due diligence

• PEP screening

• Alert adjudication

• Predictive risk scoring 

A paper on arXiv emphasizes that black-box algorithms can pose systemic risks if not governed properly. Tools like Explainable AI (XAI) are used to address this by making outputs interpretable by humans.

Algorithms and Regulatory Expectations

Frameworks like the FATF Recommendations and FCA Regulations emphasize the importance of responsible AI and clear decision-making processes. Algorithms used in financial services must be:

• Traceable

• Explainable

• Validated

• Monitored

Non-compliance can lead to fines, reputational damage, and system audits. That’s why AI Risk Management is a growing priority for both regulators and institutions.

Challenges in Algorithm Design and Deployment

Developing compliant algorithms is not straightforward

Challenges include:

• Bias and discrimination: Algorithms can unintentionally replicate social or institutional bias

• Concept drift: Real-world data patterns change over time

• Data quality issues: Incomplete or mislabelled training sets skew results

• Lack of explainability: Complex models like deep neural networks can be opaque

These issues are addressed through tools like Model Governance, regular audits, and internal risk controls, especially in high-stakes areas like AML Screening and Alert Adjudication.