In the fight against financial crime, artificial intelligence (AI) and analytics often capture attention, but the real foundation of effective anti-money laundering (AML) programmes lies in the quality of data.
Napier AI recently outlined six steps to ensure high-quality data for effective AML.
Without reliable, timely, and well-structured information, even the most advanced technologies fail to detect genuine risks. High-quality data enables compliance teams to separate meaningful signals from the background noise, ensuring accurate screening and monitoring.
However, maintaining data fit for AML purposes remains one of the most complex challenges faced by financial institutions. Fragmented data silos, outdated legacy systems, and restrictive regulations all hinder accessibility and consistency. Bringing together information across business lines and jurisdictions can be daunting, making governance and quality assurance critical. The challenge is not simply to centralise everything, but to make sure that the right data is available when and where it’s needed.
Data quality directly determines the effectiveness of screening for sanctions, politically exposed persons (PEPs), and suspicious activity, Napier AI explained. Poor data — such as outdated customer profiles, inconsistent record formats, or incomplete transaction details — undermines risk assessments.
Regulatory frameworks like the NYDFS Part 504 and OCC SR 11-7 Model Risk Management have increased expectations around data governance, testing, and transparency, demanding proof that systems align with institutional risk appetites. Weak data practices can result in false positives that overwhelm teams or false negatives that allow illicit transactions to go unnoticed.
To help financial institutions strengthen their AML data strategy, Napier AI highlighted six practical steps to follow.
First, connect, don’t consolidate. Rather than forcing all data into one repository, adopting an API-first approach allows systems to draw the data they need in real time. Second, accept that data will never be perfect. Designing systems that can handle inconsistent or incomplete inputs is more realistic than striving for unattainable uniformity. Third, take a risk-based approach. Different screening types — for example, sanctions versus PEPs — require distinct configurations to balance speed, accuracy, and cost-efficiency.
Fourth, validate and assure data continuously. Institutions should regularly check data validity, freshness, and completeness to ensure meaningful analysis. Strong governance processes should assign clear ownership and establish lifecycle management standards. Fifth, leverage external insights. Partnering with external vendors and consultants can help organisations benchmark against industry standards and benefit from lessons learned across the sector. Finally, use AI-powered solutions. Machine learning models and natural language processing tools can help cleanse, interpret, and structure data before it reaches the screening layer — enhancing results without replacing foundational governance.
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