Good quality data is the foundation of accurate and reliable KYC/AML compliance processes. To keep up with regulatory requirements and avoid fines and reputational damage, financial institutions (FIs) must ensure that the entities they interact with are not a risk to their business. And the data they consult must be accurate, easily accessible, consistent, secure and regularly updated. To speed up their decision-making, FIs have invested heavily in sophisticated compliance teams, and in technologies such as machine learning (ML), ‘fuzzy’ logic tools, natural language processing (NLP) and analytics to clean and process large volumes of data.
Despite these moves, for many FIs, accurately deciding who they can and should do business still carries significant implications in terms of cost, time and resources. Regulatory compliance is a demanding and complex task, given the inconsistencies in rules across different jurisdictions and the sheer volume and scale of client data that many firms must manage. Compounding the problem, information may be outdated, inaccurate, duplicated or of insufficient granularity.
In this report we explore these challenges further, and consider the strategies FIs can employ to manage them. We also examine the processes FIs and technology vendors deploy to source, clean, manage and deliver their KYC/AML data, and the role of technologies such as ML in improving speed and efficiency of analysis and processes. (Note that we focus on vendors that provide raw and enriched KYC/AML data rather than data management software tools.)