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In the fight against financial crime, artificial intelligence and advanced analytics often steal the spotlight. But behind every effective screening or monitoring system lies a far more fundamental element: data. High-quality, timely, and well-structured data is what enables compliance teams to distinguish genuine risk from background noise. Without it, even the most sophisticated tools will fail.
Yet ensuring data is fit for purpose is one of the most complex challenges financial institutions face. Data silos, legacy infrastructure, and regulatory hurdles all contribute to the problem. So, how can compliance teams take a practical approach to improving data quality and accessibility for screening?
Why data-quality and governance are key
Screening for sanctions, politically exposed persons (PEPs), or unusual customer behavior is only as good as the data behind it. Poor-quality inputs, like outdated records, inconsistent formats, or incomplete information generate unreliable results. Regulators are also demanding effectiveness, with frameworks like NYDFS Part 504 and OCC SR 11-7 Model Risk Management requiring strong governance, testing, and proof of risk alignment.
The consequences are significant: false positives that overwhelm teams, or worse, false negatives that let illicit activity slip through undetected.
Financial institutions often struggle to bring together a holistic view of customer behavior. Customer and transaction data may sit across multiple systems, owned by different teams or even different geographies. Legacy platforms make it difficult to consolidate or share data, while data protection obligations limit what can be shared across borders.
The reality is that fully centralised data warehouses or “lakes” are rarely a complete answer on their own. Many firms have learned the hard way that forcing everything into a single repository can be costly and still leave governance issues unresolved. Instead, the more practical goal is ensuring the right data is available at the right time, in the right context — regardless of whether it sits in a lake, warehouse, or distributed system.
6 practical strategies to ensure data is fit for purpose
Achieving this requires a mindset shift from attempting to fix all data at once, to focusing on risk-based orchestration. Here are some practical steps firms can take:
1) Connect, don’t consolidate
Rather than forcing all data into a single hub, firms can take an API-first approach to connect sources directly into the risk decisioning layer. This enables screening systems to pull the data they need, in the format available, at the moment it’s required.
2) Accept that data will never be perfect
Data will always vary with some transactions arriving in real time, others in batch; some sources using fixed formats, whereas others relying on free text prone to human error. Instead of waiting for perfect standardization, build systems that can handle evolving data standards and incomplete inputs.
3) Take a risk-based approach
Not all data needs to be treated equally. For example, sanctions screening should be configured differently to PEP screening. A multi-configuration approach allows firms to screen sanctions lists against high-volume transactions in near-real time, while handling PEPs against internal watchlists with more nuance. This not only improves detection of true risks but also reduces unnecessary processing costs.
4) Validate and assure data continuously
Before relying on data, firms must check its validity, reliability, and auditability. Key questions include:
- Has the data been processed in ways that strip out useful information?
- How old is it, and does it need refreshing?
- Is it complete enough to support meaningful analysis?
Continuous testing ensures poor-quality data doesn’t undermine screening and monitoring results. Strong data governance is essential and should be done through proper internal process for ownership and lifecycle management.
5) Leverage external insights
Relying solely on internal data limits your perspective. Vendors and consulting partners often bring aggregated insights from across markets, offering a broader view of emerging risks. Pre-set configurations, benchmarked against industry best practices will help firms benefit from lessons learned elsewhere through incremental changes without having to rip and replace entire systems.
6) Use artificial intelligence powered solutions
AI can play a powerful role in improving screening results, but only with the right foundation. Machine learning models can detect inconsistencies across multiple data sources, flag missing information, and help cleanse inputs before they reach the screening layer. Natural language processing can interpret unstructured data fields, reducing errors introduced by free text. AI sharpens the system, but it cannot fix a broken foundation.
For financial institutions, the path forward lies in building flexible systems that orchestrate data from multiple sources, apply risk-based screening configurations, and continuously validate results. By accepting that data will never be perfect, but can always be improved, firms can ensure they are using the right data, at the right time, to identify and manage risk effectively.
Learn more about using a risk-based approach for financial crime compliance
Photo by Pawel Czerwinski on Unsplash
