Why data matters to AML processes
Earlier this week we saw how important AML is for BFSI organizations, in this article, let’s try to understand the importance of good data in the fight against money laundering
Complex, real-time insight, analytics and customer identification are part of an overall data management platform. Trusted data, readily available with actionable insights, can help ensure that investigators don’t waste time on false positives. At each stage in an investigation, from initial flag-down to conviction, data plays a pivotal role. In order to make rapid and informed decisions, investigators need different data at each stage. Data volumes grow at each alert progression, with transactional and historical data coming from multiple systems inside and outside the organization. Constant change is a byword in AML - watchlists expand daily, marketplaces go global, customers interact through new channels, fraudsters find new tactics and the authorities evolve regulation further.
Good data—and how to get it
There’s no shortage of data—but what investigators need is quality data, intelligently presented in a timely manner. Good data must come from all the right sources, and is available at the right times, and must be “normalized” and centralized correctly, so that it’s consistent across different systems. The problem with this scenario is that in the real world, data is used by multiple compliance-critical applications, often with different data models. Many data integration initiatives focus on moving the data to a single “target,” or repository, but don’t provide the needed focus on the actual quality of the data being moved. For a compliance department, this is critical, because bad data quality means that potentially risky transactions may not be flagged correctly. This is why any AML initiative must focus on both data quality and data integration.
Within that focus are several key areas: improving the completeness and accuracy of data through reference content (including Watch and PEP lists); sophisticated identity resolution capabilities to truly identify customers; the ability to handle international data sets and standards; powerful content profiling; and solid, business-grade reporting functionality. Actionable and timely insight from complex event processing is critical – to identify and filter ever-changing patterns of suspicious behavior, and to trigger the right alerts. And, management-level reports should measure completeness, conformity, consistency, accuracy, duplication (including Know Your Client/Counterparty and Know Your Employee relationships), transaction ranges and integrity across all data.
Another critical need is ‘Data Quality’ of the alerts triggered by a separate AML package. Most AML alerts are plagued by poor quality and usually manifested as a high incidence of false positives. The data quality and data integration platform can perform complex event processing to filter false positives generated by the AML package. This type of post-production of AML alerts can involve data enrichment, analysis, prioritization, notification and reporting of alerts. By ensuring and enhancing the data quality of alerts, this type of platform can double analyst and investigator productivity by allowing them to focus on real cases.
To fulfill these critical needs, financial services organizations must choose a data integration platform for AML that supports alerting and reporting on event and identity insight, data quality, data measurement and monitoring, scorecards, and data grading. A good data management solution should also provide the ability to “grade” or compare data on a row-by-row basis to assess its fitness for specific business processes. For example, Grade 2 data may be suitable for use in a marketing system, but risk management or financial and regulatory reporting may require higher quality.
This leads to the areas of measurement and monitoring, which are central to data quality because they ensure that data related to new and existing clients along with their related transactions are continually reviewed. These processes provide a foundation for data quality improvement as well as for understanding the appropriate uses of data. Scorecards are useful in tracking data quality improvements over time and can be integrated with dashboard reporting tools, as well as with third-party business intelligence solutions, to further enhance reporting capabilities. And, the natural evolution of Data Quality Scorecarding is the automated Data Quality Firewalls. These firewalls are ideal for stopping poor data getting into and out of compliance engines and allowing investigators to target potentially fraudulent behaviour at an earlier point in the data flow.
Build AML solutions on a foundation of good-quality data
Financial businesses must learn to demand sophisticated and flexible AML solutions to protect themselves from being used for money laundering. But, before deploying a top-notch AML system, an organization needs to build a platform of integrated, high-quality data. By working with the highest-quality data, the financial institution can ensure its AML systems are working at the highest level, and can trust that data meets regulatory requirements. By creating a system that meets these tests, institutions can manage their Customer Information Profiles (CIP) and their Currency Transaction Reporting (CTR) systems – and defend their business effectively against the determination and innovation of skilled money launderers.
So back to the original question - how do you catch a rat? By making sure you have the necessary tools and the most up-to-date systems to track, bait and capture these unwanted visitors.
- Ten Trends Redefining Enterprise IT In 2018
- 5 Ways AI Can Live Up To Its Promise In 2018
- 70% Indian Firms To Deploy AI By 2020: Intel
- Why Cloud Adopters Need Visibility Into Their Network
- Cyber Security Jobs At Premium As India Goes Digital
- Enterprise Networks: Things To Focus On In 2018
- Trends In Information Management: An India Perspective
- Cyber Security Predictions For 2018
- SpiderOak CEO Warns Of 10 Cybersecurity Threats For 2018
- Uber Data Breach: Accountability, Corporate Ethics In Question