Discipline Comes to Data Management: 6 Considerations for Effective AML Compliance


Data is everywhere. It is available from client onboarding, government agencies, credit bureaus, the internet, and social media. The challenge financial institutions face is not capturing data, but managing it. That means not only ensuring data is accurate, but also understanding how to update, share, store, maintain, archive, secure and analyze the information.

Used intelligently, data is a valuable asset. It can uncover fraud, identify business opportunities and enhance client service.It is also a necessity. With increasing anti-money laundering (AML) compliance requirements and ongoing scrutiny by regulators, institutions are beginning to realize the foundational role of responsible data management for meeting know your customer (KYC) and other regulatory obligations.

Leveraging the full benefits of data requires discipline in data management. Formalizing a data management strategy into a full data governance program that includes the following six attributes will help institutions get started on the right foot.

  1. Accountability
    A successful data governance program requires a “data steward” to be responsible for the strategy and management of data. This has led to the development of a new role over the last few years – Chief Data Officer (CDO). Untethered from its IT roots, this C-suite position is now considered an essential business function. According to Gartner, 90% of large organizations now have a CDO.

    Assigning accountability of data to a CDO or a team of data stewards working together can bring great value to an institution. A CDO can help institutions “build a culture where information is proactively used to meet regulatory demands, manage risk, identify market opportunities and increase shareholder value,” according to a report by PwC.

  2. Accuracy
    Garbage in, garbage out is not a new saying yet many institutions still struggle with gathering and maintaining accurate client data. Client onboarding for KYC compliance is typically inefficient, relying on legacy applications and error-prone manual input. It remains a painful and time-consuming process for both customers and banks.

    Verifying customer information is complex. Yet improper onboarding and poor subsequent data management can lead to erroneous information or gaps that expose banks to undue risk. Not to mention fines for failure to comply with the Bank Secrecy Act, PATRIOT Act, Dodd-Frank, beneficial ownership requirements or other regulatory obligations.

    To improve accuracy, institutions should implement a system infrastructure that reduces manual input and includes multiple points of verification. Establishing internal policies backed by consistent, standardized and repeatable processes through automation will greatly improve accuracy. Standardization also makes it easier to work with data so that it can be aggregated, screened, sorted, managed and assessed for risk such as suspicious activity.

  3. Accessibility
    In order for data to be useful, it must be accessible by relevant parties and lines of business across the enterprise. Consistency and standardization will enable data to be shared more easily, but institutions must still work to dissolve business silos that impede the flow of information. Achieving accessibility across the enterprise is therefore as much a data management issue as it is a strategic issue for the institution.
     
  4. Auditability
    “Know your data” is never more critical than when regulators come knocking. Transparency into the underlying source data provides an audit trail that enables regulators to trace transactions and information flow. When combined with consistent, repeatable and measurable processes, transparency can help institutions demonstrate compliance and avoid fines. It also offers stakeholders a window into the bank’s financials, providing assurance that the bank is not engaging in unlawful or unreported risky behavior.
     
  5. Usability
    Institutions are inundated with data, which shows no sign of abating. While the “cloud” provides a repository that can easily scale as data pours in, institutions must shift their focus from simply capturing data to understanding how to use it to add value.

    Artificial Intelligence and machine learning tools can help. In a report published by Celent, the research firm points out that AI-enabled solutions “offer superior insights through advanced capabilities for analyzing structured and unstructured data.”

    Organizations that incorporate advanced technologies with intelligent screening in their data governance program will be steps ahead of their peers in efficiently and effectively managing risk.

  6. Flexibility
    With a moving target of regulatory changes, banks must be able to quickly adapt to new requirements. Systems should be flexible so that adding more data fields or running new reports for regulators can be done quickly and efficiently. This flexibility is only possible if data management has achieved the aforementioned accuracy, accessibility and usability.

    Thoughtful data governance will enable institutions to go from simple data collectors to data users – being able to extract actionable intelligence for risk management, assured compliance, competitive differentiation, client service and business growth.

Contact Accuity to learn more about how improved input data can support your screening and compliance processes.