4 Big Data Compliance Mistakes That Financial Service Organizations Make
There are few industries subject to a regulatory landscape as complicated and rigorous as financial services. Tasked with preventing crimes such as money laundering and fraud, among other obligations, financial service firms face a dizzying array of data compliance related rules and reporting obligations. The result is a complex patchwork of regulatory requirements that make addressing the needs of blanket rules like GDPR seem downright simple.
Adding to the strain felt by financial service firms, compliance teams are being asked to address the risks they face in more efficient, cost-effective, and proactive manners—fulfilling obligations such as Know Your Customer as a first line of defense.1
Meet ever-growing regulatory requirements by avoiding the following data compliance mistakes.
Mistake #1: Treating Data & Analytics as Supportive Endeavors Only
Unfortunately, many financial service enterprises cannot successfully address data-related risk because they:
- Don’t see data as a critical compliance and business driver.
- Have yet to name an executive to own the optimization of data and analytics.
- Don’t prioritize the development of data-related enterprise competencies.
Financial service firms must shift their approach to data as the true crux of compliance; take steps to improve data literacy across the enterprise; and implement more holistic approaches to the organization, storage, and accessibility of data from all sources.
Mistake #2: Treating Data Compliance as an End in Itself
In financial services, data is a source of opportunity as much as it is a risk. By seeing data purely as a compliance risk, organizations miss out on key opportunities to delight their customers and leverage data to reveal winning insights and ideas.
But neither goal should exist in isolation. By giving greater consideration and priority to how data is approached from the outset—and by taking steps to standardize how data is processed and made accessible across the enterprise—organizations can knock down the siloes that stand in their way. This will not only aid in compliance but will also improve the availability of actionable business intelligence—doubling down on the payoffs of effective data management.
Mistake #3: Neglecting to Address the Challenge of Unstructured Data
When every client onboarding form and new application, every contract, and every query expands the customers’ data footprint, the problem of how to manage high volumes of sensitive data becomes especially pressing. The challenge of unstructured data complicates the equation even further.
From a data compliance perspective, unstructured data is the elephant in the room. With 80% of unstructured data hiding in images, email, CAD documents, MS Office applications, and within various fileshares, most financial service organizations are sitting on a landmine of risky data.
Unfortunately, because they often require manual lift and typically have high error rates, traditional tools are not equipped to tap into the messy world of unstructured data.
Mistake #4: Relying on Traditional Tools to Manage Unstructured Data
Expanding on the points outlined above, simplistic tools cannot provide the scalability or flexibility needed to address evolving business needs related to data, analytics, and compliance.
When it comes to finding and addressing data compliance risk, simplistic image/OCR-based tools are inadequate. Another mistake that organizations make is relying on traditional capture tools that flatten image-based content and re-OCR it—which leads to critical data errors and omissions.
Financial service organizations must evaluate data-preparation tools for their ability to scale from self-service models to enterprise-level projects. To reduce risk and implement scalable data compliance practices, companies must choose their solutions wisely and give preference to tools that can:
- Coexist with other data-management tools.
- Capture, analyze, and share metadata and lineage.
- Deliver robust data-enrichment capabilities, including entity extraction and capturing attributes from the integrated data, among other capabilities.
- Manage born-digital content and work with the original file type—whether it’s a Word document, an email, a PDF, or another document type.
Organizations should also look for a highly accurate OCR solution that can manage large volumes of content and automatically standardize both image-based and born-digital documents into fully searchable data.
Digital business thrives on data and data analysis, yet many enterprises find their ambitions inhibited by their earlier behaviors. To reduce data compliance risk, financial service firms must rethink their approach to data. That means taking steps to find and manage unstructured data and ensuring that the right tools and processes are in place to optimize data as it is created. By making strong data governance a core competency and objective, organizations can swiftly respond to emerging compliance rules.
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1Accenture: 2019 Global Financial Services Consumer Study