How to Implement Data Governance in Small and Mid-Sized Banks

Data governance is crucial in every industry, but the stringent regulatory requirements in the financial sector make well-governed data one of the highest business-critical priorities. Globally, banks are required to abide by specific regulatory practices, and these requirements are stringent.

Ultimately, banking regulations are tailored to the amount a bank has in assets. Fundamentally, this is because smaller banks deal with banking activities, like loans and deposits, that have less of a potential impact on the broader economic climate of a jurisdiction than larger banks that deal with securities.

Comprehensive data governance is critical, no matter how big or small your bank is. However, when a US bank exceeds $10 billion in assets, the requirements from regulators ramp up considerably. Despite being eased by the 2018 Economic Growth, Regulatory Relief, and Consumer Protection Act, the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 laid out strict regulatory policies for banks breaching $10 billion in assets, many of which still stand.

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In this article, we'll explain how small to mid-sized banks can implement data governance to ensure they remain compliant. And there are other benefits too. Read on to learn more.

What are the core banking regulations in the US?

Small to mid-sized banks must be aware of a comprehensive range of banking regulations that, while different, all carry significant penalties for non-compliance. They include the following:

• The Truth in Lending Act demands that lenders disclose comprehensive details about loan terms and the cost to borrowers.
• The Equal Credit Opportunity Act (ECOA) is in place to ensure that banks don't follow discriminatory practices when deciding who to lend to.
• The Fair Credit Reporting Act (FCRA) protects customers' credit information, ensures access to credit scores, and allows them to make amendments when something is wrong.
• The Electronic Fund Transfer Act (EFTA) covers electronic banking transactions, like debit cards, ATMs, and online banking activities.
• The Bank Secrecy Act (BSA) requires banks to implement anti-money laundering procedures.
• The Sarbanes-Oxley Act of 2002 is one of the best-known regulations focusing on corporate governance and transparent financial reporting.
• The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 aims to reduce risk in the financial industry and protect consumers from malpractice.
• The Payment Card Industry Data Security Standard (PCI DSS) governs the safe and secure processing and storage of consumer cardholder information.

How to develop a data governance program?

Thankfully, implementing comprehensive data governance to ensure regulation in a small to mid-sized bank is far less complicated and expensive than it used to be. That's because the manual approach has been resigned to history.

Today, data governance tools, like OvalEdge, not only break down the cost of data governance by making it easy to implement incrementally but also simplify the implementation of data governance in your organization. We've surveyed several banks that use OvalEdge for end-to-end data governance, and on average, only one paid data governance position was required; the rest of the data governance team were volunteers.

While it is advised to bring in consultancy services to ensure your data governance program is working correctly, your volunteer team will be fully invested in the process because of the benefits to the departments.

What are the broader benefits of data governance?

Of course, compliance is the primary driver for data governance in the banking sector, but it isn't the only one. When data is of high quality, one of the consequences of data governance and a requirement when preparing it for compliance, it can be used as a strategic asset.

Related Post: Implementing Data Quality for Fair Lending Compliance in Banking

As AI matures, more new technologies will help you add value to your data. For example, you might find a better AI-powered credit scoring program and make data-driven decisions more quickly. However, these mechanisms need high-quality data to run efficiently.

Before, when you wanted a technology, purchasing it was a simple process. However, an extensive dividing line enabled banks with large budgets to get the competitive edge because of the high price many of these technologies demanded. Today, the playing field is more level, but there is a caveat: the technology runs on your data. So, if your data is of low quality, you won't be able to leverage the technology sufficiently, and your competitors will take advantage.

Competition is based on operational efficiency, which depends on today's technologies. While everybody has access to the same technology, banks with comprehensive data governance in place will have a competitive advantage because they can dramatically reduce time to market.

How to implement data governance with OvalEdge

Lineage building is the core process in preparing data for compliance in the banking industry. This was a costly undertaking, but with a data governance tool like OvalEdge, the same task can be carried out at a much lower cost.

With OvalEdge, users crawl all the metadata and collate this knowledge into a centralized data catalog. From here, along with lineage building, you can implement a series of data governance programs that constitute end-to-end governance in your organization.

1. Data literacy

Ensure that everyone in your organization has governed access to data via self-service. This helps users learn how to use data to develop new strategies, collaborate on projects, and drive a culture of data-driven decision-making.

2. Data quality improvement

Make your data high-quality and actionable with an ongoing data quality improvement program embedded into the OvalEdge platform.

3. Data privacy and access 

Data access management features enable you to develop policies that can be implemented automatically, while ad-hoc access management enables you to grant specific access requests. Use AI algorithms to identify and classify PII and other sensitive data and use this knowledge to allow secure access to verified users.