Best Practices for Data Governance for Financial Institutions

financial data security

The power of information technology that helped grow the global financial system exponentially has also enabled complexity in last 10 years more than what was built over centuries. With the ever-competitive pace of the financial sector and the blazing speeds of technological advancement trying to find productive coherence, there is a renewed emphasis on data governance strategies.

First of all, it’s important to establish the fact that despite everyone sharing the same opinion on the technical definition of data governance, its significance holds a unique interpretation in the minds of different people.

For some financial institutions, it is the act of establishing governance bodies and councils, while others regard it as the process of defining data ownership and workflow. Hence, some of them focus only on master data management and quality control programs, and some financial firms believe in taking a more holistic approach to data governance.

Why Data Governance Could Be a Boon for Financial Institutions

Ideally, data governance covers both the systematic and formal management of processes necessary for effective information management. However, businesses with a more pragmatic viewpoint choose to facilitate certain regulations that justify ROI over others.

For the finance industry, the most important governance objective is to ensure maximum accuracy and security in aggregating data for risk analysis and reporting.

Regulatory commissions around the world are wising up to the challenge of data management and setting the laws and regulations in place to keep companies handling sensitive electronic information in chec. For instance, the Office of the Superintendant of Financial Institutions in Canada (OFSI) published a set of data maintenance principles following the International Ratings-Based (IRB) approach.

Despite data governance being typically classified as an IT department priority, a successful and valuable initiative must be driven by business needs to provide direction and streamline the organizational workflow.

Managing expectations from a data governance program takes quite a bit of commitment and familiarity with technical and enterprise limitations. With the help of quantifiable key performance indicators (KPIs) and metrics, financial organizations can get their data governance act together.

So what business prerogatives really drive data governance initiatives? Here are some of the most common reasons:

  • Improved risk management and regulatory reporting solutions
  • Address mergers, acquisitions and divestitures
  • Superior analytics to harness strategic business insights
  • Enable real-time decision making
  • Avoid excess expenditure
  • Promote cross and up-selling
  • Maximum compliance with data regulations
  • Lower customer attrition
  • Boost customer service experience

Financial stakeholders are dependent on a host of business objectives aside from the ones given above due to the sheer complexity in data management involved. Therefore, the priorities and flexibility in data governance policies for each financial organization may vary.

However, when it comes to keeping their balancing act between revenue, efficiency, and compliance in place, these few data governance practices are guaranteed to bring good results in every kind of financial organization:

Accurate Risk Assessment
The risk and assurance teams working under financial companies are responsible for figuring out the amount regulatory capital reserves when building credit risk models. In order to do this, they must be able to leverage the underlying data and attest to the data quality by installing appropriate monitoring, controls, and alert systems.

Winning the confidence of regulators is crucial to the reputation of financial companies, and the right alignment of data and processes is necessary in determining the regulatory capital calculations.

Process Efficiency

The hunt for superior data quality can take quite a lot of time, and in the world of finance, time is money. Extensive manual verification of reports isn’t the best way to make use of your organizational resources as this process is also prone to human errors, which impact data quality.

A solid data management strategy must be equipped with topnotch automated analytic and reporting tools to mitigate this problem. The operational model must be able to provide high-level data governance organization with clarity in structuring responsibilities and processes according to the roles of individuals in the financial organization.

Developing a Data Governance Management Manual (DGM) is a great foolproof way to lay down the blueprint for your organization’s data management strategy. By listing down the objectives, tools, challenges, roles, and functionality available, it becomes easier to create and enforce policies and procedures that address data quality, issue escalation and metadata management.

Anti-money Laundering

Unchecked incidents of money laundering are one of the biggest fears for finance companies since a scandal like that can sink their business overnight. In order to curb this, data governance must encompass end-to-end currency transaction reporting protocols.

Even clients depositing money from different branches, various deposit mechanisms, or even minor alterations in their names must be tracked. Systems monitor wire transfers must verify the authenticity of countries or individuals as per the Treasury’s Office of Foreign Assets Control list using fuzzy matching.

Author: Prashant Bajpai

image courtesy: njaj/