In working with clients in the financial services business, I’ve noticed that there is a common set of reasons why they adopt data governance. When it comes down to proving value of data management, it’s all about revenue, efficiency and compliance.
Number One – Accurate Risk Assessment
Based on new regulations like Sarbanes and Dodd-Frank, a financial services company’s risk and assurance teams are often asked to determine the amount regulatory capital reserves when building credit risk models. A crucial part of this function is understanding how the underlying data has the on the accuracy of the calculations. Teams must be able to attest to the quality of the data by having in place the appropriate monitoring, controls, and alerts. They must provide regulators with information they can believe in.
Data champions in this field must be able to draw the link between the regulations and data. They must assess the alignment of data and processes that support your models, quantify the impact of poor data quality on your regulatory capital calculations, and put into place monitoring and governance to manage this data over time.
Number Two – Process Efficiency
If your team is spending a lot of time checking and rechecking your reports, it can be quite inefficient. When a report generated conflicts with another report, it may bring some doubt to the validity of all reports. There is likely a data quality issue is behind it. The problem manifests itself as a huge time-suck on monthly and quarterly closes. Data champions must point to this inefficiency in order to put in place a solid data management strategy.
Number Three – Anti-money Laundering
Financial Services companies need to be vigilant about money laundering. To do this, some look for currency transactions designed to evade current reporting requirements. If a client is making five deposits of $3,000 each in a single day, for example, it may be an attempt to keep under the radar on reporting. Data quality must help identify these transactions, even if the client is making deposits from different branches, using different deposit mechanisms (ATM or Customer Service Rep.) and even when they are using slight variation on their name.
Other systems monitor wire transfers to look for countries or individuals that appear on a list compiled by Treasury’s Office of Foreign Assets Control (OFAC). Being able to successfully match your clients against the OFAC list using fuzzy matching is crucial to success.
Number Four – Revenue
Despite all of the regulations and reporting that banks must attend to, there is still obligation to stockholders to make money while providing excellent service to the customers. Revenue hinges upon a consistent, current and relevant view of clients across all of the bank’s products. Poor data management creates significant hidden cost and can hinder your ability to recognize and understand opportunity – where you can up-sell and cross-sell your customers. Data champions and data scientists must work with the marketing teams to identify and tackle the issues here. Knowing when and how to ask the customer for new business can lead to significant growth.
These are just some examples that are very common to financial services. In my experience, most financial services companies have all of these issues to some degree, but tackle them with an agile approach, taking a small portion of one of these problems and solving it little by little. Along the way, they follow the value brought and the value potential if more investment is made.
Taken from Data Governance and Data Quality Insider