Top Four Reasons Why Financial Services Companies Need Solid Data Governance

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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.

Using Data Quality Tools to Look for Bad Guys

Most companies do not want to do business with bad guys - those on the FBI most wanted or international terrorists. Here in Boston, we’re always on the lookout for James “Whitey” Bulger, a notorious mobster who has been on the FBI most wanted list for years. But how do you really know of you’re doing business with bad guys if you don’t pay attention to data quality?
If you work for a financial organization, you may be mandated by your country's government to avoid doing business with the bad guys. The mandates have to do with the lists of terrorists offered by the European Union, Australia, Canada and the United States. For example, in the U.S., the US Treasury Department publishes a list of terrorists and narcotics traffickers. These individuals and companies are called "Specially Designated Nationals" or "SDNs." Their assets are blocked and companies in the U.S. are discouraged from dealing with them by the Office of Foreign Asset Control (OFAC). In the U.K., the Bank of England maintains a separate list but with similar restrictions.
If your company fails to identify and block a bad guy (like Whitey here), there could be real world consequences such as an enforcement action against your bank or company, and negative publicity. On the other hand, many cases may be a "false positive," where the name is similar to a bad guy's name, but the rest of the information provided by the applicant does not match the SDN list. The false positives can make for poor customer relationships.
If you have to chase bad guys in your data, you need to make data quality a prerequisite. Data quality tools can help you both correctly identify foreign nationals on the SDN list and lower the number of false positives. If the data coming into your system is standardized and has all of the required information as mandated by your governance program, matching technologies and more easily and more automatically identify SDNs, and avoid those false positives.