While Basel III, FATCA and the Dodd-Frank Act will see renewed pressure on businesses to ensure high quality of reference data, focusing on satisfying regulator concerns as the sole reason for improving data management and data quality, would be short sighted. High quality reference data is even more important to the business itself and this alone should provide a strong case for improvement.

Reference data refers to static information such as product descriptions, calendar events, pricing etcetera. The following are some of the ways low reference data quality inhibits the smooth function of specific departments and processes in a bank:

Sales & Marketing

The absence or low quality of reference data can make it difficult to identify opportunities for cross or up selling. If sales staff do not have a comprehensive and accurate view of client behaviour and preferences, they cannot know what bank products that client would be interested in purchasing. In addition, the business cannot accurately perform analytics that would show the profitability of a particular product, client or region.

Poorly managed reference data also lengthens the time it takes from client acceptance to actual transaction execution. There is also the strong likelihood that the institution can take on a client that they should otherwise not because the client would not satisfy AML (anti money laundering) considerations. Getting rid of the client after they have already signed up can result in regulatory censure and a dent on the bank’s reputation.

Operations/Transaction Execution

Poor reference data can lead to an inaccurate data warehouse or ineffective pre-transaction risk assessment and analysis. It can delay or compromise the setup of new products resulting in unreliable pre-product analysis and pre-transaction analysis including potentially disastrous pricing errors.

In the high octane world of financial services provision where transaction volume and value can be enormous, even a seemingly miniscule error that’s fractions of a percentage off the mark can have substantial repercussions on the bank’s P&L. The surprise $2 billion trading loss by international banking giant JPMorgan Chase announced in May 2012 is probably the most classic recent example of how catastrophic a slight oversight can be.

Poor quality reference data can also inhibit straight through transaction processing (STP) of both outgoing and incoming payments. This slows down transaction execution, ties down human resources via increased manual repairs, increases the likelihood of erroneous transaction posting and thus exacerbates customer dissatisfaction.

With some estimates placing the cost of bank transaction repair at a hefty $25 to $50 each, repairs in high transaction volume institutions can be difficult to sustain.

Middle and Back Office

Poor quality reference data in a bank’s risk data warehouse means more reporting errors. Reporting errors in turn entail additional manual intervention in order to generate accurate external or internal reports. While such manual interventions are of themselves costly, they are also time consuming and make it virtually impossible for the financial institution to have increasingly crucial real time risk reports.

But they also present another problem. The risk and compliance units are not the only ones generating reports for management and regulators. The finance department of a bank often has to generate regulatory and management reports of their own – in most cases, much more reports than the risk or compliance teams.

Manual interventions increase the likelihood of inconsistency, discrepancy and a lack of reconciliation between reports generated by different departments and even those from the same unit. Wrong reports can require more capital charges than is necessary and thus placing substantial constraints on other profit making activities of the business.

Another middle and back office concern from poor product or client data is erroneous settlement instructions including delays, wrong payment, reconciliation challenges, cash flow disruptions, and reputational and financial losses.

Customer Service

For a bank, sending a customer an inaccurate statement of their account is anathema. Worse still is if such an error simultaneously affects several customers. The impact on the institution’s reputation can be devastating. As a customer, wouldn’t you be wondering whether you can really entrust your hard earned cash to an institution that cannot get something as basic as a statement right?

Yet, this is precisely one of the risks poor reference data poses to financial institutions’ brand strength. Note that the inaccuracy need not be numbers related for it to dent the firm’s reputation. It could be sending statements to the wrong client, missing the correct date for issuing dividend payments or continuing to use an old company name post-merger/acquisition.

Author's Bio: 

Graz Sweden AB provides financial services players with the most cost-effective way to access, manage, and analyze their data. Using the flexible data management platform HINC, Graz’s data warehouse infrastructure helps manage tens of thousands of investment portfolios for several institutions including 9 insurance companies, 120 banks and the largest fund manager in Scandinavia. For more information, visit www.graz.se