President Obama signed the Dodd-Frank Wall Street Reform and Consumer Protection Act into law on July 21, 2010. Representing the biggest change to U.S. financial regulation since the Great Depression, Dodd-Frank is designed to help promote greater financial stability and consumer protection.
Given the breadth and depth of the changes to existing regulations, banks are likely to be challenged by the new requirements. Analytics can help banks comply in two distinct ways. First, by defining a broad analytics framework, banks can incorporate advanced analytics to drive compliance. Advanced analytics encompasses a wide range of data analytics disciplines that can help uncover patterns and predict trends. These include data modeling, data mining, statistical analysis, text mining and visualization. Second, as different provisions of the Dodd-Frank Act are implemented, banks will need to perform searches to fulfill requests for information from regulators. Analytics can help them much more efficiently deliver the data requested.
These solutions could be further enhanced if banks forge an enterprise-wide analytics strategy that is implemented by a cross-functional team of legal, risk and data analytics professionals. Such a strategy could help enhance compliance across the organization.
Following a Defined Framework for Applying Analytics for Compliance
In preparation for Dodd-Frank, banks may start by outlining an analytics framework that helps them first identify the challenges they face (see infographic below). They can then determine the data needed, enhance existing processes and finally incorporate advanced analytics to gain insights, make predictions and drive compliance.
In addition, merging data from multiple departments is helpful for building a 360-degree view of bank's investments and customer relationships. Banks can then use advanced analytics tools on merged datasets to help gain the deeper insight they require for compliance.
For example, to protect banking customers, the Volcker Rule of the Dodd-Frank Act imposes certain restrictions on proprietary trading and investing in hedge funds and private equity funds. One restriction mandates a 3-percent limit on the total ownership interest of private funds across the enterprise. Banks may consider leveraging advanced analytics to help identify, quantify, and classify ownership interest in such funds. For instance, they can apply cluster analysis to their holdings across the enterprise to measure their exposure to private equity funds. They can then use regression modeling to predict future exposure to those funds and thereby help determine if current trading activity across the bank could lead to a violation of the rule.
As another example, the Credit Risk Retention Rule of the Dodd-Frank Act requires banks that sell asset-backed securities to retain at least 5 percent of the credit risk, unless the underlying loans meet standards that reduce riskiness. To comply with this rule, banks can use visualization tools to convert data about underlying assets, related cash flows, and probabilities of default into visual representations that better demonstrate risk. They can also use classification algorithms to help predict whether this rule could be violated based on current trading activity across the bank. Such techniques can help banks determine the level of credit risk they target while taking into consideration both their risk appetites and the 5-percent rule.
Although many banks may already be using these techniques at the departmental level, they might consider deploying them higher in the organization to better assess risk exposure across the enterprise.
Enhancing Searches to Efficiently Respond to Regulators
Under Dodd-Frank, banks will likely need to quickly respond to more -- and more complex -- requests for information. Analytics can help them do this efficiently.