Organizations in the financial services sector face a unique set of challenges as they consider how to wrangle and process the vast amount of data they collect. During our Financial Services Summit, I was lucky enough to speak to Brian Anthony, chief data officer for the Municipal Securities Rulemaking Board (MSRB), to learn how the MSRB is integrating technologies such as artificial intelligence (AI) and machine learning to modernize its data. He revealed intriguing developments at the MSRB and steps the organization is taking to make data as actionable as possible.
As the principal regulator of the $4 trillion municipal securities market, the MSRB has a vision for the regulation of this market, and modernizing data is at the heart of this vison. The MSRB’s four strategic goals are:
- Modernize market regulation by modernizing the rule book to provide a fair and efficient market that facilitates capital formation.
- Enable market transparency by leveraging the MSRB’s investment in the cloud, specifically around the MSRB’s market transparency platform, the Electronic Municipal Market Access system (EMMA).
- Provide high-quality market data that enables comprehensive analysis and insight into the municipal securities market.
- Uphold public trust and the public interest and integrity of the market through a commitment to diversity, equity, and inclusion.
“I believe that access to high-quality data is essential for all four of these components,” says Brian.
Tackling Structured and Unstructured Data
During the session, Brian explains that as the central, sole repository for data in the municipal securities market, the MSRB takes in both structured and unstructured data. Structured data includes trades and underwriting transactions, while unstructured data covers a range of items such as new bond offerings coming into the market, issuer annual financials, material event notices, and more. The unstructured data is where the real challenge lies, and the MSRB is deploying technologies like machine learning and natural language processing to derive structure from it.
Making sure the unstructured data is searchable is one way the MSRB aims to provide data that is as actionable as possible. Using natural language processing to identify and contextualize topics or themes such as climate risk or cyber security in that data helps to provide value. Another method is to take specific attributes of the data, extract them, structure them and present them back to EMMA for dissemination throughout the industry.
As the MSRB’s flagship transparency system, EMMA houses all the data the organization collects and makes it available for free to the public. The MSRB collects data from different market participants, and it is modernizing the platform to make it more responsive and present data in a more consumable way.
To enhance how that information is presented and leveraged, the organization has created a regtech sandbox called EMMA Labs. Scheduled for release later this year, EMMA Labs uses natural language processing in a search function to unlock insights into the 100,000+ disclosures that the MSRB receives every year. The next step is to apply sentiment analysis to move beyond just identifying those disclosures and identify where certain sentiments are expressed within the information.
Leveraging Sentiment Analysis for Impressive Insight
One of the most riveting parts of my discussion with Brian was when he revealed how employing advanced algorithms is leading the MSRB to understand how certain subjects are referenced within disclosures. For example, the MSRB was able to generate a report about specific disclosures, and it plans to use sentiment analysis to understand the positive or negative sentiments associated with those disclosures.
This is an example of the huge opportunity to contextualize data from disclosures to understand the narrative expressed throughout the data, which would ultimately give investors and the public unprecedented market insight.
Following Emerging Trends
In its effort to advance market transparency by improving the quality of data made available to investors and others, the MSRB is following a few emerging trends. The most prominent one is the focus on environmental, social, and corporate governance (ESG) considerations. Investors are demanding greater disclosures around climate risk and other ESG factors, but there aren’t any universally accepted data standards and definitions of ESG. This presents a classic data challenge to the MSRB, but Brian noted that the organization has the right plan in place to move the needle forward. Hear more from our conversation and learn how the financial sector is exploring advanced analytics and AI techniques in the on-demand version of our session here.
Thomas De Souza is CTO of Financial Services at Hitachi Vantara.