On Friday, May 11, a group of Semantic Technology thought leaders from around the globe met with their host and colleague, Paul Miller, for the latest installment of the Semantic Link, a monthly podcast covering the world of Semantic Technologies. This episode includes a discussion about Semantics in the Financial Services Industry, and “the Linkers” were joined by special guest, Lee Feigenbaum, VP Marketing & Technology at Cambridge Semantics. Lee shared insights gained over many years working in the semantic technology field and with numerous customers in the financial services industry.
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The financial services sector was in focus at this week’s Sentiment Analysis Symposium in New York City, which is organized and produced by Alta Plana Corp. and its founder, Seth Grimes.  Take, for example, the presentation by Rich Brown, head of Elektron Analytics at Thomson Reuters, who disclosed that the company is about to launch market response indicators in support of its Thomson Reuters News Analytics system for the financial community. That product this week also won The Technical Analyst’s 2012 award for best news analytics software.

With its software, originally discussed here, qualitative, unstructured information is turned into a quantitative data set allowing users – machines and humans – to quickly analyze thousands of news stories in less time than it takes to read a single headline, as Thomson Reuters describes it. It uses natural language processing technology to get to the end game, which is to forecast financial market response from news and social media sentiment. Some 82 fields of metadata come into play for automating the analysis of news content. That encompasses sentiment down through to the degree of positive, negative or neutral expressions and how individual companies mentioned in a piece fare in those respects – rather than just the tone of the piece at large. “The computational linguistics system measures the author’s tone as positive or negative on any given entity, which is important and the harder part of it,” Brown said. Other fields include, for example, relevance, genre, intensity of news flow, and more.

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These days, it’s not just the traditional publishing community that has reason for leveraging the content syndication model. As more and more companies across vertical sectors themselves become content providers, syndication makes sense for them, too.

NewsCred has a new – and semantic – take on content syndication, with content partners ranging from Reuters to The Guardian to The Economist. Recently-added customers that leverage the service’s fully licensed text, image and video content include traditional publishers such as the New York Daily News (and NewsCred is in talks with it about becoming a content provider, too). But other recent customers point to the importance of quality content to the consumer and corporate brand market:  For example, insurance provider Zurich recently signed on. NewsCred also just closed a deal with Johnson & Johnson to be a subscriber of its syndication services for content related to the health care products and pharmaceuticals space.

Brands, says NewsCred CEO Shafqat Islam, are responding to consumers getting smarter and more demanding. “They have so much access to information that brands are starting to realize they can’t just sell products or services anymore,” he says. “They need more authentic, engaging conversations with their customers and the best way to build these authentic relationships is with highly-engaging, trusted, high-quality content.”

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Bryan Bell of Cogito reports that financial companies are wise to turn to semantic metadata for better Big Data analytics. Bell writes, “Financial institutions are looking to linguistics and semantics as the best option for managing and taking advantage of their unstructured data, using it to better understand customers and competitors, to identify impactful market trends or simply to automate the process of answering common customer questions. As one Chief Data Officer put it, ‘We are the stewards of one of our firms’ most important assets, data, and we have been charged with bringing meaning to the data. I believe semantics offers a consistent, long-term capability and change in how data will be managed’.” Read more

Back in March, The Semantic Web Blog wrote an article about FIBO, the Financial Industry Business Ontology that’s on its way to being an Object Management Group series of standards. There, we explored its value as an open semantic standard that can be used by financial institutions and industry regulators, both to support conformance to federal regulatory reporting requirements and for internal business processes and risk analysis.

To continue the discussion about the operational value of FIBO, we recently spoke with key participants developing the standard: David Newman, Strategic Planning Manager, Vice President, Enterprise Architecture, Wells Fargo Bank, who is lead of the industry team collaborating on semantics OTC (over-the-counter) derivatives proof-of-concept, and Mike Atkin, managing director at the Enterprise Data Management (EDM) Council, where FIBO was born and is included as content of EDM’s Semantics Repository.

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Thinking about this past week’s Demystifying Financial Services Semantics conference in NYC, hosted by OMG and The EDM Council, and messages that kept coming across: Think about the business issues first, and be realistic in the way your particular organization can accommodate them – semantic technology may be a large, limited or perhaps even non-existent factor, depending on the challenge.

Some commentary on this point from the panelists:

• Citi chief data officer Eric Chacon discussed the challenges of not having a single source of master data that exists for businesses that grow  through acquisitions, as well as any organization highly decentralized in nature. Read more

Bryan Yurcan interviewed David Saul of State Street regarding how semantic technologies can be used to tame big data. He writes, “Financial institutions are accumulating data at a rapid pace. Between massive amounts of internal information and an ever-growing pool of unstructured data to deal with, banks’ data management and storage capabilities are being stretched thin. But relief may come in the form of semantic databases, which could be the next evolution in how banks manage big data, says David Saul, Chief Scientist for Boston-based State Street Corp. The semantic data model associates a meaning to each piece of data to allow for better evaluation and analysis, Saul notes, adding that given their ability to analyze relationships, semantic databases are particularly well-suited for the financial services industry.” Read more

 

Credit default swaps. Collateralized debt obligations. Moral hazards. The average person might find the financial services sector and its language as mystifying as some of those involved in the industry might find semantic technology. An event hosted by OMG and the EDM Council in New York City yesterday was aimed at demystifying the latter for Wall Street. But putting the technology to work there might help clarify the discourse around financial instruments for a wider audience, including the regulators who want to deal with concentration of risk issues that played a big role in the Wall Street meltdown.

One part of the picture is FIBO, the Financial Industry Business Ontology, which was the subject of two sessions at the event. An advance discussion of the topic with Thematix principals Elisa Kendall and Jim Rhyne, who was a panelist at the event, set the stage for us here at The Semantic Web Blog. “The primary practical use for an ontology like FIBO that is descriptive of various kinds of financial instruments, including so-called exotics, is that regulators and financial market participants get a common language to talk about things,” Rhyne explains. This is important, given that financial regulators try hard to be collaborative with the industry, pointing out the need, he says, for careful management of financial instruments, including recommendations about capital buffers to deal with downside risk and asking for timely reports of information that would allow them to assess the possibility that a systemic problem could occur rather than directly intervening by stopping trades.

Especially in the derivatives marketplace, there is a lot of “funky terminology,” he says, and not all of it is as well-understood as it should be. Different parties and different parts of the marketplace may call the same instrument by different terms, and one of FIBO’s aims is to provide a common vocabulary.

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What does the compliance lifecycle look like at your company? In globally-operating industries such as finance, there’s likely a herd of people charged with monitoring rules and regulations across countries, drafting policies and procedures for individual geographies or business units, and working to ensure controls are in place to prevent and detect violations. And that herd of individuals in some respects may be trying to herd cats, given how often aspects of compliance regulations change.

The situation presents the ideal use case for semantic technology, says Cambridge Semantics’ co-founder and VP of Technology and Client Services Lee Feigenbaum: There’s data to consider from a wealth of sources, from internal documents and control databases describing what is necessary to enforce policy at different areas and levels of the business and what reports are needed to ascertain compliance, to regulatory information published on governing bodies’ web sites or RSS feeds; people are working cross-organizationally within the company and in conjunction with the regulatory organizations; and the rules regularly change. At yesterday’s Demystifying Financial Services Semantics conference in New York City, it demonstrated its just-released Compliance Information Management Solution Accelerator, based on its Anzo semantic technology, to deliver information integration across multiple data sources, as well as an editor workplace where compliance officers or others managing these tasks can contribute and track content changes and workflow, and then seamlessly bring together the compliance content applicable to particular business units or geographies.

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Sherman So recently interviewed Feng Yue, founder of Stock Radar, a new tool that uses semantic analysis to help investors choose good stocks. Feng told So, “We gather all the information on the Web regarding a particular stock, such as news, analysts’ comments, people’s personal blogs, etc., and give the user an indicator about how the stock will do in the future: will its price rise, fall or just stay the same.” Read more