Image Courtesy: Flickr/Patrick Hoesly

The financial services industry is taking to semantic tech in an important way, and that’s in the form of the Financial Industry Business Ontology (FIBO), which aims to standardize the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions. Attendees at SemTech Biz in San Francisco will get a deep dive on the how’s and why’s, at this session, while the FIBO Technology Summit invitation event will present an opportunity for working collaboratively to continue advancing the effort that has its roots in The Enterprise Data Management Council and communities of interests.

Leading that event will be Dennis E. Wisnosky, founder of Wizdom Systems, Inc. and former CTO and Chief Architect of the DoD Business Mission Area, who was recently named to provide technical strategy and operational guidance to help the Council finalize and implement FIBO standards, and David S. Newman, SVP & Strategic Planning Manager Enterprise Architecture at Wells Fargo, and Chair of the EDM Council’s Semantics Program. (Newman, with Enterprise Data Management Council Head of Semantics and Standards Mike Bennett, will also host the SemTech FIBO session.)  Speaking of the upcoming event, Wisnosky explains that a goal is to cast a wide net to find the new tech ideas and developments that both can bring benefits to FIBO in the short term and influence the longer-term research agenda to help the financial industry.

As FIBO stands now, in June the second draft of the FIBO Foundations ontology and the conceptual FIBO Business Entities ontology will be presented at a meeting of the Object Management Group in Berlin. By year’s end it is expected that the OMG will have ratified these as formal standards. “We are on the path to turn the corner from thinking of what FIBO will be to delivering it,” says Wisnosky. Read more

Earlier this month Gartner named semantic technologies to its top ten trends list (see our story here). Recently, we caught up with Gartner vp and distinguished analyst Debra Logan, the lead author on the semantic technologies section of the Top 10 Technology Trends Impacting Information Infrastructure, 2013, to learn more about sem tech’s earning a place on the list.

One interesting point Logan made is that the top ten trends list actually is a reflection of inquiries Gartner sees from its end-user clients. So, semantic technologies’ spot on the list would seem to indicate a bubbling-up of real-world, enterprise interest. As Logan sees it, it’s very much about information overload, about minimizing the risk and maximizing the value of the data on their hands, and about the availability now from providers like Amazon and Google of infrastructures for analyzing Big Data sets.

“If we could get the same meaning from data, we might actually know what is going on, because we sure don’t now,” says Logan, of the quandary facing enterprise IT leaders. “They are struggling with definition issues and reconciliation because of the proliferation of different IT systems.”

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The Semantic Web Blog earlier this month covered the news that Ontology Systems is updating its Ontology semantic search platform to Version 4.0, which was previewed last week at Mobile World Congress in Barcelona. Following that, we had an opportunity to catch up with CTO and co-founder Leo Zancani to learn more about the upcoming version of the platform that features new search capabilities under the Project Rothko code name banner, as well as the just-released Version 3.7, and how the telco and financial sectors its technology is focused on can leverage it.

The company’s Ontology platform connects its Ontology Intelligence 360 and Ontology Integrity Manager solutions. The former builds dependency models of business entities by looking at data in existing enterprise systems, and the latter is a data integrity solution that measures and monitors in an ongoing way the data alignment among various different systems that talk about the same thing. Those two products, Zancani explains, “depend on quite finely modeled data, so there are quite strict semantic models inside them. Data that is taken from existing systems populates those strict models, and customers are interested in then using the data in those models to drive business processes,” he says. Due in Version 4.0 that should debut in April, “Rothko adds the capability on the side that says, that’s great, but there also is value in the data you don’t want to or need to or can’t afford to model right now, and you can access that with a much more direct search capability.”

What’s the call for this two-tiered search approach in the verticals Ontology Systems is focused on? Take the telco sector, where the company founders have a long history. The industry, says Zancani, is in crisis now, as vendors like Apple and Google eat its lunch, and as the fallout from major consolidation among telco players makes traditional data integration economically untenable.

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This week a new Big Data startup company launched, Ayasdi, co-founded by Stanford mathematics professor Gunner Carlsson and based on his DARPA-funded research in the area of applied topology, with $10+million in Series A funding led by Khosla Ventures and Floodgate.
The technology, dubbed the Insight Discovery platform, is explained to be the “first machine learning platform that combines computer science and a branch of mathematics known as Topological Data Analysis (TDA) that visualizes the entire dataset.” Hundreds of machine learning algorithms, it says, go to work exploring datasets to in minutes automatically discover insights that can’t be determined through query-based or ad hoc approaches.

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The talk of falling off the fiscal cliff that’s drowning out the holiday music could take its toll on what historically is a strong month for the stock market, according to Reuters, How that scenario will play out, not to mention a ton of other factors, is just the kind of thing to keep hedge fund managers, wealth advisors and advanced individual investors on their toes as they calculate investment strategies.

A new cloud-based artificial intelligence solution from Lucena, the first features of which are going live today, focuses on helping these users scientifically validate their investment plans, the idea being to find new market opportunities and reduce risk. The early stage company is headed up by serial entrepreneur and CEO Erez Katz, whose partner in the venture is CTO Tucker Balch, a professor of Interactive Computing at the Georgia Institute of Technology whose work focuses on machine learning and robotics.

QuantDesk is the result of five years of research Balch has done at the institution. It is, as Katz describes it, a “quant in a box” that can give sophisticated investment professionals in small or mid-size firms, who lack the resources of the large investment houses to hire quantitative analysts to derive complex and sophisticated trading algorithms, access to a scientific approach to “validate or pivot the decision process,”

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Real-time macroeconomic and geopolitical events and sentiment about them now figure into RavenPack News Analytics 3.0, to help financial firms react more quickly to what’s happening in the world.

The solution is aimed at quant finance shops, such as hedge funds, banks, and some financial research houses, where the machines are doing the trading. It’s a part of the market that’s had some rough times of late because of some trading errors, but in general quant firms represent a growing piece of the financial space, says RavenPack Head of Sales and Business Development Hugh Taggart.

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Abhishek Bhattacharya and Pravin Lal have written an interesting article for Information Week India regarding the influence of developing technologies on capital markets. They write, “Technology plays a significant role in the capital and commodity market, in fact trading firms are the first ones to leverage any cutting-edge technology that can help to get the pulse of the markets faster, help them execute trades faster, or measure their risks faster. In the last two decades we have seen several innovations in the industry in terms of new financial products and new ways of doing business, all of which require substantial application of very advanced technologies.” Read more

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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Photo Courtesy: Flickr, epicharmus

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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