Drew Warren, President and CEO of Recognos Financial explains the development of Artificial Intelligence technology in our days. In today’s world, the integration of large quantities of disparate data, the ability to structure and interpret unstructured data, data storage, and analytics are the principal changes in financial industries. (more…)
Archive for the ‘Announcements’
Artificial Intelligence technology in our days
Recognos Announces New Release of Mutual Funds Reference Data Center (RDC) Platform
Enhancements include alerts to SEC filing changes; new data comparison tools.
NEW YORK, April 5, 2016 — Leading semantic data analysis provider Recognos Financial today announced a (more…)
Machine Learning Nears Inflection Point
“Say that you have an auto insurance policy and a stone cracks your windshield[…]You could look at your policy and ask it if you were covered for the damage. The document would look for the answer in the body of your policy, highlight it, and then present the answer to you.” (more…)
Artificial Intelligence Can Nab Money Launderers
George Roth, in the following articles talks about AI, the combination technologies, and one of the approaches: ontology-based.
Clients must provide supporting documents that substantiates the information disclosed on the form. (more…)
Recognos ETI Creates Smarter Data
New Platform Extracts, Transforms and Integrates Data to Improve Business Intelligence
New York, NY — January 27th, 2016 — Recognos Financial, provider of AI based data management solutions for the financial services industry, today announced the launch of Recognos ETI, a new data extraction platform designed to unlock greater context and insight from otherwise untapped business data. Utilizing the latest advancements in artificial intelligence (AI), natural language processing (NLP), semantic (more…)
7 Ways Semantic Technologies Make Data Make Sense
In today’s world, when paper is replaced by electronic documents and information must support fast business decisions, turning printed material into structured information is a major challenge.
This article about semantic technologies that make sense in today’s word, including an interview with Recognos Financial CEO Drew Warren, offers insights and analysis from key players in the industry:
Top 5 sectors using artificial intelligence
Some 80 per cent of the underlying data being processed in the financial services sector remains either semi-structured or not structured and has to be processed manually, says George Roth in a must read article, for every business leader. This article is part of a 16 page report on ‘Artificial Intelligence for Business’, published on December 15, 2015.
The financial services industry was one of the first commercial sectors to deploy AI in mainstream business decision-making. Citibank, for example, was working on first generation expert systems as far back as the 1980s. Such interest is not surprising given the sector’s reliance on massive amounts of data. On top of structured data about millions of transactions held by every financial services business, Reuters publishes 9,000 pages of financial news every day and Wall Street analysts produce five research documents every minute.
George Roth, chief executive of Recognos Financial, says some 80 per cent of the underlying data being processed in the financial services sector remains either semi-structured or not structured and has to be processed manually. With AI, firms can analyse and contextualise such data almost instantly. AI technologies being applied in financial services include natural language processing, data mining and text analytics, semantic technologies, and machine-learning.
IBM has identified the sector as a customer for its Watson AI system, which uses natural language processing and machine-learning to glean insights from large amounts of unstructured data.
IBM says the “ultimate financial services assistant” is capable of performing deep-content analysis and evidence-based reasoning to accelerate and improve decisions. For example, a bank could use the system to make better recommendations of financial products based on comprehensive analysis of market conditions, the client’s past decisions, recent life events and available offerings.
Another application is in compliance, fraud detection and security. Integrating structured and unstructured data ensures compliance rules are being applied and can help to detect offences, such as money laundering and insider trading. Natural language processing systems can uncover subtle cues in transactions that might indicate behaviour that does not show up in the numbers.
So-called “know your customer” systems are another widespread use of AI to manage unstructured and constantly changing data in order to assess risk.
Top 5 sectors using artificial intelligence
Published in The Times
December 15, 2015
By Michael Cross
Full article available here: http://raconteur.net/technology/top-5-sectors-using-artificial-intelligence
Brain over brawn: Semantic technology and machine learning take new role in managing finance data
Pairing semantic technology with machine-learning-with-human-intervention enables the company to improve accuracy of data with minimal human impact. The result is 99.9 percent accuracy rate, said Drew Warren, Recognos Financial CEO in an interview, published on Finance Technology Weekly.
The finance industry has more than its share of data-intensive processes. But at least one technology firm is making the case that the answer to these challenges is not the traditional approach of ramping up data processing power – it’s smarter data processing.
With a semantic technology-based platform, Recognos Financial is taking a different approach to data processing and applying it to financial processes as wide-ranging as securities master management, onboarding and KYC compliance.
In mutual funds, for example, the company has assembled a securities master file for the mutual fund industry using a variety of tools from semantic technology to machine learning. As an Edgar distributor, Recognos has all mutual fund prospectuses and amendments, and it uses a combination of technologies to extract data from those unstructured data sources.
“Our platform uses about six different methods of data extraction. At the top of the list and the most complex is semantics, but we also use NLP, regular expression, machine learning and machine learning with human intervention,” said Drew Warren, Recognos CEO.
Warren estimates there are approximately 250 data points the company pulls from prospectuses and amendments to create its mutual fund securities master files. Semantic data aids the process because of three main benefits: its capabilities to structure unstructured data, integrate data very quickly, and deeply mine data for relationships. That last aspect is aided by the way semantic technology stores data – in triples, meaning data is stored in context, making it easier to align with other data points of similar contexts.
Pairing semantic technology with machine-learning-with-human-intervention enables the company to improve accuracy of data with minimal human impact. If a piece of data were incorrect, a user to the system could drag-and-drop the correct data in the appropriate field instead. (Data is only lassoed, or dragged-and-dropped, rather than rekeyed, as Recognos maintains that typing data introduces potential errors.)
Furthermore, the process of correcting a field by dragging and dropping a new value into it triggers a new script, ensuring that the new type of data will always be collected for the corrected field moving forward. The result is 99.9 percent accuracy of its mutual fund securities master data file, Warren said, with significantly less manpower.
“We have 12 data analyst that work on this, and that covers all the mutual funds issues in the U.S.,” Warren said. By contrast, a competitor has approximately 300 people working on mutual fund securities master, with lower accuracy results.
In know-your-customer (KYC) compliance, information that customers supply as part of the compliance process is verified against unstructured data pulled from supporting documents.
“Our technology is being used to process those supporting documents to verify information about an entity,” Warren said, adding that a wide range of documents are used to support this process, from articles of incorporation to tax documents, contracts and more.
With onboarding, a key challenge, particularly for a firm that is onboarding a group of clients on behalf of another provider, is that the old provider may have used different data formats than the new one.
“A lot of the data in onboarding is already structured, but it’s going to be coming off the system of one company, and going to the system of another,” Warren said. “You have to normalize the data from the output of one to the input of another.”
The ability of semantic data to store in context and mine more deeply can be used beyond the onboarding process, through the lifecycle of the client as well, Warren said.
“Using this technology, you can read all of the notes and documents for a given client across the entire platform, and learn where problems lie, what kinds of problems clients are having and delve much deeper into satisfaction levels to determine where you need to focus your efforts over time,” he said.
Brain over brawn: Semantic technology and machine learning take new role in managing finance data
Published in Finance Technology Weekly
December 3, 2015
By Renee Caruthers
Trends in AI technology
The financial services industry has begun to undergo a significant transformation following the introduction and application of artificial intelligence (AI) over the last several years, says George Roth, CEO, Recognos.
While the industry remains heavily data-dependent, about 80% of the underlying data being processed remains either semi-structured or not structured, and is being processed using costly, inefficient and risk intensive manual processes. Artificial intelligence applications are changing these practices, making it simpler and more profitable for firms to analyze and contextualize their data.
The most common AI categories being applied in financial services include natural language processing (NLP), data mining and text analytics, semantic technologies (ontologies, linked data, sentiment analysis), and machine learning.
In order to be able to access enterprise data as a ‘whole’, natural language-processing techniques are used to prepare the data for downstream AI-related analysis processes. It will become a mandatory ‘infrastructure component’, which when combined with semantic technology will make the difference between ‘structured’ and ‘unstructured’ disappear in data processing. In future, the following are the areas where we see financial institutions applying AI related technologies:
Generalization of the semantic search: Search will be transformed into a performant tool to access information in a holistic way. Search will evolve from the ‘keyword search’ approach to a natural language-based search, where the user can address a question using the natural language. The trend can already be seen in the evolution of Google and the migration towards a question / answer solution. Answers to search questions will be provided from completely integrated databases that contain structured and semantically tagged data. In order to achieve these, unstructured documents will be semantically tagged using NLP, ontologies will be built to support the search and the linked data will be used for enhancing the search results with information available inside and outside the enterprise.
Lead generation and lead management: Using AI technologies, financial services companies will be able to find leads from different public sources, qualify leads and optimize the sales process by targeting prospects with very efficient and less intrusive campaigns. The key for this is gathering information from multiple sources, integrating them and enhancing the information by using linked data. For example, an investment company specializing in high-net-worth individuals will be able to detect large purchases in certain areas of interest and determine the approximate net worth of the buyers. They can then cross-check to determine if these individuals are in their lead or customer databases (this is not a trivial problem, and also can be simplified by leveraging semantic deduplication techniques) and generate a new lead. In this way, internal ‘lead factories’ can be generated.
Compliance, fraud detection, security: By integrating structured and unstructured data, AI techniques will help financial services companies increase the efficiency of enforcing compliance rules (e.g. in customer communication), prevent fraud (insider trading events), detect money laundering schemes and detect and prevent intruders from access into secure networks. A fundamental property of these solutions is that all these systems will auto-develop in time based on the detected incidents.
Currently, business reporting is based on analyzing current data that resides in data warehouses and traditional relational databases. These reports can only solve problems that ‘we know that we don’t know’ – for example finding in time how many clients had a set of pre-defined different properties. The big leap using AI in this area is to be able to find these things, i.e. discovering new facts and building predictive models using the technology bundle known under the name of “data analytics”. This new reporting paradigm will allow financial institutions to create reports on the fly and to integrate data from multiple sources, creating a more clear and accurate picture to improve business performance.
Customer relationship optimization: By understanding enterprise data as a whole and merging information contained in structured and unstructured data silos, customers will become better known. This will allow companies to implement predictive customer relationship actions, minimize customer complaints or to quickly fix issues that can cause a high percentage of churn in the enterprise.
There are many other areas in financial services enterprises that will benefit from the AI technologies and ultimately will allow for continual business improvements, higher qualities of service and increased customer satisfaction. These AI technologies, which were often thought of as “rocket science” in the past, will become a must for both buy and sell side financial firms in order to create and maintain a competitive advantage in the market.
George Roth is the President and CEO of Recognos Inc and also functions as the chief architect for Recognos Financial. In 1999, Roth and his par tner Ken Rogers founded Recognos Inc, a Silicon Valley-based firm specializing in systems development. Roth has extensive experience in the financial ser vices industry and in the semantic technology. He has previously worked for Citibank, State Street, The Northern Trust Company, Merrill Lynch, BGI, Charles Schwab and Fisher Investments.
Trends in AI technology – explained by George Roth, CEO, Recognos
Published in Wall Street Letter
Recognos Reference Data Center to Transform Critical Mutual Fund Data into Actionable Insight
By Joanna Wright
Inside Reference Data
September 15 2015
Service provides utility and analytics on unstructured data
Semantic data analytics company Recognos Financial has launched an industry utility for mutual funds.
The Recognos Data Center (RDC) is now live for mutual fund and broker-dealer clients.
Recognos says the RDC is not only a central repository for documents relating to all US-based open-ended mutual funds, but provides analytics on the unstructured data in these documents using Recognos Financial’s proprietary semantic technology.
“The RDC builds upon Recognos’s behind-the-scenes involvement in the mutual fund profile service of the Depository Trust and Clearing Commission (DTCC)”, says Ira Cohen, executive vice president of asset management at Recognos Financial.
“About 10 years ago, there was a scandal in the mutual funds arena,” says Cohen. “That scandal involved brokers or registered investment advisors selling mutual funds to clients and charging clients more commission than they should have been. This resulted in fines for the broker-dealer community.
Their defense was that all of these funds have different rules, different pricing structures, different processing capabilities – how was it possible to keep track? So the solution was to build this black box, the DTCC’s mutual fund profile.”
“The RDC has improved on this registry”, says Cohen, “by capturing data on the entire universe of US-based open-ended mutual funds: 600 mutual fund families—some big, some small—and about 35,000 individual funds.
Secondly, the RDC allows funds to view each other’s fund profiles in order to do competitive analysis (the data is all public).”
Cohen explains: “Recognos has built a facility where funds can not only view other funds’ data, they can go in and do analysis on any of the 200 data points that we are capturing. So if a fund wants to start a new fund, a class A European growth fund, for example, before they do so, they can go out and do an inquiry and say, ‘Let me look at all the European growth funds out there, let me see what their expenses are, what privileges they offer the shareholder’—all public information.
But we bring it all together in a way where you can get a report, look at a graph, it gives you very quick analysis. It takes all this non-structured data and structures it to be much more usable for the fund companies themselves.
And to the consumer—the broker-dealer—the advantage we believe we have is this entire universe of funds.
Two of the major benefits of the RDC are that it eliminates manual data entry and processing, and the source data comes directly from the SEC.”