Patent Application
20110131076
This invention generally relates to the area of risk management. More specifically, this invention relates to automating risk identification using information mined from information sources.
Organizations operate in risky environments. Competitors may threaten their markets; regulations may threaten margins and business models; customer sentiment may shift and threaten demand; and suppliers may go out of business and threaten supply, etc. Risk management is thus a central part of operations and strategy for any prudent organization.
Currently, various risk alerts with respect to entities and activities are common. However, such risk alerts occur after the fact. While alerts as to the actual occurrence of an event which puts an entity or topic/concern at risk is important, the mining of potential risks is believed to be very useful in decision making with respect to such an entity or issue. In order to perform a meaningful risk assessment, it is often necessary to compile not only sufficient information, but information of the proper type in order to formulate a judgment as to whether the information constitutes a risk. Without the ability to access and assimilate a variety of different information sources, and particularly from a sufficient number and type of information sources, the identification, assessment and communication of potential risks is significantly hampered. Currently, gathering of risk-related information is manually performed and lacks defined criteria and processes for mining meaningful risks to provide a clear picture of the risk landscape.
One possibly related area is research on correlations between stock prices or stock price volatility (a proxy for risk) and published documents. The first step in the risk management cycle, i.e. risk identification, however, has received little or no attention. In other words, methods of the prior techniques are cumbersome, inefficient for identifying risk and lack accuracy. In particular, prior techniques require manual or operator intervention and analysis to access documents that may impact risk before alerting an analyst. Thus, the state of the art is incapable of dealing with risk unanticipated by a risk analyst.
The present invention recognizes the difficulties analysts currently have in anticipating risks and seeks to overcome these difficulties. The present invention provides a method to accurately and efficiently identify potential risks associated with various entities and activities, and includes various advantages and benefits as described further herein.
The present invention avoids the problems of the prior art by mining risk-indicating patterns from textual databases that can then be used to activate alerts, thus informing users, such as analysts, that a risk may or is about to materialize. In particular, the present invention is directed towards automatically mining risk from different sources, thereby allowing an analyst to review many more information sources than possible with techniques of the prior art.
In one aspect of the present invention a computer implemented method for mining risks is provided. The method includes providing a set of risk-indicating patterns on a computing device; querying a corpus using the computing device to identify a set of potential risks by using a risk-identification-algorithm based, at least in part, on the set of risk-indicating patterns associated with the corpus; comparing the set of potential risks with the risk-indicating patterns to obtain a set of prerequisite risks; generating a signal representative of the set of prerequisite risks; and storing the signal representative of the set of prerequisite risks in an electronic memory. Prior to this mining, a corpus of textual data is first searched with the computing device containing the risk-identification-algorithm for instances of a set of risk-indicative seed patterns provided to create a risk database, which is done by a risk miner function. The corpus may include any searchable source of information. Generally such sources are digital and accessible through computerized searching. For example, the corpus may include, but is not limited to, news, financial information, blogs, web pages, event streams, protocol files, status updates on social network services, emails, short message services, instant chat messages, Twitter tweets, and/or combinations thereof. Rather than alert a user after a risk factor has in fact occurred, a risk alerter function may pass warning notifications to a user directly, thereby avoiding the shortcomings of the prior art.
In another aspect of the present invention a computing device or system may include an electronic memory; and a risk-identification-algorithm based, at least in part, on the set of risk-indicating patterns associated with a corpus stored in the electronic memory.
These and other features of the invention will be more fully understood from the following description of specific embodiments of the invention taken together with the accompanying drawings. Like reference symbols in the various drawings indicate like elements.
In the drawings:
The computing device 120 may also be used to alert users 130 through a computer interface (not shown) of risks, including but not limited to imminent risks, i.e., risks that are likely to occur including, but not limited to, likely to occur in the near future or a defined time period. Typically, the users 130 are alerted via a computing device (not shown). The present invention, however, is not so limited, and any device having a visual display or even a voice communication may suitably be used. As used herein, the term “computing device” refers to a device that computes, especially a programmable electronic machine that performs high-speed mathematical or logical operations or that assembles, stores, correlates, or otherwise processes information. Examples include, without limitation, mainframe computers, personal computers and handheld devices. Before mining the corpus 110 for risk, the present invention utilizes the computing device 100 to extract risk-indicating patterns from corpus or corpora of textual data. As used herein, risk-indicating patterns are patterns developed through the techniques of the present invention which relate possible prerequisites to possible events.
As depicted in
In one embodiment of the invention, trigger keywords are used (e.g. “risk”, “threat”) to generate the risk database. In another embodiment, regular expressions are used (e.g. “(“may”)? pose(s)? (a)? threat(s)? to”) to generate the risk database. Candidate risk sentences or sentence sequences are created, and new patterns are generalized by running a named entity tagger or Part of Speech (POS) tagger, and chunker (entities can be described by proper nouns or NPs, and not just given by named entities) over it, and by substituting entities by per-class placeholder (e.g. “J.P. Morgan”=>“<COMPANY>”). These generated patterns can be used for re-processing the corpus, in one embodiment of the present invention after some human review, or automatically in another embodiment. The extracted sentences or sentence sequences are then both validated (whether or not they are really risk-indicating sentences) and parsed into risks of the form P=>Q (i.e. finding out which text spans correspond to the precondition “P”, which parts express the implication “=>”, and which parts express the high-impact event “Q”), using, but not limited to, the following nonlimiting features:
In one embodiment of the present invention, a variant of surrogate machine-learning (i.e., technology for machine learning tasks by examples) may be used to create training data for a machine-learning based classifier that extracts risk-indicative sentences. One useful technique is described by Sriharsha Veeramachaneni and Ravi Kumar Kondadadi in “Surrogate Learning—From Feature Independence to Semi-Supervised Classification”, Proceedings of the NAACL HLT Workshop on Semi-supervised Learning for Natural Language Processing, pages 10-18, Boulder, Colo., June 2009. Association for Computational Linguistics (ACL), the contents of which is incorporated herein by reference.
A risk type classifier 230 classifies each risk pattern by risk type (“RT”), according to a pre-defined taxonomy of risk types. In one embodiment of the present invention, this taxonomy may use, but not limited to, the following non-limiting classes:
A risk clusterer 240 groups all risks in the database by similarity, but without imposing a pre-defined taxonomy (data driven). In one embodiment Hearst pattern induction may be used. Hearst pattern induction was first mentioned in Hearst, Marti, “WordNet: An Electronic Lexical Database and Some of its Applications”, (Christiane Fellbaum (Ed.)), MIT Press 1998, the contents of which is incorporated herein by reference. In another embodiment of the present invention a number k is chosen by the system developer, and the kNN-means clustering method may be used. Further details of kNN clustering is described by Hastie, Trevor, Robert Tibshirani and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Second Edition Springer (2009), the content of which is incorporated herein by reference. In such a case, the risks are grouped into a number, i.e. k, of categories and then classified by choosing the cluster with the highest similarity to a cluster of interest. In another embodiment of the present invention, hierarchical clustering is used. Alternatively or in addition to, both k-means clustering and hierarchical clustering may be used.
A risk alerter 250 performs a similarity matching operation between the risks in the database and likely instances of P or Q in a textual feed 110. If evidence for P is found, the risk P=>Q is “imminent”. If evidence for Q is found, the risk P=>Q has materialized. In one embodiment of the present invention, the risk alerter 250 passes warning notifications to a user 130 directly.
As a result, when inspecting the risk database the user 130 (e.g. a risk analyst) can take immediate action before the risk materialises and increase the priority of the management of imminent risks (“P!, . . . , P!, P!, P!, . . . P! . . . ”) in the textual feed and materialized risks (“Q!”) as events unfold, without having to even read the textual feeds.
In one embodiment of the present invention, the output of the risk alerter 250 is connected to the input of a risk routing unit (not shown in
In one embodiment of the present invention a set of risk descriptions as extracted from the corpus defined as the set of all past Security Exchange Commission (“SEC”) filings is matched to the risks extracted from the textual feed. The method proposes one risk description or a ranked list of alternative risk descriptions for inclusion in draft SEC filings for the company operating the system, in order to ensure compliance with SEC business risk disclosure duties.
The present invention may use a variety of methods for risk identification. For example, as depicted in
In
In
The above-described examples and techniques for mining risks may be used individually or in any combination. The present invention, however, is not limited to these specific example and other patterns or techniques may be used with the present invention. The mined patterns from these examples and/or from the techniques of the present invention may be ranked according to ranking algorithms, such as but not limited to statistical language models (LMs), graph-based algorithms (such as PageRank or HITS), ranking SVMs, or other suitable methods.
In one aspect of the present invention a computer implemented method for mining risks is provided. The method includes providing a set of risk-indicating patterns on a computing device 120; querying a corpus 110 using the computing device 120 to identify a set of potential risks by using a risk-identification-algorithm 140 based, at least in part, on the set of risk-indicating patterns associated with the corpus 120; comparing the set of potential risks with the risk-indicating patterns to obtain a set of prerequisite risks; generating a signal representative of the set of prerequisite risks; and storing the signal representative of the set of prerequisite risks in an electronic memory 150. The method may further include determining an imminent risk from the prerequisite risks, the imminent risk being determined using the risk-identification-algorithm 140, the imminent risk being associated with at least one risk from the set of prerequisite risks; generating a signal representative of the imminent risk; and storing the signal representative of the imminent risk in the electronic memory 150. Still further, the method may further include, after storing the signal representative of the set of prerequisite risks, determining a materialized risk, the materialized risk being determined using the risk-identification-algorithm 140, the materialized risk being associated with the set of risks; generating a signal representative of the materialized risk; and storing the signal representative of the materialized risk in the electronic memory 150. Moreover, the method may still further include, after storing the signal representative of the imminent risk, determining a materialized risk, the materialized risk being determined using the risk-identification-algorithm 140, the materialized risk being associated with the imminent risk; generating a signal representative of the materialized risk; and storing the signal representative of the materialized risk in the electronic memory 150.
Desirably, the corpus 110 is digital. The corpus 110 may include, but is not limited to, news; financial information, including but not limited to stock price data and its standard derivation (volatility); governmental and regulatory reports, including but not limited, to government agency reports, regulatory filings such as tax filings, medical filings, legal filings, Food and Drug Administration (FDA) filings, Security and Exchange Commission (SEC) filings; private entity publications, including but not limited to, annual reports, newsletters, advertising and press releases; blogs; web pages; event streams; protocol files; status updates on social network services; emails; Short Message Services (SMS); instant chat messages; Twitter tweets; and/or combinations thereof.
The risk-identification-algorithm 140 may be based upon various factors and/or criteria. For example, the risk-identification-algorithm 140 may be based upon, but not limited to, a set of terms statistically associated with risk; upon a temporal factor; upon a set of customized criteria, etc. and combinations thereof. The set of customized criteria may include and/or take into account of, for example, an industry criterion, a geographic criterion, a monetary criterion, a political criterion, a severity criterion, an urgency criterion, a subject matter criterion, a topic criterion, a set of named entities, and combinations thereof.
In one aspect of the present invention, the risk-identification-algorithm 140 may be based upon a set of source ratings. As used herein, the phrase “source ratings” refers to the rating of sources, for example, but not limited to, relevance, reliability, etc. The set of source ratings may have a one to one correspondence with a set of sources. The set of sources may serve as a source of information on which the corpus 110, 210 is based. The set of source ratings may be modified based upon an imminent risk, a materialized risk, and combinations thereof.
The method of the present invention may further include transmitting the signal representative of the set of prerequisite risks, transmitting the signal representative of the imminent risk, transmitting the signal representative of the materialized risk, and combinations thereof. Moreover, the present invention may further include providing a web-based risk alerting service using at least one of the signal representative of the set of risks, the signal representative of the imminent risk, the signal representative of the materialized risk, and combinations thereof.
In another aspect of the present invention a computing device 120, as depicted in
In another aspect of the present invention, a computer system 500, as depicted in
While the invention has been described by reference to certain preferred embodiments, it should be understood that numerous changes could be made within the spirit and scope of the inventive concept described. Accordingly, it is intended that the invention not be limited to the disclosed embodiments, but that it have the full scope permitted by the language of the following claims.
