Risk identification and risk register generation system and engine

Patent metadata

Field	Value
Publication number	US-2016371618-A1
Application number	US-201615181194-A
Country	US
Kind code	A1
Filing date	Jun 13, 2016
Priority date	Jun 11, 2015
Publication date	Dec 22, 2016
Grant date	—

Abstract

The present invention relates to a computer-based system for generating a risk register relating to a named entity. The system comprises a computing device, a risk database accessible by the computing device and having stored therein a set of risk types based on an induced taxonomy of risk types previously derived at least in part upon operation of a machine learning module, an input adapted to receive a set of source data, the set of source data being in electronic form and representing textual content comprising potential risk phrases, a entity-risk relation classifier adapted to identify and extract entity-risk relations from the set of source data, a risk tagger adapted to identify in the set of source data a set of risk candidates (r i ) based on the set of risk types, a entity tagger adapted to identify mentions of entity names (c i ) in the set of source data, and a risk register aggregator adapted to generate a first risk register based on the set of tuples associated with a first entity.

First claim

We claim:

1 . A computer-based system for generating a risk register relating to a named entity comprising:
a computing device having a processor in electrical communication with a memory, the memory adapted to store data and instructions for executing by the processor; a risk database accessible by the computing device and having stored therein a set of risk types based on an induced taxonomy of risk types previously derived at least in part upon operation of a machine learning module; an input adapted to receive a set of source data, the set of source data being in electronic form and representing textual content comprising potential risk phrases; an entity-risk relation classifier adapted to identify and extract entity-risk relations from the set of source data, the entity-risk relation classifier comprising:
a risk tagger adapted to identify in the set of source data a set of risk candidates (r i ) based on the set of risk types; and
an entity tagger adapted to identify mentions of entity names (c i ) in the set of source data;
wherein the entity-risk relation classifier maps the identified set of risk types to the identified entity names to generate a set of tuples [ENTITY c ;RISK r ]; and
a risk register aggregator adapted to generate a first risk register based on the set of tuples associated with a first entity.

2 . The system of claim 1 wherein the identified names are stored in a entity index and the first risk register is associated with ENTITY cl , defined as the set of all risks l . . . r . . . |R| where the entity index (c) is the same.

3 . The system of claim 1 wherein the set of source data received comprises one or more of: an indexed search; a news archive; a news feed; structured data sets; unstructured data sets; social media content; regulatory filings.

4 . The system of claim 1 wherein the entity-risk relation classifier maps the set of risk types to the entity names (c i ) in the set of source data to generate the set of tuples, the results comprising candidate risk exposure relationship tuples.

5 . The system of claim 1 wherein the entity-risk relation classifier is further adapted to filter the set of tuples to eliminate false positive tuples.

6 . The system of claim 1 further comprising an output adapted to generate and transmit a risk alert in response to an update to the first risk register.

7 . The system of claim 1 wherein the entity-risk relation classifier is adapted to map the set of risk types to a plurality of entity names (c l . . . c n ) to generate a plurality of sets of tuples (t l . . . t n ) for each of the entity names and the risk register aggregator is further adapted to generate a plurality of risk registers (rr l . . . rr n ) respectively associated with entity names (c l . . . c n ) and sets of tuples (t l . . . t n ).

8 . The system of claim 7 wherein the input is further adapted to receive a search query and to execute a risk search on the plurality of risk registers (rr l . . . rr n ).

9 . The system of claim 7 further comprising:
a risk register database adapted to store the plurality of risk registers (rr l . . . rr n ); and
a search engine adapted to receive and execute a search query on the plurality of risk registers (rr l . . . rr n ).

10 . The system of claim 1 further comprising a user interface module adapted to generate for display a risk visualization interface representing aspects of the risk register.

11 . The system of claim 1 wherein the entity-risk relation classifier is adapted to identify and extract entity-risk relation mentions by using a set of purpose-defined features for risk sentence classification implemented as a Support Vector Machine (SVM).

12 . The system of claim 11 wherein the Support Vector Machine (SVM) is trained and wherein the set of purpose-defined features is derived from a corpus of text to inform classification based on a machine learning process.

13 . The system of claim 11 wherein the set of purpose-defined features includes a tree kernel.

14 . The system of claim 1 wherein the entity-risk relation classifier further comprises:
a supply chain risk tagger adapted to identify supply chain relationships between one or more companies identified by the entity tagger and to identify in the set of source data a set of supply risk candidates (sr i ) based on a set of supply risk types associated with supply chain risks;
wherein the first risk register comprises a tuple representing a supply risk type.

15 . The system of claim 13 further comprising a user interface module adapted to generate for display a risk visualization interface representing a supply risk type of the first risk register.

16 . The system of claim 1 further comprising a risk presentation module adapted to automatically generate a representation of risk for inclusion in a user-defined document.

17 . The system of claim 15 wherein the user-defined document is one of: an SEC filing; a regulatory filing; a power point presentation; a SWOT diagram; a supply-chain cluster diagram; editable text document.

18 . The system of claim 1 wherein the entity is selected from one of the group consisting of: a company; and a person.

19 . A method for generating a risk register relating to a named entity comprising:
receiving input from an indexed search and a news archive; creating from the input a risk taxonomy with risk types by a machine learning module; mapping the risk types to the named entity identified in the news archive, the results comprising candidate risk exposure relationship tuples; filtering the mapping results to eliminate false positive tuples; and generating in response to the identified tuples the risk register.

20 . The method of claim 19 further comprising generating a risk alert in response to an update to the risk register.

21 . The method of claim 19 further comprising performing a risk search on the risk register.

22 . The method of claim 19 further comprising displaying a risk visualization by representing aspects of the risk register.

Assignees

• Thomson Reuters Global Resources

Inventors

• Leidner Jochen L
• Nugent Tim
• Nourbakhsh Armineh
• Shah Sameena

Classifications

• G06Q10/0635 Primary

  Risk analysis of enterprise or organisation activities · CPC title

• G06N5/025

  Extracting rules from data · CPC title

• G06F16/2246

  Trees, e.g. B+trees · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N99/005

  Physics · mapped topic