1. Legal Entity Risk and Exposure - A Solution
Risk Data Aggregation
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Bill Freeman – Ontology2
Bill.freeman@ontology2.com
1-774-301-1301
LEGAL ENTITY RISK AND EXPOSURE
Counter party or Legal Entity risk and exposure has become a very important subject in both the U.S.
Financial market and the wider Global market.
Legal Entity risk is the risk to either party to a contract that their counter party will default on their
contractual obligations. Whilst this is a simple concept, the challenge facing financial services
organizations is far from simple. These organizations are looking to be able to understand the full
extent of their exposure to the risks associated with any given counter party or legal entity, to be able
to monitor market-driven events as they are occurring and to gain a rapid understanding of the
potential impact of these events, and to be in a position to take early action.
With the growing complexity of assets being traded, it is often difficult to determine the identity of the
issuer and as a result to understand the identity of the counter party, their parent and ultimate parent.
Likewise, with positions data scattered across many different internal systems, it is time consuming
for organizations to gather all the data together to determine their overall exposure to any given
counter party. And with the sheer volume of information available across the web that can be
monitored for events that could materially impact the level of risk, this can become an overwhelming
challenge.
And yet this challenge has recently been brought into sharp relief by events within the market. The
recent financial debacle was largely driven by mortgage-backed securities and credit default swaps
where the identity of the ultimate legal entity was obscure. Determining exposure and responsible
party on mortgage-backed securities was a complex challenge stemming from the fact that firms like
Lehman created subsidiaries in which these securities were issued and as default issues arose, it
was very difficult to identify the underlying responsible party associated with these securities.
There have been other high profile events that have also brought the problem to greater prominence,
including the Madoff situation. For many large global financial services firms, it took up to 3 weeks to
determine their exposure to Madoff. These market events have heightened the recognition that
greater effort is required to determine the full extent of Legal Entity Risk and Exposure for financial

2. services organizations, and this is compounded by new regulatory requirements such as Dodd-Frank
and Basel III.
The fundamental concept of counter party or legal entity risk and exposure is essentially
straightforward. It is simply a matter of knowing what one is holding, who issued it, who is responsible
for it, and what are the ongoing risks associated with it. While that seems relatively simple in concept,
implementing an automated approach to achieving this is far from simple. This can be an enormous
task when viewing it from the perspective of a small firm, but when viewing it from the standpoint of a
large global institution it can be overwhelming.
LEGAL ENTITY RISK AND EXPOSURE –
This solution is not designed to be a portfolio management system but is designed to be an event-driven
investigative framework that helps organizations identify potential events as they are occurring, to rapidly
respond by helping identify the affected instruments and the issuers and to allow organizations to
understand their potential exposure as fast as possible to allow them to take appropriate action.
It is known that this data doesn’t currently exist. Rather the problem is that operational staff, in the time-
starved environments within which they operate, are facing the challenge to get to, understand and
bring all the relevant information together in a timely manner. The data may be there, but the
intelligence to find it and make sense of it has to be added at precisely the moment there is no time
to do so.
The challenge is to bridge the gap between the masses of data, and the creation of actionable
insight. The Legal Entity Risk and Exposure solution is designed to bridge this gap, providing a
framework within which an early warning™ of potential events can be detected, the potentially affected
instruments and their issuers identified, and the positions determined to understand the potential
exposure. It automates much of the detection, discovery and presentation of relevant information,
leaving operational staff to make better informed decisions and helping them respond to events early.

3. LEGAL ENTITY RISK AND EXPOSURE - how it works
The Legal Entity Risk and Exposure application has four main components, each of which is
described below.
Exposure
Using semantic data integration, all the position and holdings data is drawn together from the
different systems across the organization, whether this is a single data source for a small firm,
through to the many different data sources within a large financial services organization. The solution
collects the following data:
• issue, issuer, parent, ultimate parent
• position
• current price
• current fx rates
• account
• account type
• asset manager
• client static data
• country of both client and issue
• currency of issue, firm, and client
• product
• sector
The calculation of exposure begins with individual positions. Once position data is obtained, the
current price is multiplied by the number of shares or units to determine the valuation. For purposes
of firm wide aggregate risk, the valuation is then converted to the base currency of the firm.
The exposure data can be selected and sorted in different ways so that an instant view of client,
country, sector, product or other criteria can be displayed.
There are additional issues and challenges in determining exposure at various levels. For example, a
given client can have multiple accounts across an organization that may require aggregation, for
which the application will access additional information from a customer information database.
Valuation of non-publicly traded holdings can be provided by accessing the current valuation as
carried on the books of the organization and through confirmation and validation against externally
provided market views of these valuations.

4. Legal Entity Identification
Counter party identification reveals the relation between issue, issuer, the issuer parent company, and
the ultimate parent company. This capability is provided through the integration with the Business
Entity Service provided by a third party like Avox. This award-winning service combines
entity details, including parent and ultimate parent information, with instrument details linked to the
issuing party.
Through integration with this service, the Legal Entity Risk and Exposure solution can determine the
complete view of instruments that are potentially affected for any given counter party, and instruments
that are affected through relationship within a corporate hierarchy to that counter party. This
capability extends to identifying Guarantors for instruments where they are known.
The discovery of the full range of instruments relating to any counter party is automated through the
solution, resulting in a list of instruments that are fed to the Exposure module to compile the
aggregated exposures against each instrument for any given counter party. This automated
investigation, detection of the full extent of a counter party, the detection of instruments affected, and
finally the gathering of positions data for these instruments significantly reduces the operational
overhead associated with tracking down this type of information. The Business Entity Service
provides detailed information on entities and on instruments issued by them.
Risk Profile Cloud Access
The Risk Profile Portal brings together different web-based data sources into a single portal both for
easy review by operational staff and for automated monitoring, automated extraction of data and
automated analysis. This capability combines the text extraction product with the data
services provided by a third party, supplemented by other data sources, particularly
social media, as needed. The categories of data that are included in the Risk Profile Portal are as
follows:
€˘ Pricing or Valuation (and history)
- SEC Filings, quarterly and annual reports
€˘ Fundamentals
€˘ Analyst Opinions
˘ Ratings
˘ Insider Trading
€˘ Dividends
€˘ Corporate Actions
€˘ News/Blogs/Social Networks
€˘ Legal Filings
€˘ SEC Complaints/Fines/Investigations
˘ FINRA Complaints/Fines/Investigations
€˘ FED Complaints/Fines/Investigations
The users can select which data sources they want to include in their Risk Profile and for which entity.

5. Financial Data Store and Risk Monitoring
The Financial Data Store is the back end repository for the data displayed in the Risk
Profile Portal. Using semantic technology, the content from the many different data sources and
gathered from the many different web sites is extracted and analyzed, providing a level of
understanding about the content that makes it possible to automatically monitor for potential risk
events through the configuration of appropriate rules.
Graph Model and Semantic technology is able to analyze and understand the content of these disparate data
sources and has the ability to derive much deeper meaning from the data, making the monitoring of events
through applied business rules significantly more effective.
Monitoring Rules and Exception Cases for Risk Events
It is time-consuming to manually monitor all the potential snippets of information that may be
relevant and which may affect the risk of default of any given counter party. The application of rules and
Inference can be leveraged; we believe it is necessary to have automated rules that will monitor each
potential data source.
Each data type has a semantic rule-driven application that actively monitors for events that can be
detected within this data source, such as Insider Trading, or changes in credit ratings, or negative
sentiment detected within social media. These rules fire when specified underlying conditions are
detected within the data source triggering an event within the application.
When an event is triggered, the application creates an exception case and performs data enrichment
to compile a complete view of the potential increased risk exposure. This will firstly collect related
information about the event to give operational staff a complete picture across multiple data sources.
• It will then automatically research the entity to discover the full extent of that entity and the
instruments that are potentially affected, resulting in a listing of these instruments. Finally, the
positions data can be brought together from across the different systems within the organization to
determine the aggregate risk exposure against all these instruments, and therefore the full extent of
the exposure to the counter party can be quickly reviewed.
Operational staff are alerted to events as they occur and can view the exception cases through a
specific User Interface that summarizes the risk event with a drill down capability to view the source
information, a summary of the counter party details, and a rolled up view of the overall exposure.
In some instances, an event can occur but its significance is not known. For example, a rule on
ratings data may simply be triggered when there is a notification on a ratings change. If a rating

6. changes, however, there is normally an underlying reason. When a rating goes down, depending on
the source of the data the reason may not be clear, but by monitoring other types of data the cause
for decline may become apparent providing operational staff a clearer picture as to the risk
worthiness of the underlying assets.
Each of the data types included in the risk profile has a set of rules that a user can select and apply
to that specific data type. Within these rules there are further adjustments that can be made to
improve and refine the identification of risk events. For example, on pricing a tolerance rule may be
applied. Since prices move as a percentage very differently for blue chip stocks versus pink sheets,
the user has the ability to not only apply a tolerance rule but also to set the percentage.
• All the exception cases are automatically assigned a priority code, and the users can immediately
see and review these cases in priority order.
Watch List Monitoring
In addition to the monitoring rules for the different data sources, there is also the ability to create
watch lists. The watch list allows users to add a set of tickers/cusips/sedols related to the common
stock of the ultimate parent of entities of interest. The watch list generates a watch report which
displays current information related to that ticker based on the selection of the user. For example, if a
rating changes for AT&T, the user can review the exception case and review the risk profile screen
and may not detect anything significant at this point. However, the user can place AT&T on their
watch list and can specify the data sources to monitor for this entity, for example pricing data and
news. As new pricing data and news data comes in related to AT&T it is placed on a watch report for
that user. This makes the monitoring of issues of concern easier and more efficient for the user.
Monitoring Rules for Positions Data
In addition to rule sets that may be data type specific, there are additional rules that a given firm may
require. These rules may relate to other data types. One example is exposure data. A rule set that
can be applied to exposure data that ignores monitoring of a position based on value may be desired.
To illustrate, if a large firm having millions of positions wants to set a threshold of 25k on position
monitoring, then any aggregate position less than a value of 25k will not be monitored.
IMPLEMENTATION OPTIONS
The Legal Entity Risk and Exposure application contains the 4 core modules described above,
namely:
Exposure
Legal Entity Identification
Risk Profile Portal

7. Financial Data access and Risk Monitoring
The Financial Data Access with all the risk monitoring rule infrastructure is provided through
a cloud service. Optionally, a complete installation can be provided for an organization within their
infrastructure.
The Risk Profile Portal is a configurable component that links to the cloud service, and allows
customers to define the data sources that they want to subscribe to that are provided through the
cloud service. This includes the ability to add additional data sources to which the customer already
subscribes, or chooses to subscribe, that are not offered as standard services within the cloud
service.
The Legal Entity Identification is provided as a subscription service through the cloud service.
The Exposure module is implemented within a client’s infrastructure, and is configured to connect to
the different transactional systems across their infrastructure to rapidly bring together the positions
data. This is a non-invasive method of gathering the information using the querying and reporting
methods of the underlying systems, but using semantic techniques to automate the data gathering
and common understanding across the data.
For clients that do not require position data but merely want to monitor a set of asset identifiers, the
application can be provided through the cloud service. For clients that do want to include position and
exposure data then the Exposure module and a supporting database must be installed at the client
site, and linked to the cloud service for the other 3 modules.
Optionally, the Financial Data Access and Risk Monitoring module can be implemented
within the client’s infrastructure, and connected to the external data services provided by a third party and
connected to other data services based on their requirements and existing subscriptions.
Please call for more information. Risk data aggregation may be implemented in a very flexible, agile and
fast manner. Results can be shown quickly.
We invite you to see a quick demonstration of LEI quality assurance capability at:
www.Ontology2.com
Please see a description of upcoming requirements and commentary at:
http://www.garp.org/#!/risk-intelligence/detail/a1Z40000003Bs5PEAS/risk-data-
aggregation-barcodes-finance