Semantics and Ontology – The Future of Data Aggregation - Wipro

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SEMANTICS AND ONTOLOGY – THE FUTURE OF DATA AGGREGATION

Giridhar Vugrala, Management Consultant Gaurav Bafna, Business Analyst Sonali Pawar, Business Analyst

Table of Contents 03 ................................................................. Abstract 03 ................................................................. Challenges of Financial Data Management 05 ................................................................. Semantics and Ontology 06 ................................................................. Application of Semantics and Ontology to Financial Data Management 07 ................................................................. Operating Model 07 .................................................................. Pricing Model 09................................................................. Conclusion 09 ................................................................. References 10 ................................................................. About the Authors 11 ................................................................. About Wipro’s BFSI Business Unit 11 ................................................................. About Wipro Ltd.

Abstract The quest for futuristic solutions and models that bring positive changes in Data Management has always been to transform sourcing, distribution and managing the golden copy of data. Unlike sourcing, chaffing and maintaining data at every stage of the trade lifecycle, the data in itself is transforming into a platform with centralized data management principles. This article explains the application and benefits of using semantic technology combined with ontological structures allowing semantic requests to source business-driven data.

Challenges of Financial Data Management Financial Data Management has become more important and complex over the years due to the following reasons: Increased regulatory compliance: The 2008 financial crisis has led to tighter regulatory norms and more stringent compliance and reporting requirements. This meant better financial performance and financial data management.

There is much more focus in the market on improving data quality overall due to regulatory, risk management, and business pressures. Timeliness and accuracy of data are both of primary importance in a market where financial and reputational damage can be caused by inaccurate reporting to regulators

Increased Cost: When firms are operating in a low returns model,

and clients. Getting the right framework in place to

managing data is proving to be more cost intensive and inefficient.

measure data quality and to ensure the taxonomies

Timeliness and Efficiency: From the perspective of consumers, it is of

are consistent across an organization is therefore

utmost important to receive data from the aggregators and deliver it

very important.

internally in time to enable optimized batch processes. Given the

Virginie O'Shea

dependencies on batch processing timelines, it has become even more

Senior Analyst, Securities and Investments

important to deliver data with high quality and minimal human

Aite Group

interventions. Refer to Figure 1 for what consumers are looking for. 3

Timeliness of response

8

Business satisfaction

6

Efficiency/reduced manual effort

6

Data quality

2 4 2

4

5

Accuracy

3

Reduction in cost

3

Scalability

3

1

Ease of use of system

3

1

Consistency of reported data

2

Operational risk reduction

2

Elimination of legacy

1

Data availability

1

3 2

4

3

Completeness 1

Don’t know 0

5 Asset managers

10

15

Wealth managers

Source: Aite Group survey of 26 asset managers, 2013 Figure 1: Results of survey asking respondents the criteria used to measure success of a data management implementation (Source: Aite Group, 2013) Redundant data: Financial Data Management is done in independent

Lack of single golden source: Firms have to source data from

silos by organizations, which results in higher costs, further aggravated by

multiple vendors for various asset classes. There is no universal standard,

redundant data subscriptions and more technology platforms to cleanse

leading to many data vendors and firms using proprietary standards to

and manage data.

identify and to enrich their data.

Duplication of efforts: Same data is maintained separately by

Ever-changing technology and skills: Technology is changing at a

the front-office, middle-office and back-office teams, because

rapid pace and so is the demand for specific skillsets. Firms pay a premium

their usage varies. This leads to added complexity and duplication

in this catch-up game and end up focusing on non-core businesses to

of efforts.

remain competitive.

Semantics and Ontology Semantic technologies are meaning-centered that include tools for

Semantic

auto-recognition

topics, concepts, information, extraction, and

representation of data enables machines as well as people to

categorization. Semantic technology provides an abstraction layer above

understand, share, and reason data and its file content during

existing layers to enable bridging and interconnection of data and processes.

execution time. Figure 2 explains the ontological structures of Basic

At the same time, they provide level of depth that is far more intelligent,

Financial Instruments concepts like stock exchange market, trading

capable, relevant, and responsive interaction than with information

day, and analysis.

of

technology

when

combined

with

the

Ontological

technologies alone. 4

FI:rating

FI:summary

FI:grade Ownership

FI:grade Valuation

FI:grade Technical

FI:grade Fundamental

FI:Location

rdfs:domain

rdfs:domain

rdfs:domain

rdfs:domain

rdfs:domain

rdfs:domain

rdfs:domain

FI:Mean Analysts Recommendation rdfc:subClassOf

FI:value

FI:date

rdfs:domain

rdfs:domain

FI:Fundamentals rdfc:subClassOf

rdfc:subClassOf

FI:PE

rdfc:subClassOf

rdfc:subClassOf

FI:Analysis

Owl:inverseOf

rdfs:range

rdfs:range

FI:HasTraded

FI:TradedAt

FI:Has Analysis

rdfs:range

FI:Has Valuation

FI:ETF

FI:Financial Instrument

rdfc:subClassOf

FI:IsBased On rdfc:subClassOf Owl:inverseOf

rdfs:domain

FI:ISIN

FI:date

rdfs:domain

rdfs:domain

FI:Stock

rdfs:domain

FI:open

FI:Symbol

rdfs:domain

rdfs:range

FI:Valid For

FI:close rdfs:domain

FI:Equity-related Instrument

rdfs:domain

rdfs:domain rdfs:domain

FI:name

FI:PS

rdfc:subClassOf

FI:Stock Exchange Market

FI:Stock Scouter Rating

rdfs:domain

FI:low

Owl:inverseOf

rdfs:domain

FI:Traded On FI:high

FI:Is Part Of rdfc:subClassOf

FI:PreviousTrading Day

FI:Equity-linked Derivate

Owl:inverseOf

rdfs:domain

rdfs:domain

rdfc:subClassOf

rdfs:domain

FI:KO Barrier rdfc:subClassOf

FI:Interest Rate Instrument rdfc:subClassOf

rdfc:subClassOf

FI:Deposit

FI:Loan

FI:Next Trading Day

FI:volume rdfs:range

FI:KO Certificate

FI:Forward Type Derivate

FI:Trading Day

rdfc:subClassOf rdfc:subClassOf

FI:Option Type Derivate

rdfc:subClassOf

FI:Buy Trading Day

rdfs:domain

FI:Sell Trading Day

FI:Trade Reason

rdfs:domain

rdfc:subClassOf

Figure 2: Ontological structures of Basic Financial Instruments concepts like stock exchange market, trading day, and analysis Source: Employing Semantic Web technologies in financial instruments trading by Dejan Lavbic and Marko Bajec

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Application of Semantics and Ontology to Financial Data Management Application of Semantics technology has been limited to academic

The role of semantic integration defines a wrapper around data sources and

purposes barring a couple of exceptions. With respect to financial data,

establishes dynamic links between Ontology entities (e.g. classes, properties,,

the use of Semantics and Ontology will bring considerable changes to

etc.) and the data sources. This is best explained with an example where an

the industry:

asset manager is interested in sourcing data for “Bonds with yield greater

•

Shift towards a consumer-centric data consumption model

•

Organizing data based on Ontological structure with a Semantic

than 7% in APAC”. There are two aspects involved here: •

First, the semantic engine has to decode the business language, identify data elements and the relational factors.

language layer •

Consumers shall pay for the data that they consume

•

Data consumers can focus on sourcing credible data rather than

•

Second, based on the data elements and relational factors, the underlying small case. Thus, semantic engine should be able to traverse through the ontological structures to get the related bonds

ascertaining data credibility in house

that satisfy the conditions as depicted in Figure 3.

Data

Market Data

Reference Data

Analytics Data

Yield

Duration

Geographies

Instrument

Name

ID (ISIN, Cusip)

Valuation

APAC

Geography

US

Europe

Figure 3: Ontological representation for a subset of data for getting yield rates for a specific category of fixed income security

The Future Operating and Pricing Models Financial Institutions are forever reshaping their operating models and

data-centric operations and ownership. The application of Semantic technology

processes to bring in more data credibility and reduce the total cost of

can enable operating models and cost of sourcing to significantly change.

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Operating Model Traditionally, the data was sourced from multiple vendors/aggregators

b) Data credibility is maintained and managed by the data aggregators

based on the quality and the prioritizing needs of the data. However, the

c) Target model fundamentally shifts the golden source of data

future operating model as depicted in Figure 4 fundamentally relies on the

residing in consumer’s firm to the data aggregator’s firm

following principles:

d) This model aggregates the data from various sources that

a) Aggregators provide data based on semantic language

meet the needs of the data consumers

Future Operating Model

Issuers

Exchanges

Regulators

Brokers

News Agency

Ratings

Prospectus

Financial Websites

Finacial Information

Data Governance

Data Stewardship

Content Management

Quality Management

Technology Management

EOD/SOD

Real-time

Ontology Mapper

Dynamic Requests

Time Series

On-demand

Semantic Language Processor

Pre-configured Requests

Asset Managers

Brokerage Firms

News

Analytics

Operations Management

Fund Managers

Figure 4: Target Operating Model

Pricing Model Invariable pressure on the data aggregators to provide better credibility and

Given better options, Financial Institutions can pay for the data that they actually

lower costs to consumers shift the focus from providing huge volume of data

consume in their business rather than the entire offering. Figure 5 below depicts data

to the more relevant and meaningful data that satisfies the firm's business.

consumption usage against various asset classes and data characteristics. 7

Factors that affect pricing of data

Instrument Types

Complexity

Quality

Quantity

Geography

Time

Funds

Data needed by user

Derivatives

Redundant data user pays for

Bonds Equities Price paid by user Figure 5: Data pricing using Traditional Model

The proposed Semantics and Ontology-based model would price

Institutions to move from a uniform pricing to a pay-as-you-go model.

data based on various parameters such as type of instrument,

Figure 6 represents the various dimensions such as asset classes,

instrument attributes, geography, complexity of sourcing data,

type of instrument and other factors (quality, complexity, etc.) for

quality of data, and time delay factor. This model helps Financial

pricing and the semantic usage of data sourcing.

Factors affecting data pricing Complexity

Quantity

Geography

Quality

Time

Equities

Get Corporate Actions data for US stocks

Bonds

Get BB-rated LATAM bonds with yield greater than 5%

Asset Classes

Indices

Derivatives

Get all indices with annual return greater than 5%

Fund

Get EOD prices for OTC derivatives

ABS/CMOS

Get rating data for ETFs traded in US

Commodities

Forex

Get issuer data for my custom portfolio

Custom Instrument Data

Issuer Data

Corporate Actions

Ratings Data

Analysis Data

Regulatory Data

Pricing Data

Market Data

Type of Data Figure 6: Data pricing using the Proposed Model 8

Conclusion Acquiring data from multiple sources, integrating with the existing systems and processes, and ascertaining data quality has always been a challenge. Although in a nascent stage, the advent of Semantics and Ontology has been transforming with the development of cognitive technology and tools. The application of these technologies in the financial services industry is certainly a revolution that would only succeed with the changes to the underlying data organization and management. With the increasing thrust on operations improvement and reducing cost of ownership, this technology would be adapted slowly but with great caution and care. To reap the benefits of this shift in data management, it is imperative that this initiative and implementation should come from the data aggregators. Without the initiative from the data aggregators, this technology would remain forever un-incubated. This change would eventually happen as the consumers see a huge potential in terms of outsourcing the management and maintenance of "Golden Source of Data" outside the firm’s environment to benefit from the economies of scale.

References •

Employing Semantic Web technologies in financial instruments trading by Dejan Lavbic and Marko Bajec

•

Compliance and competitiveness – A report from the Economist Intelligence Unit

•

Reference Data Acquisition Challenges: Getting it Right From the Start by Aite Group

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About the Authors Giridhar Vugrala Giridhar Vugrala is a Management Consultant with the Capital Markets business at Wipro Technologies. His area of expertise spans business architecture and development of solutions in areas of data management, exchanges, front office and product control. Giridhar has been involved in executing strategic multi-million dollar signature programs of global reputation and visibility across North America, Europe, Middle East and Africa as well as Asia Pacific. Giridhar has hands-on experience in functional, technology and delivery of systems across Fixed Income, Derivatives, Wealth Management and Trading, Quant and Investment Systems. Giridhar is a graduate in Computer science from Osmania University and masters in Finance and Systems from JNTU. He is based out of Bangalore and can be reached at: [email protected]. Gaurav Bafna Gaurav is a Business Analyst with the Securities and Capital Markets vertical at Wipro Technologies. He has pursued his Masters of Business Administration in Finance from IIM Shillong. He is based out of Bangalore and can be reached at: [email protected]. Sonali Pawar Sonali is a Business Analyst with the Securities and Capital Markets Industry Advisory Group at Wipro Technologies. She has pursued her Masters of Business Administration, specializing in Finance from Symbiosis Center for Management & HRD, Pune. She is based out of Bangalore and can be reached at: [email protected].

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About Wipro’s BFSI Business Unit Wipro’s Banking, Financial Services and Insurance (BFSI) business unit specializes in providing Consulting, Information Technology and Business Process Services to our clients across all industry segments - retail banking, commercial banking, buy side firms, sell side firms, market infrastructure providers, life and annuity insurers as well as property and casualty insurers. The BFSI business unit contributes close to 27% of the company revenues, has over 100 client relationships spanning Americas, Europe, Middle East, Africa and Asia Pacific, which are serviced by more than 25,000 professionals globally. Wipro has been the trusted partner of choice for the largest and most reputed financial institutions that are looking to ‘differentiate at the front’ and ‘standardize at the core’. Wipro offers an unparalleled advantage to financial institutions by leveraging its industry-wide experience, vertically aligned business model, comprehensive portfolio of services and deep technology expertise.

About Wipro Ltd. Wipro Ltd. (NYSE:WIT) is a leading Information Technology, Consulting and Business Process Services company that delivers solutions to enable its clients do business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of "Business through Technology" - helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner's approach to delivering innovation, and an organization wide commitment to sustainability, Wipro has a workforce of over 150,000, serving clients in 175+ cities across 6 continents. For more information, please visit www.wipro.com

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