Rebuild Financial Data Model: Start with Security Master

Drivers for rebuilding efforts:
Regulatory, Risk management and KYC (Know Your Customer) compliance regulations are the major drivers that is currently driving several key initiatives worldwide in Financial Industry.

Major Benefits:
Among these initiatives Security Master for Stock & Fixed Income Instruments at enterprise level is a potential candidate one that could provide big gains and maintenance ease within their enterprise Financial Data model besides providing data accuracy, flexibility to its various downstream applications from one single source of truth.

Current Design Issues:
“Security Master” that exists in the current form and shape are evolved over period of time and carry a heavy baggage of processing and huge of amount of data redundancy even for basic reporting needs. Secondly these evolved ones are built on mainframe or older technologies and are currently not scalable or flexible to demanding customer reporting requirements.Thirdly the cost to maintain a single truth on instruments turns out be pretty expensive proposition because of copies of the same data set exists in different silos with various degree of relevance by time periods.Lastly they are built around outdated batch process and are not real-time.

Security Master A Data Store:
Security Master is a Data Store holding an Integrated view of Financial securities information  of Issuers, & Issues from Global exchanges and are easily identifiable with cross reference identifiers like CIN, ISIN, CUSIP, Ticker Symbols, Industry classification, exchange listing, dividends, splits, Ratings (From Moody, Standard & Poor, Fitch), Yield Rates etc. 

Security Master serves as a "Universe of Securities" addressing the various subject area needs for Equities, Fixed Income, Swaps, Repos, Commodities, Futures, Derivatives, Funds, Options, Indexes, Security Lending, Custodial Services, Asset Management, Alternate Investments for both operational and reporting needs with different data set requirements. Security Master is a subset of the Integrated Financial Data model in many companies.

This Data Store gets its daily updates either on demand or or real-time from several major vendors like Bloomberg, JJ Kenny, IDC and others.

Some of the Key Data Sets that could potentially comprise or included in the Financial Data Model are:

Fundamental Data S&P Compustat (Basic) Annual and Qtrly BackData (S&P) Worldscope Market Guide (Multex) Valueline Zacks  Fundamental Research w/Ratings Ratings Direct Global (S&P) Ratings Direct for Muni’s (S&P) Fitch IBCA – Muni’s & Corporates Moody’s High Grade/Bank Research Moody’s Leveraged Finance Pricing Databases Fund Runs (FT Interactive) Merrill Lynch JJKenny MuniView (FT Interactive) Telekurs Bloomberg Data Other Fundamental Research Ned Davis (Via Factset) Quote Systems Bloomberg ILX Reuters RTW Instinet TM3 (Thomson) Marquee

Analytics Factset Salomon YieldBook Baseline Wilshire Atlas Reuters R&A MMD (Thomson) PCPoint (Lehman) MONIS KBC Financial Vestek Events/Conferences Alerts CCBN (Includes Street Events) Transcript Service (CCBN) Event Briefs (CCBN) Benchmarks MSCI Indices S&P Index Alert S&P Constituent History Russell Indices FTSE Proxy Service ISS (Institutional Shareholder Svcs)

In some organizations, Security Master is closely linked with Corporate Actions data which mainly consists of the following types:

• Cash Dividend
• Stock Dividend
• Stock Split
• Name Change
• Domicile Change
• State of Incorporation
• Round lot change
• Ticker Change
• Re convention
• Re denomination
• ID Number Change
• Equity De-listing
• Change in listing
• Equity Listing
• Variable Interest Reset
• Voting rights Change
• Currency Quotation Change
• Shareholder meeting.. etc

Fixed Income Instrument master Attributes:

• Security id
• CUSIP
• ISIN
• SEDOL
• Short name - short name for the security.
• Type - The first level of security classification.
• Sub type – The second level of security classification.
• Country of incorp - country where the issuer company is incorporated.
• State - The state of the security. For example active, defunct, bankrupt and so on.
• Last status change date
• Rating
• Principal payment frequency and other details related to it like last date and next payable date
• Issuing details
• Issuer name
• Face value
• Paid up value
• Non paid amount
• Start date - The date on which security is created in the market.
• End date

Others

• Underlying security - Another security associated with the security as in the case of ADR’s.
• Contractual income -cash dividend is paid contractually.
• Convertible - indicates security is convertible to another security
• Taxable - indicates that security is taxable.
• Contractual settlement - indicates contractual settlement is allowed. This is usually used in trade settlement.
• Issue currency
• Stock exchange listings
• Units
• Price
• Volumetric details
• Legal restrictions
• Interest details
• Factor
• Percentage outstanding
• Pool factor details
• Other linked security details like the specific instrument, type, qnty etc

Data Model:
Choice of Data model becomes a key for any successful Security Master Data hub implementation. Some organization have build their own while some have purchased Financial data models off the shelf from vendors like FTI, Golden Source and have implemented Security Master. There are two sectional pieces in any Financial Data model. One which changes often like Pricing Data and others which changes rather infrequently like 'Issuer information. Nevertheless a Financial Data model should support as a "Universe of Securities" with tight linkage between its various functional Data Sub Models.

Data Load Enablers (ETL/Scripts):
The Data load enablers can be synchronized either through generic scripts are via ETL Jobs to Update/Insert newer data with appropriate Data Governance to load data into various entities of the Financial Data Model. There are four phases in any Data load process.

Data Cleansing

• Data Cleansing is the act of detecting and correcting (or removing) corrupt or inaccurate records from a record set.
• A data profile will be carried out to check inconsistencies in data
• Standard corrections will be performed after the extract process
• Theses corrections will be performed by  validating and correcting values against a known list of data issues.

Data Standardization

• Standardization helps us to transform inconsistent data into one common product or entity representation.
• This includes uniform abbreviations, correct spelling, formatted patterns, etc.
• This task will be performed by building a consensus with data stewards and owners on acceptable standardizing methods and values.

Data Matching

• Matching is the algorithmic comparison of two or more sets of records which relate to the same individual or entity
• Determine, based on the profile of data and amount of duplicates, the applicability of a Probabilistic or Deterministic match algorithm
• Load data into the Matching Technology Tool to produce matching Results
• Review  results, tune algorithm if desired and re-run algorithm.

Data Managing/Linking

• Based on match results and its review, set merge and link rules
• Produce merges/links
• Manually review merges/links over a sample set of data
• Determine suitability and “Go-Live” on finalized rules

Data Governance:
Data Governance is an emerging discipline which marriages Data Quality, Policies, Processes and Risk on handling of data in an organization.Organization plays a key and vital role in Data Governance and  Non IT personnel are required in data decisions and IT should not make these decisions by itself. Data ownership at Top Tier management should be the main concern.Regulation, compliance (SOX, Basel II), or contractual requirements call for formal Data Governance today in Financial industry.

• Organization’s main focus is around Data Quality and hence the main objective for running Data Governance program.
• The Organization should build clear decision rules and decision making processes for its shared data.
• Organizations should plan to move towards from Silo ed data management to Integrated Data management so that quality and governance can be assured at an Enterprise level.
• Organizations should increase the value of data assets and have a robust mechanism to resolve data issues.

 A Security Master Framework

ISO Standards:
Thousands of companies follow ISO Standards while integrating their process for Data Convergence, Consumption & Publication of Quality data feed. Following are the list of ISO Standards relating to Financial Instruments that could potentially be used while building Security Master:

• ISO9362 (BIC Bank Identifier Codes)
• ISO3166 (Country Codes)
• ISO4217 (Currency Codes)
• ISO10383 (Exchange/Market Codes)
• ISO6166 (ISIN Security Codes)
• ISO10962 (Security Types)
• ISO2014 (Date format)
• ISO8532 (Certificate Numbers)
• ISO 639  (Languages)

Building a Financial Data Model including Security Master involves understanding the Financial sphere/arena and offerings by the Organization, IT Structure & Support, Management Best Practices, Business Processes Maturity Model. Above ALL be able to see the Big Picture and Breath on Details while working as a team of member in building a quality Enterprise level Security Master hub and taking pride in each step of the way as a participant.