Enterprise Data Management in Capital market

Enterprise Data Management in Capital market

Reference data is the data used to classify other data in any enterprise. Reference data is used within every enterprise application, across back-end systems through front-end applications. Reference data is commonly stored in the form of code tables or lookup tables, such as country codes, state codes, and gender codes.

Reference data in capital market is the back bone of all financial institutions, banks and investment management companies. Reference data is stored and used in front office, middle office and back office systems. A financial transaction uses the reference data when interacting with other associated system and applications. Reference data is also used in price discovery for the financials instruments.

Reference data is primarily classified into two types –

  • Static Data– Financial instruments & their attributes, specifications, identifiers (CUSIP, ISIN, SEDOL, RIC), Symbol of exchange, Exchange or market traded on(MIC), regulatory conditions, Tax Jurisdiction, trade counter parties, various entities involved in various financial transaction.
  • Dynamic Data– Corporate actions and event driven changes, closing prices, business calendar data, credit rating etc.

Market Data

Market data is price and trade-related data for a financial instrument reported by stock exchange. Market data allows traders and investors to know the latest price and see historical trends for instruments such as equities, fixed-income products, derivatives and currencies.

Legal Entity data

The 2008 market crisis exposed severe gaps in measuring market credit and market risk. Financial intuitions are facing hard challenge to identify the complex corporate structure of the security issuer and other counter parties & entities involved in their business. Institutions must have the ability to roll up, assess, and disclose the aggregate exposure to all the entities across all asset classes and transactions. Legal Entity is the key block of this data which will help the Financial institution to know all the parties with whom they are dealing with and help to manage the risk.

The Regulation rules like The Foreign Account Tax Compliance Act (FATCA), MiFID II will require absolute clear identification of all the entities associated with the security. LEI plays a vital role to perform such due diligence.

EDM workflow

  • Data Acquisition – Data is acquired from leading data providers like Bloomberg, Reuters, IDC, Standards & Poors etc.
  • Data Processing –Data normalization & transformation rules are applied & validation processes clean the data.
  • Golden Copy creation – Cleaned & validated data is transformed into more trusted Golden Copy data through further processing.
  • Data Maintenance –  Manual intervention if necessary to handle the exceptions that cannot be handled automatically.
  • Distribution/Publishing – Golden Copy data is published to the consumer application like Asset Management, Portfolio management, Wealth Management, Compliance, Risk & Regulatory applications, other Business Intelligence platform for Analytics.

Importance of efficient EDM system

The fast changing regulatory & business requirements of financial industry, poor quality of data, competition demand a high quality centralized data management system across the firm.

In current market situation companies, must be able to quickly process customer requests, execute trading requests quickly, identify holdings and positions, assess and adjust risk levels, maximize operational efficiency and control, and optimize cost all while implementing regulatory and compliance needs in a timely fashion.

An efficient EDM system enable the business to –

  • Establish Centralized database management system
  • Reduced manual work
  • Decreased operational risk
  • Lower data sourcing costs
  • Having better view of data
  • Governance & auditing needs
  • Better overview on risk management
  • Tailor-made user rights
  • Analytics & data driven decision

Challenges need to overcome

  • Data quality & data accuracy
  • Siloed data and disparate data across firms making it difficult to have consolidated view of the risk exposure
  • Data lineage
  • Keeping the cost lower in such a fast changing financial market
  • Ability to quickly process customer requests, accurately price holdings, assess and adjust risk levels accordingly

Complexity of latest national and international regulations

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