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Financial Institutions

The Praedea DEP automatically and efficiently extracts specific data from multiple sources to feed proprietary research, trading and credit risk (e.g., Basle II) models in real-time. Some of the many advantages that the Praedea DEP provides financial institutions, including investment banks, commercial banks, fund management companies (including hedge funds), financial advisory firms, credit ratings agencies, research houses and stock exchanges, include:

Substantial Cost Savings: No longer pay for data you don’t need. The Praedea DEP allows you to collect only the specific data that you desire customized to your precise specifications. Additionally, the Praedea DEP allows you to achieve more with limited internal technology and R&D resources. [top]

Real-Time Data Extraction: Upon the processing of documents with the Praedea DEP, data can be utilized in end-user applications almost instantaneously. No longer wait hours, days or weeks after filings are made to receive the data you desire. [top]

Universal Data Extraction Platform: The Praedea DEP extracts predetermined numeric figures and text from any semi-structured electronic document – whether US or foreign, in any language. [top]

Scalability: With the Praedea DEP, text-mining models in large part build themselves after initial manual tagging, enabling the building of models to extract thousands of terms quickly and efficiently. [top]

Global Workflow Efficiency: The Praedea DEP and its text-mining models can be easily customized into existing work flow and processes on a global basis. [top]

All Document Types: Documents are inserted and organized into the Praedea DEP for data extraction from virtually any source including SEC filings, FTP sites and internal document management systems. [top]

Consistent and Accurate Data: Automated data collection utilizing the Preadea DEP eliminates the inconsistencies and inaccuracies associated with much of the data currently provided in the marketplace. The Praedea DEP’s text-mining models are continually optimized for best performance. To further enhance accuracy, the Praedea DEP’s text-mining models also apply “company-specific memory,” the ability to recall how specific companies report information. [top]

Enriched Data: Unlike data you may receive from other sources, the Praedea DEP provides you with the ability to create/utilize “enriched data” (i.e., with audit-ability, commentary and custom calculations) to enhance your end-user applications. [top]

Improved Quality Control: Praedea’s data extraction process provides for efficient quality control. Data validation occurs based on your custom quality control rules and error-checking protocols are run with minimal human interaction; manual overwrites of the auto-extracted data are possible, if desired. As such manual overwrites occur, the text-mining models are capable of employing a self-learning refinement process to incorporate such alterations in future data extraction processes. [top]

Enhanced Data Transparency: With data extracted by the Praedea DEP, highly-paid professionals are no longer required to spend valuable time manually verifying the accuracy of data. Source Links, embedded upon extraction, provide hyperlinks from any output format (including Microsoft® Excel) to precise locations in underlying source documents, facilitating efficient quality control and value-added end-user applications. [top]

Broad Compatibility: Built on open standards, the Praedea DEP is broadly compatible with most server and database platforms and other software applications. Extracted data is deposited into existing database schemas or delivered as XML or XBRL files for easy access and utilization. End-user applications such as Microsoft® Excel, Microsoft® Word, Lotus Notes® or other custom analytic applications may be populated with the extracted data. [top]

Immediate Implementation: Basic installation of and on-site training regarding the Praedea DEP are typically completed in only one day with minimal engineering resources. No prior programming or text-mining experience is required. In most cases, initial text-mining term models are functional within days, with full automation-assisted production occurring within a month. [top]