{"id":1018,"date":"2015-07-17T10:30:54","date_gmt":"2015-07-17T09:30:54","guid":{"rendered":"http:\/\/dotnetsql.info\/net-sql\/?p=1018"},"modified":"2015-07-31T16:17:00","modified_gmt":"2015-07-31T15:17:00","slug":"implementing-a-credit-risk-data-warehouse","status":"publish","type":"post","link":"http:\/\/dotnetsql.info\/net-sql\/index.php\/implementing-a-credit-risk-data-warehouse\/","title":{"rendered":"How to implement a Credit Risk Data Warehouse – Part 1"},"content":{"rendered":"

In the past years I’ve worked as a hands-on architect on the design and implementation of various data migration, data warehouse and business intelligence systems in various industries like Insurance, Retail, Food & Bev’s, Pharma & Investment Banking. Out of all nothing quite stands out like the complexity of building a Credit Risk Data Warehouse & Reporting system for the compliance with the Basel Regulatory requirements.\"\"
\nThere is a specific satisfaction delivered by this kind of projects due to the high amount of upstream data generation systems and the complexity of regulatory reporting logic that has to sit on top of the business and technical flows.
\nBasel II required changes to a bank\u2019s public disclosures and the regulatory review of their capital along with key requirements for credit data storage and management including the maintenance of a cradle-to-grave history of obligors.
\nThe banks have to use consistent, reconciled risk data across origination and servicing, risk management, and financial reporting systems.<\/p>\n

Usually the portfolio management division is managing the bank\u2019s portfolio of credit risk exposure through modern portfolio theory and risk management techniques.<\/p>\n

However, the way that they are collecting credit data from a number of systems and manual data feeds, is usually resulting in data inaccuracies.<\/p>\n

In addition, the divisions usually finds it hard to identify the various potential sources for credit risk data or associated data gaps.<\/p>\n

In order to have an easy journey to achieve this, the banks have to increase the overall enterprise focus on data architecture and to upgrade the data collection processes, storage and management capabilities.<\/p>\n

The Challenges of implementing a Credit Risk Data Warehouse<\/strong><\/p>\n

Similar to a regular implementation of a Data Warehouse and Business Intelligence reporting systems the first step required is an in-depth analysis of the business flows and data flows in the context of credit risk reporting along with a detailed assessment of the Basel data compliance and reporting requirements.<\/p>\n

This analysis represents the core document or the “bible” for the development and implementation of the data warehouse and is of critical importance that the resulted requirements and structures are formulated 100% from the business team’s perspective.<\/p>\n

Based on the experience and expertise of the implementation team and understanding of Basel II, the following items will have to be completed in the inception & planning stages of the project:<\/p>\n