01 December 2015


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Data Management 101

A community blog about data and how to manage it

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12 July 2010  |  9141 views  |  0

During the normal course of business, financial institutions glean valuable data about their customers and the markets in which they operate. This data includes customer demographics, historical and current banking trends, historical and current financial statements, business activities and plans, market trends and norms, as well as probable risk indicators and more. When a financial institution does not have a centralized data repository, data is saved in several independent data silos that are difficult if not impossible to integrate making it quite a challenge to obtain a complete picture of a counterparty, exposure or portfolio.  In many cases the data simply isn’t saved at all. In such conditions, important counterparty data is lost or overlooked, resulting in incomplete analyses and reduced quality for no reason other than the lack of a central system to retain relevant information.

Consolidating data into a single information source gives your financial institution a significant edge. The financial institution will be able to obtain an up-to-date, accurate depiction of individual counterparties as well as portfolios from a single aggregated data source, without wondering if additional data resides elsewhere. The Lean & Mean financial institution will also be able to create advanced analytical models with confidence that the data gleaned from the central repository is the most accurate and up-to-date data available.

The consolidated data repository solution will also make a huge contribution towards creating and maintaining risk scoring models.  Credit and borrower scoring has become a mandatory part of the lending process.  Maintaining a sound scoring system poses a serious challenge to banks, the greatest of which is perhaps the initial building and implementing of the scoring models.  Maintaining a data repository will assist bankers in selecting risk scoring methodologies most appropriate for the types of data that have been captured.  Furthermore, the more data captured, the more extensive the analyses may be.  Of course, thanks to the data repository, the data, analyses and risk ratings would persist historically while enabling periodic updates and revisions.


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