Difference between revisions of "Credit Data"
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== Types of Credit Data == | == Types of Credit Data == | ||
| − | There is a wide variety of possible credit data sources. Two | + | There is a wide variety of possible credit data sources. Two important classifications are by data content (the type of information captured by credit data) and by borrower type. |
=== Classification by Data Content === | === Classification by Data Content === | ||
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* Legal History Data: Any judicial track record of [[Credit History]] collected from courts | * Legal History Data: Any judicial track record of [[Credit History]] collected from courts | ||
* Repayment Data: Actual track record of credit performance | * Repayment Data: Actual track record of credit performance | ||
| − | * Alternative Credit Data: Increased | + | * Alternative Credit Data: Increased digitization means that new sources of credit-relevant information become available. This is usually denoted as [[Alternative Credit Data]] |
=== Segmentation by Borrower Type === | === Segmentation by Borrower Type === | ||
Latest revision as of 19:15, 6 March 2023
Contents
Definition
Credit Data is any well defined dataset that has direct applications in the assessment of Credit Risk of an individual or an organization, or more generally allows the application of data driven Credit Portfolio Management policies.
Types of Credit Data
There is a wide variety of possible credit data sources. Two important classifications are by data content (the type of information captured by credit data) and by borrower type.
Classification by Data Content
- Identification Data: e.g., company name, business code, address of registered office, legal form, date of establishment
- Accounting and Financial Data: e.g., company accounting records, an individual's income or assets
- Qualitative Data: E.g from review of management structures, interviews etc
- Behavioral Data: Any soft (non-legal) indicators of behaviors / attitudes towards exercising options
- Legal History Data: Any judicial track record of Credit History collected from courts
- Repayment Data: Actual track record of credit performance
- Alternative Credit Data: Increased digitization means that new sources of credit-relevant information become available. This is usually denoted as Alternative Credit Data
Segmentation by Borrower Type
Credit Data are naturally segmented along the Debtor categories and Lending products such borrowers may be involved with. The most important subcategories are:
- Consumer Credit Data which are typically also subdivided by lending product
- Corporate Credit Data which are typically not subdivided by product
- Sovereign / sovereign debt analysis (Sovereign Risk) also involves substantial data requirements. Yet the relative paucity of credit events, the public availability of much government data (See Open Risk Data) and the complexity of these entities means that the related data form a category quite distinct from the other two.
Credit Data Formats and Templates
There are currently no public credit data formats or standards for the transmission of credit data. Generally, credit data are transmitted using ad-hoc formats primarily driven by regulatory requirements. Some notable examples:
Credit Data Sources
- For application scoring, the client provides information in the various application forms
- Credit data collection is generally a commercial activity with a variety of providers in different countries
Issues and Challenges
- Data Privacy
- Data Quality
- Accessibility of data (formats, API's) may vary greatly
