Early Risk Identification By Customer Consumer Profile Enrichment


Early Risk Identification for default behaviour in financial products, such as loans– cards/credits, can help lenders. With Segura, they will be able to utilize customer consumer profile identification based on income, psychographics, and product purchase behaviour patterns in all relevant categories.

Segura helps in customer risk identification.

The Situation

Banks/Lenders often have negative PIN codes based on historical delinquency experience, where they may not lend.
If the applicant bears the same address as someone who has been a defaulter for either loan repayment or credit card dues, the chances of loan rejection increase.


Two applicants from the same area, PIN code 211006 Allahabad but having different incomes.

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The Problem

PIN code territories are too divisive in the profile of residing population to be judged as ‘one’ type of loan behaviour.

Today one can observe even different loan behaviour among ‘traditional’ negative PIN codes.

Typical PIN code ‘negative (sub-)areas, such as high crime, and difficult collection pockets, will be used by all players in the industry, and alone will not yield a competitive cutting edge.

The KENTRIX Solution

Enhancement of applications by monthly household-level income based on residential building precise consumer data.

Add geospatial credit worthiness scores from traditional negative rated areas.

Breaking PIN code territories into homogeneous residential population profile pockets.



Monthly household-level income score
customer SEC classification customer
lifestyle affinity segmentation score.


Finding the nearest branch/service station for the customer as per the custom distance criteria implemented custom distance criteria can be set upper regio-type based (Differentiated Urban Classes/Rural) city limits/custom distances for individual product lines.


A valuable mechanism for ‘New to Credit’ customer selection

  • Complements bureau score through overlay heat maps on delinquency profiles, customer behaviour, and income levels.
  • Helps in a more robust credit assessment resulting in a more accurate approval/rejection percentage.
  • Identifies negative areas and green zones within the same PIN code for higher customer reach and sales force productivity.
  • Aids credit team on micro market policy formulation based on geography to identify pre-qualified areas.
  • Define geo-limits (OGL) for various products.
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Identifying risk clusters of specific customer lifestyle-income segments to default in
credit repayment

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