| Irevna's risk analytics team, has experience, building empirical
models (scorecards), strategies and performing data-driven analysis in the risk
domain for consumer banking/finance businesses, across various stages of the
consumer's credit life cycle - Product Planning, Credit Acquisition, Account
Maintenance, Collections, and Account-Write-Offs.
Our risk analytics team has experience in working with large
databases (application data, account performance data with on-us information,
account behavior data with on-us information, bureau data, etc). The
statistical tools that we generally use are:
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Logistic regression
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Linear regression
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Principal component analysis
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Factor analysis
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Cluster analysis
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Decision trees (CHAID , CART)
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Discriminant analysis
Our main analytical offerings include:
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Application score development
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Behavior score development
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Fraud score/strategy development
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Collection and recovery score development
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Strategy development at various phases of consumer credit cycle.
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