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Develop a consumer credit default model and dashboard analytics predicting prepayment, default and loss severity for individual consumer loans.
Create, document and support PD (Probability of Default), LGD (Loss Given Default), Default, Prepayment and Loss Curves (DPLC), and EAD (Exposure at Default) models to identify high-risk delinquencies. Utilize sophisticated consumer lending statistical techniques and risk analytics including machine learning (Neutral network, decision tree, discriminant analysis, SVM, k-nearest neighbor, clustering), survival analysis (cox proportional / censored quantile regression), regression, and bayesian analysis
• Forecast portfolio loss rates through credit roll rates and time series analytics.
• Develop highly segmented risk-based pricing models using lifetime credit loss rates.
• Assess credit risk of consumer loan products through multivariate logistic regression modeling of credit risk factors and payment trends.
• Stress cash flows under mult...
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