Utilize autoencoders for anomaly detection and customer credit risk evaluation
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Updated
Mar 30, 2021 - HTML
Utilize autoencoders for anomaly detection and customer credit risk evaluation
This is a Credit Analysis project developed by Felipe Solares da Silva and is part of his professional portfolio.
Comparison of models for credit risk purposes - logistic regression vs random forest. Empirical research.
Course draft - bank corporate lending
A credit score classification is a system used by lenders and financial institutions to assess an individual's creditworthiness. A credit score is a numerical representation of a borrower's credit history, ranging from 300 to 850. The higher the score, the better the borrower's creditworthiness.
By the data set from 'Give Me Some Credit' (2012), this work is to use it to illustrate some useful techniques in Credit Scoring Modelling, namely: GLM, SMOTE, CARET, CHAID, and MOB.
A short course on survival analysis applied to the financial industry
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
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