Work detail

The future of credit scoring modelling using advanced techniques

Author: Mgr. Jolana Čermáková
Year: 2020 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 86
Awards and prizes:
Link:
Abstract: Machine learning is becoming a part of everyday life and has an indisputable
impact across large array of industries. In the financial industry, this impact
lies particularly in predictive modelling. The goal of this thesis is to describe
the basic principles of artificial intelligence and its subset, machine learning.
The most widely used machine learning techniques are outlined both in a
theoretical and a practical way. As a result, four models were assembled
within the thesis. Results and limitations of each model were discussed and
these models were also mutually compared based on their individual performance. The evaluation was executed on a real world dataset, provided
by Home Credit company. Final performance of machine learning methods,
measured by the KS and GINI metrics, was either very comparable or even
worse than the performance of a traditional logistic regression. Still, the
problem may lie in an insucient dataset, in the improper data preparation, or in inappropriately used algorithms, not necessarily in the models
themselves.

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Deloitte

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CRIF
McKinsey
Patria Finance