Process Mining for Optimization of a Loan Approval Process in a Financial Institution

Varas Gándara, Eduardo (2017). Process Mining for Optimization of a Loan Approval Process in a Financial Institution. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S.I. Telecomunicación (UPM), Madrid.

Description

Title: Process Mining for Optimization of a Loan Approval Process in a Financial Institution
Author/s:
  • Varas Gándara, Eduardo
Contributor/s:
  • Iglesias Fernández, Carlos Ángel
Item Type: Final Project
Degree: Grado en Ingeniería de Tecnologías y Servicios de Telecomunicación
Date: 2017
Subjects:
Freetext Keywords: Process Mining, Data Mining, Python, Disco, Fintech, Process, Analysis
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Financial institutions are experiencing drastic changes after the global financial crisis of 2008. On the one hand, financial institutions need to quickly adapt to new compliance regulations that require adapting their internal processes. On the other hand, Fintechs are changing the traditional banking rules by introducing innovative financial processes. Therefore, financial institutions need to be able to improve their operational inefficiencies, and represents a business case for the application of process mining techniques. In this final degree project a real case is faced through different process and data mining techniques. The data used was provided in the BPIC 2017. This challenge proposes a real use case where a Dutch financial institution provides event logs of the loan approval process, with 1.202.267 events pertaining to 31.509 applications. For the approach, we leverage the Disco process mining tool with Python-based data analysis and visualization tools such as Pandas in order to combine different granularity inspection techniques to provide answers to the given questions. In particular, we focus on the main requests from the BPIC 2017 challenge, which are: throughput times per part of the process, influence on the frequency of incompleteness to the final outcome and the frequency of customers asking for more than one offer. Our approach has consisted in identifying the process phases and analyzing each question by phase and then globally. Finally, we discuss concluding remarks and future work.

More information

Item ID: 47545
DC Identifier: http://oa.upm.es/47545/
OAI Identifier: oai:oa.upm.es:47545
Deposited by: Biblioteca ETSI Telecomunicación
Deposited on: 28 Aug 2017 05:50
Last Modified: 28 Aug 2017 05:52
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