Multi–objective Evolutionary Algorithms for the Risk–return Trade–off in Bank Loan Management

By Amitabha Mukerjee, Rita Biswas, Kalyanmoy Deb, Amrita Mathur. 2002

101 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Isabel Valera
2. Bernhard Schölkopf
3. Dominik Janzing
4. Jan Peters

In Finance

1. Bernhard Schölkopf

In Management Science and Operations Research

1. Isabel Valera
2. Gilles Barthe
3. Tai Le Quy
4. Bernhard Schölkopf
5. Eirini Ntoutsi
6. Vasileios Iosifidis
7. Arjun Roy

Citations in the corpus

• A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “Examples such as the above are prevalent not only in finance [20, 133] but also in justice (e.g., pretrial bail) [10], healthcare (e.g., prescription of life-altering medication) [24, 27, 72], and other settings (e.g., hiring) [43, 134, 160] broadly classified as consequential decision-making…”
• A literature review on the application of evolutionary computing to credit scoring (2013, methodology): “Mukerjee et al (2002) present a novel tool for the application of a multi-objective genetic algorithm, namely NSGA-II (Deb, 2001), to a bank credit management problem, with the aim of giving an appropriate trade-off between the multiple objectives of return maximization and risk minimization.”
• A survey on datasets for fairness‐aware machine learning (2021, background): “…businesses, governments and other organizations to improve their operational quality and assist in decision-making in areas such as loan approval (Mukerjee, Biswas, Deb, & Mathur, 2002), re-cruiting (Faliagka, Ramantas, Tsakalidis, & Tzimas, 2012), school admission (Moore, 1998), risk prediction…”