Modeling mobile apps user behavior using Bayesian networks

Dharmasena, Isuru; Domaratzki, Michael; Muthukumarana, Saman

doi:10.1007/s41870-021-00699-7

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…The hybrid approaches combine the previous two approaches. Representative algorithms are H 2 PC (Chen et al ., 2021), RSMAX2 (Constantinou, 2020) and the Max-Min Hill Climbing algorithm (MMHC) (Dharmasena et al ., 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

A decision support method for credit risk based on the dynamic Bayesian network

Lu,

Wu,

Dong

et al. 2023

IMDS

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PurposeCredit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.Design/methodology/approachFirst, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.FindingsCompared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.Originality/valueThe authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

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Section: Literature Reviewmentioning

confidence: 99%