[HTML][HTML] Explainable AI for operational research: A defining framework, methods, applications, and a research agenda
The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …
aiding decision-making, has become a critical requirement for many applications. For …
[HTML][HTML] Credit scoring methods: Latest trends and points to consider
A Markov, Z Seleznyova, V Lapshin - The Journal of Finance and Data …, 2022 - Elsevier
Credit risk is the most significant risk by impact for any bank and financial institution.
Accurate credit risk assessment affects an organisation's balance sheet and income …
Accurate credit risk assessment affects an organisation's balance sheet and income …
Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction
Customer retention campaigns increasingly rely on predictive analytics to identify potential
churners in a customer base. Traditionally, customer churn prediction was dependent on …
churners in a customer base. Traditionally, customer churn prediction was dependent on …
Advanced customer analytics: Strategic value through integration of relationship-oriented big data
As more firms adopt big data analytics to better understand their customers and differentiate
their offerings from competitors, it becomes increasingly difficult to generate strategic value …
their offerings from competitors, it becomes increasingly difficult to generate strategic value …
Profit-driven weighted classifier with interpretable ability for customer churn prediction
Customer churn prediction methods aim to identify customers with the highest probability of
attrition, improve the effectiveness of customer retention campaigns, and maximize profits …
attrition, improve the effectiveness of customer retention campaigns, and maximize profits …
The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a
multitude of sophisticated classification techniques have been developed to improve the …
multitude of sophisticated classification techniques have been developed to improve the …
Two-stage consumer credit risk modelling using heterogeneous ensemble learning
M Papouskova, P Hajek - Decision support systems, 2019 - Elsevier
Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions
to support decision-making on granting loans. To model the overall credit risk of a consumer …
to support decision-making on granting loans. To model the overall credit risk of a consumer …
Incorporating textual information in customer churn prediction models based on a convolutional neural network
This study investigates the value added by incorporating textual data into customer churn
prediction (CCP) models. It extends the previous literature by benchmarking convolutional …
prediction (CCP) models. It extends the previous literature by benchmarking convolutional …
A survey on churn analysis in various business domains
In this paper, we present churn prediction techniques that have been released so far. Churn
prediction is used in the fields of Internet services, games, insurance, and management …
prediction is used in the fields of Internet services, games, insurance, and management …
Dynamic behavior based churn prediction in mobile telecom
Customer churn is one of the most challenging problems that affects revenue and customer
base in mobile telecom operators. The success of retention campaigns depends not only on …
base in mobile telecom operators. The success of retention campaigns depends not only on …