Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00017
Celine Ganar, Patrick Hosein
The internet has had a significant impact on financial institutions by allowing customers to access many bank services virtually thus creating a very competitive environment. Therefore, efficient customer segmentation is a key objective for achieving more profitable market penetration. We propose a hybrid model that predicts a financial institution client’s propensity to transition to an online banking platform. In this research, we utilized a hybrid approach where the first stage is Transaction Cluster Analysis where Recency, Frequency and Monetary (RFM) segmentation and K-Means cluster analysis were performed to detect the most loyal market segments. Analytic Hierarchy Process (AHP) was used to deduce the weightings of each cluster which aided in calculating the Customer Lifetime Value (CLV) of each cluster. Then two clustering algorithms, K-Modes and K-Means, were utilized on the clients’ demographic features. In the final stage, we performed experiments that compared two supervised learning algorithms, Decision Tree and Extreme Gradient Boosted (XGBoost), to predict online transition behaviour. Our results showed that K-Modes clustering algorithm and XGBoost classification model yielded the best test accuracy of 96.1%. Our results illustrate our claims by showing that the bank can attract more customers, maintain its customer base, and keep their important customers satisfied.
{"title":"Customer Segmentation for improving Marketing Campaigns in the Banking Industry","authors":"Celine Ganar, Patrick Hosein","doi":"10.1109/ACMLC58173.2022.00017","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00017","url":null,"abstract":"The internet has had a significant impact on financial institutions by allowing customers to access many bank services virtually thus creating a very competitive environment. Therefore, efficient customer segmentation is a key objective for achieving more profitable market penetration. We propose a hybrid model that predicts a financial institution client’s propensity to transition to an online banking platform. In this research, we utilized a hybrid approach where the first stage is Transaction Cluster Analysis where Recency, Frequency and Monetary (RFM) segmentation and K-Means cluster analysis were performed to detect the most loyal market segments. Analytic Hierarchy Process (AHP) was used to deduce the weightings of each cluster which aided in calculating the Customer Lifetime Value (CLV) of each cluster. Then two clustering algorithms, K-Modes and K-Means, were utilized on the clients’ demographic features. In the final stage, we performed experiments that compared two supervised learning algorithms, Decision Tree and Extreme Gradient Boosted (XGBoost), to predict online transition behaviour. Our results showed that K-Modes clustering algorithm and XGBoost classification model yielded the best test accuracy of 96.1%. Our results illustrate our claims by showing that the bank can attract more customers, maintain its customer base, and keep their important customers satisfied.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/acmlc58173.2022.00028
Shuang Huang, F. Li, Xu Cao, Heng-chu Fang
In solving the issue of efficiency in global path planning of UUV underwater multi-task points, and reduce energy and time consumption during task execution, a hybrid GA-AFSA algorithm was constructed based on the Genetic and Artificial Fish Swarm Algorithm. Maximize the advantages of genetic algorithm global rapid convergence and artificial fish swarm algorithm with high solution accuracy, to solve the initial population generation and optimal path solution problems in UUV path planning, then a comparative experiment between the genetic and the GA-AFSA algorithm is put into effect. The experimental results show that the GA-AFSA algorithm takes into account both the global search ability and the fast search performance, compared with the improved GA algorithm, its best iteration time is reduced by 41%, the optimal path length is reduced by 16%, it has the advantages of fast optimal solution rate and shorter optimal path solution, and has strong efficiency and practicability.
{"title":"UUV Path Planning Based on GA-AFSA Algorithm","authors":"Shuang Huang, F. Li, Xu Cao, Heng-chu Fang","doi":"10.1109/acmlc58173.2022.00028","DOIUrl":"https://doi.org/10.1109/acmlc58173.2022.00028","url":null,"abstract":"In solving the issue of efficiency in global path planning of UUV underwater multi-task points, and reduce energy and time consumption during task execution, a hybrid GA-AFSA algorithm was constructed based on the Genetic and Artificial Fish Swarm Algorithm. Maximize the advantages of genetic algorithm global rapid convergence and artificial fish swarm algorithm with high solution accuracy, to solve the initial population generation and optimal path solution problems in UUV path planning, then a comparative experiment between the genetic and the GA-AFSA algorithm is put into effect. The experimental results show that the GA-AFSA algorithm takes into account both the global search ability and the fast search performance, compared with the improved GA algorithm, its best iteration time is reduced by 41%, the optimal path length is reduced by 16%, it has the advantages of fast optimal solution rate and shorter optimal path solution, and has strong efficiency and practicability.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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