Data Mining and Modeling use Case in Banking Industry

Simić

2020

Entropy

Due to telecommunications market saturation, it is very important for telco operators to always have fresh insights into their customer’s dynamics. In that regard, social network analytics and its application with graph theory can be very useful. In this paper we analyze a social network that is represented by a large telco network graph and perform clustering of its nodes by studying a broad set of metrics, e.g., node in/out degree, first and second order influence, eigenvector, authority and hub values. This paper demonstrates that it is possible to identify some important nodes in our social network (graph) that are vital regarding churn prediction. We show that if such a node leaves a monitored telco operator, customers that frequently interact with that specific node will be more prone to leave the monitored telco operator network as well; thus, by analyzing existing churn and previous call patterns, we proactively predict new customers that will probably churn. The churn prediction results are quantified by using top decile lift metrics. The proposed method is general enough to be readily adopted in any field where homophilic or friendship connections can be assumed as a potential churn driver.

Section: Discussionmentioning

confidence: 99%

Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Simić

2020

Entropy

“…Minat nasabah untuk dapat berlangganan deposito pada sebuah bank masih terbilang minim. Hal ini disebabkan karena penarikan uang deposito hanya dapat dilakukan pada waktu tertentu, padahal suku bunga deposito lebih tinggi dibandingkan tabungan biasa (Darmawan, 2017), sehingga deposito juga dapat dijadikan sebagai sarana alternatif dalam berinvestasi (Ula, 2018) Maka dari itu pemasaran langsung kepada nasabah digunakan sebagai strategi pihak perbankan (Kosti, Miloš, Simi, & Kosti, 2018).…”

Section: Pendahuluanunclassified

Analisis Algoritma KNN Berbasis Feature Selection untuk Memprediksi Nasabah Pengguna Deposito Melalui Pemasaran Langsung

Rahmawati

Yulianti

Yuliani

et al. 2020

SWABUMI

Sebuah bank menggunakan teknik pemasaran langsung dalam menargetkan segmen nasabah dengan cara menghubungi nasabah tersebut untuk memenuhi tujuan tertentu. Setelah menghubungi nasabah, bank mendapatkan informasi apakah nasabah tersebut sudah berlangganan produk yang ditawarkan oleh bank atau belum. Salah satu produk yang ditawarkan oleh bank antara lain yaitu deposito. Dari banyaknya informasi nasabah yang dikumpulkan, bank mampu menawarkan produk dan layanan kepada nasabah. Kemampuan tersebut dapat menggunakan teknologi data mining, seperti tujuan dibuatnya penelitian ini yaitu memprediksi nasabah yang berlangganan deposito dengan algoritma K-Nearest Neighbor (KNN) dan feature selection yang diproses menggunakan tools Anaconda dan bahasa pemrograman python. Dari hasil penelitian yang diperoleh, akurasi dari penggunaan algoritma K-Nearest Neighbor (KNN) sebesar 74,37% dengan nilai K=9, sedangkan akurasi algoritma klasifikasi K-Nearest Neighbor dengan menggunakan feature selection sebesar 89,72% dengan nilai K=3, sehingga didapat selisih peningkatan akurasi sebesar 15,35%.

Adv. sci. technol. eng. syst. j.

“…The banking industry is very aggressive. To optimizing its information for convincingly winning the competition is very imperative to be conducted by banking institution [2]. The marketing department has an important role to make strategy in the business competition of other companies.…”

Section: Introductionmentioning

confidence: 99%

Balance as One of the Attributes in the Customer Segmentation Analysis Method: Systematic Literature Review

Firdaus¹,

Utama²

2020

The banking industry is very competitive. To utilize the information, they have in order to be a competitive advantage winner is reasonably very crucial for the company. At present, the company does not only focus on the company's strategy that prioritizes products (e.g. product or service oriented), however also necessitates to focus on the company's strategy in prioritizing customers. Customer segmentation, its attributes, and the appropriate analysis method are going to get accurate data segmentation results, so that it is able to be used as a reference by the company and as a basis for determining its products' marketing strategies. This systematic literature review discusses the types of attributes operated, including customer balance attributes, whether or not they can be included in segmentation. In addition, it also discusses what popular analytical methods are widely used in the customer segmentation process. Literature searching in the digital library resulted in a total are 592,363, 1,361, and 21 papers respectively in the first, second, and third stage. 10 papers found finally in the final stage that were considered capable of answering research questions. Based on 10 papers selected, it can be concluded that customer balances can be functioned scientifically as one of attributes for segmentation use. The popular analytic methods operated for customer segmentation are recency, frequency, monetary (RFM) model (4 times appeared), K-Means algorithm (6 times occurred), and C-Means (2 times emerged).