Enriching Customer Relationship Management with New Attributes and Machine Learning Methods in Banking Sector

Year 2019, Issue: 16, 382 - 394, 31.08.2019

Faruk Bulut

https://doi.org/10.31590/ejosat.520295

Cited By: 1

Abstract

A bank may have difficulty in acquiring detailed personal information from their clients. Over time, however, customers share a variety of information about their behavioral characteristics and life habits with the bank. This information for people can be hundreds or even thousands. It can reach billions of information in certain categories of all customers of a bank with millions of individual and corporate clients. Large organizations with millions of customers are generally referred to as “rich in data rich and lack in knowledge” in the field of Information Technology. This unprocessed data might contain many valuable information that is hidden from customers and the market. There might be a lot of valuable hidden information about both customers and market. Data Science essentially aims to extract this confidential information from raw data. In this study, suggestions have been made about the different vital and behavioral habits of institutional and individual customers which can be converted into numerical data. With this data, the structure of CRM (Customer Relationship Management), an artificial intelligence-based system which aims to better analyze the market, better customer segmentation, and offer services and products with less labor to the right customers, is recommended. In this project, it is aimed to propose an original artificial support model by using algorithms in Data Mining disciplines. With the identification of different data mining applications and processes, we have tried to give a new perspective to the field of banking and customer relationship management. 

Keywords

Customer recognition, Banking sector, CRM, Machine Learning

Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi

Abstract

Bir
banka müşterilerinden kendilerini tanımlayıcı detaylı kişisel verileri
kolaylıkla alamayabilir. Fakat müşteriler zaman içerisinde, davranışsal
özellikleri ve yaşamsal alışkanlıkları ile ilgili birçok bilgiyi farkında
olmadan banka ile paylaşırlar. Bu bilgiler sadece bir birey için yüzlerce,
hatta binlerce olabilir. Milyonlarca bireysel ve kurumsal müşterisi olan bir
bankanın tüm müşterilerine ait belirli kategorilerdeki bilgileri milyarlara
ulaşabilir. Milyonlarca müşterisi olan büyük kuruluşlar genel olarak Bilgi
Teknolojileri alanında “veri zengini ve bilgi yoksunu” olarak
nitelendirilirler. Bu işlenmemiş veriler içerisine müşteriler ve piyasa
hakkında gizlenmiş birbirinden farklı oldukça fazla değerli bilgiler
bulunabilmektedir. Veri Bilimi esasen bu gizli bilgileri ham veriler
içerisinden çıkarmayı amaçlar. Bu çalışmada, kurumsal ve bireysel müşterilerin
sayısal verilere dönüştürülebilen farklı yaşamsal ve davranışsal
alışkanlıklarının neler olabileceği ile ilgili önerilerde bulunulmuştur. Bu
veriler ile piyasanın daha iyi analiz edilmesi, müşteri segmentasyonunun daha
iyi yapılması, hizmet ve ürünlerin doğru müşteri kitlelerine daha az emek ile
satılmasını amaçlayan yapay zekâ tabanlı bir sunulan Müşteri İlişkileri
Yönetimi yani CRM (Customer Relationship Management) uygulamasının yapısı
önerilmektedir. Projede bilişim alanındaki Veri Madenciliği disiplinlerinde
bulunan algoritmalar kullanılarak özgün bir yapay destek modelinin önerilmesi
amaçlanmaktadır. Farklı veri madenciliği uygulamaları ve süreçlerin
tanımlanmasıyla bankacılık ve müşteri ilişkileri yönetimi alanına yeni bir
perspektif kazandırılmaya çalışılmıştır. 

Keywords

Müşteri Tanıma, Bankacılık Sektörü, CRM, Makine Öğrenmesi

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