Towards intelligent feature engineering for risk-based customer segmentation in banking
| Autor: | Damon Pezaro, Quan Z. Sheng, Sam Khadivizand, Amin Beheshti, Steven Wood, Fariborz Sobhanmanesh, Elias Istanbouli |
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| Rok vydání: | 2020 |
| Předmět: |
Feature engineering
Process management Computer science Process (engineering) Business process media_common.quotation_subject 02 engineering and technology Domain (software engineering) Market segmentation 020204 information systems 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Quality (business) Raw data media_common |
| Zdroj: | MoMM |
| DOI: | 10.1145/3428690.3429172 |
| Popis: | Business Processes, i.e., a set of coordinated tasks and activities to achieve a business goal, and their continuous improvements are key to the operation of any organization. In banking, business processes are increasingly dynamic as various technologies have made dynamic processes more prevalent. For example, customer segmentation, i.e., the process of grouping related customers based on common activities and behaviors, could be a data-driven and knowledge-intensive process. In this paper, we present an intelligent data-driven pipeline composed of a set of processing elements to move customers' data from one system to another, transforming the data into the contextualized data and knowledge along the way. The goal is to present a novel intelligent customer segmentation process which automates the feature engineering, i.e., the process of using (banking) domain knowledge to extract features from raw data via data mining techniques, in the banking domain. We adopt a typical scenario for analyzing customer transaction records, to highlight how the presented approach can significantly improve the quality of risk-based customer segmentation in the absence of feature engineering. |
| Databáze: | OpenAIRE |
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