Automated Pattern Estimation For Classification Of Consumer Perception On Green Banking

Dr. Venkateswarlu Chandu; Nkosingiphile Kunene; Sarah Motika; Peace Andrew John; Regina Banda

doi:10.69996/

Authors

Dr. Venkateswarlu Chandu Author
Nkosingiphile Kunene Author
Sarah Motika Author
Peace Andrew John Author
Regina Banda Author

DOI:

https://doi.org/10.69996/

Keywords:

Recommendation System, Pattern Estimation, Classification, Green Marketing, Automated model.

Abstract

This study investigates the landscape of green banking, focusing on its purpose, objectives, strategies, challenges, and consumer perceptions regarding various banking activities. The primary purpose is to evaluate how green banking initiatives can foster sustainable financial practices while addressing environmental concerns. The study outlines strategies employed by financial institutions, such as sustainable lending practices, green investment funds, and digital banking solutions that minimize environmental impact. However, several challenges persist, including a lack of awareness among consumers, high initial costs, and regulatory complexities, which hinder the widespread adoption of green banking practices. This paper explores the use of Pattern Intelligence in predicting consumer adoption of green marketing and eco-friendly products through the development of the Automated Pattern Identification Classification – Green Marketing (APIC-GM) model. The study examines the impact of key consumer characteristics such as environmental awareness, income level, and past eco-friendly behavior on their likelihood to adopt sustainable products. By leveraging machine learning techniques and data-driven approaches, we estimate consumer adoption probabilities and categorize them into distinct patterns, ranging from very low to very high adoption likelihoodUsing a dataset of 10 consumers, the model calculates predicted adoption probabilities, revealing significant patterns: consumers with high environmental awareness, high income, and a history of eco-friendly purchases have adoption probabilities ranging from 0.78 to 0.95 (e.g., Consumer ID 001 with a probability of 0.92 and Consumer ID 008 with 0.95), while those with low awareness and income levels show much lower probabilities, between 0.25 and 0.35 (e.g., Consumer ID 006 with 0.25 and Consumer ID 003 with 0.28). The model categorizes consumers into high, medium, and very low adoption groups, with Consumer ID 004 (0.78) and Consumer ID 005 (0.80) falling into the high adoption category. These insights offer actionable recommendations for green marketing strategies, helping businesses focus on high-potential customers while tailoring efforts for less responsive segments.

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