Articles

Enhancing Customer Service in Banking with AI: Intent Classification Using Distilbert

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With the increasing demand for efficient and responsive customer service in the banking sector, artificial intelligence offers a promising solution. This paper presents a comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain. Utilizing a comprehensive dataset of banking service inquiries, we evaluate several machine learning approaches, including Naive Bayes, Logistic Regression, Support Vector Machine with Linear Kernel, Random Forest, XGBoost, and the transformer-based DistilBERT model. The models are assessed based on their accuracy, precision, recall, and F1 score metrics. Our findings indicate that DistilBERT, with its distilled architecture, not only outstrips traditional models but also demonstrates exceptional performance with an accuracy and F1 score exceeding 92%. The paper delves into the advantages of employing such an efficient and powerful model in real-time customer service settings, suggesting that DistilBERT offers a substantial enhancement over conventional methods. By providing detailed insights into the model’s capabilities, we underscore the transformative impact of employing advanced AI in the financial industry to elevate customer service standards, streamline operational efficiency, and harness the power of state-of-the-art technology for improved client interactions. The results showcased in this study are indicative of the strides being made in AI applications for financial services and set a benchmark for future exploratory and practical endeavors in the field.

Enhancing Sustainable Banking Practices: Implementing the Besgi Framework to Indonesian Bank

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Climate change, a global issue largely caused by human activities, is now beginning to be addressed by the G20, including financial institutions. Indonesia, as part of the G20, is implementing a sustainable finance program to improve the financing, durability, and competitiveness of financial services institutions. This study evaluates the adoption of sustainable banking practices in Indonesia within the context of global climate change initiatives. Using the Banks’ Environmental, Social, Governance, and Indirect Impact (BESGI) framework, which provides a comprehensive assessment of banks’ ESG performance using the Multidimensional Synthesis of Indicators (MSI) aggregation method. The BESGI performance of 14 Indonesian banks from 2020-2022 was assessed, revealing varying results of fluctuating data with Mandiri scoring the highest in year 2021 and BTN the lowest in year 2020. The findings indicate a growing emphasis on sustainable finance within the Indonesian banking sector in terms of financing and investment. The BESGI Score has insignificant results on banks’ performance and stability. However, further research is essential to comprehend the implications of these practices on the performance and stability of banks.