Artificial Intelligence in Banking Internal Demand Management Systems: The Example of Vakıf Participation Bank

Abstract

The development of artificial intelligence and technology has accelerated the transformation of internal processes in the banking sector. In particular, Natural Language Processing (NLP) technology provides time and cost savings by automating processes such as data entry, querying, and reporting. While NLP-based systems increase customer satisfaction by understanding customer demands and providing appropriate responses quickly, they also increase operational efficiency. Classification algorithms, which are frequently used together with NLP technology, analyze text data and assign them to certain categories or classes, creating a powerful combination for the processing and analysis of text-based data. Vakıf Participation Demand Management System R&D Project has developed an NLP and classification model to be used in its internal processes. With the developed model, it was aimed to eliminate the problems encountered in workflow processes and increase efficiency by developing a language understanding model using the records of requests (demand management system) kept within Vakıf Participation and frequently used in operational processes. During this study, existing data containing in-house requests were subjected to pre-processing, and model training studies were carried out with these data. As a result of the developments, a model with 75% accuracy was developed and improvement efforts on the model continue. Thanks to the developed model, aims to shorten the response time for requests in the demand management system, reduce operational burdens, and increase internal customer satisfaction. It is planned to use the developed model in other banking internal processes as well.