Trans Vision

Abstract

In this ambitious financial landscape, customer satisfaction is the sharp-edged dagger that cuts through the intense competition that credit card companies face whose sole aim is to retain and expand their user base. This paper – “TransVision” presents an information-centric substructure that leverages data analytics[2] and machine learning techniques to optimize end to end transaction journey for credit card users of a hypothetical financial organization. TransVision provides insights into user behaviours by analyzing transaction data across key sectors, enabling targeted customer segmentation based on socio-economic attributes. Through robust fraud detection algorithms, it safeguards customer interactions by identifying anomalous patterns, thereby reducing transaction risks. Additionally, sentiment analysis[7] of customer feedback provides actionable insights into user satisfaction, enabling continuous improvement of the customer experience. Our results demonstrate that TransVision not only enhances transactional security but also improves the company's ability to proactively address customer needs, fostering both customer retention and acquisition.