Digital Banking Chatbots related MCDM Problem by TODIM Strategy in Pentagonal Neutrosophic Arena

Abstract

Chatbots is an amalgamation of two words, Chats and bots. The meaning of Chat is to talk and bots are robots. Thus specifically Chat bots are talkative robots. The machine which is planned and controlled by a computer is called Robot. The banking business has become 247, and hence there arises the requirement to provide 247, 365 days a year services to customers. By any means, this is impossible for any human being, and for this reason, the machine was required to be invented. Even the multifaceted problems can be solved by robots with the help of external human force or with entrenched system rule. Humans are replaced by robots in doing tough, dangerous, and recurring activities that could not be performed by the former because of their restrictions. In banks, Robots Process Automation can reduce operational errors, reduce the cost of human capital, and has the potential to do multitask 247 without fatigue and 100% success rate. The fundamental of this paper is to appreciate the necessity of robotics process automation in the West Bengal banking industry. A pentagonal neutrosophic number (PNN) based Tomada de decisao interativa e multicritévio (TODIM) method multi-criteria decision making (MCDM) problem is included here to uncover the most vital chatbots factors these days. This study would assist in gaining insights on the consequence of the beginning of RPA in the Banking sector and its overall impact on Customer Relationship Management.

Enhanced Performance Evaluation Through Neutrosophic Data Envelopment Analysis Leveraging Pentagonal Neutrosophic Numbers

“…One such extension, the pentagonal neutrosophic numbers, demonstrates a marked increase in nuance and flexibility in portraying uncertain or imprecise information compared to other neutrosophic numbers. The use of five parameters enables complex degrees of truth, falsity, and indeterminacy to be expressed more comprehensively [39][40][41][42]. As a result, decision-makers are better equipped to model uncertainty or vagueness with greater accuracy, potentially leading to more informed decision-making and improved problem-solving outcomes [43][44][45][46].…”