Conversational AI Chatbots in FinTech CRM: Impact on Customer Satisfaction and First-Contact Resolution Rates

DOI

10.2991/978-94-6239-772-9_24How to use a DOI?

Keywords

Conversational AI; Chatbot; FinTech; CRM; Customer Satisfaction; First-Contact Resolution; Large Language Models; Retrieval-Augmented Generation; Service Quality; Digital Banking; Natural Language Processing

Abstract

This research examines the causal effect of deploying Conversational Artificial Intelligence (CAI) chatbots in FinTech Customer Relationship Management (CRM) systems on two key metrics of service quality: customer satisfaction (CSAT) and First-Contact Resolution (FCR). Using operational data from 47 FinTech companies across six Asian and Southeast Asian markets, involving 3,842,917 customer service interactions over the past 30 months (January 2022 to June 2024), we apply a staggered difference-in-differences (DiD) design, which leverages staggered CAI deployment across firms and product lines, a regression discontinuity (RD) design around the minimum confidence score required for chatbots to provide an answer, and structural equation modelling to examine the direct and indirect effects of CAI capabilities on service outcomes. Our CAI taxonomy categorises deployed systems into three generations: rule-based chatbots (RBC), retrieval-augmented generation systems (RAG-CAI), and large language model-based agents (LLM-CAI). Findings show that LLM-CAI deployment raises CSAT (on a 100-point scale) by 14.7 points compared to pre-deployment levels (p < 0.001) and FCR by 22.3 percentage points (p < 0.001), but RBC deployment only has a marginal effect on CSAT (+2.1 points, p = 0.31) and a moderate effect on FCR (+8.4 percentage points, p < 0.01). We show that the effects of LLM-CAI deployment on CSAT are mediated by 71.4% by resolution speed, response personalisation and handling of complex queries. There is significant customer demographic diversity: Gen Z (18-27 years of age), and customers with high digital literacy have CSAT responses 31.2% higher than the sample mean, but older customers (60+) and users with low digital literacy have neutral or slightly negative CSAT responses to CAI, suggesting a risk of digital divide in strategies for full migration to CAI. Importantly, human escalation design - specifically, whether the system offers smooth human handoff - moderates the impact of CAI failure on CSAT: the CSAT penalty of failed chatbot experiences is 61.4% lower with seamless human handover. Our findings have implications for FinTech customer relationship management (CRM), digital service design and regulation of AI-based financial services.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. K. Vignesh
AU  - Thanikachalam Vadivel
AU  - Sriram Ragul Thambi Vijay
AU  - Purna Prasad Arcot
PY  - 2026
DA  - 2026/09/07
TI  - Conversational AI Chatbots in FinTech CRM: Impact on Customer Satisfaction and First-Contact Resolution Rates
BT  - Proceedings of the 2nd International Conference on Innovations and Challenges in Financial Technology (ICICFT 2025)
PB  - Atlantis Press
SP  - 302
EP  - 322
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-772-9_24
DO  - 10.2991/978-94-6239-772-9_24
ID  - Vignesh2026
ER  -