Szczegóły publikacji
Opis bibliograficzny
AI and robotic process automation in Fintech: analyzing the shift towards digitized customer services and operational efficiency / Pascal Muam MAH, John Muzam, Tomasz PEŁECH-PILICHOWSKI, Daniel Tambi Mbuh, Eyong Ako // Scientific Papers of Silesian University of Technology [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2720-751X. Organization & Management ; ISSN 2720-751X. — 2023 — no. 187, s. 399–417. — Bibliogr. s. 414–417, Abstr.
Autorzy (5)
- AGHMah Pascal Muam
- Muzam John
- AGHPełech-Pilichowski Tomasz
- Mbuh Daniel Tambi
- Ako Eyong
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 151483 |
|---|---|
| Data dodania do BaDAP | 2024-01-29 |
| Tekst źródłowy | URL |
| DOI | 10.29119/1641-3466.2023.187.21 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Zeszyty Naukowe Politechniki Śląskiej = Scientific Journal of Silesian University of Technology, Organizacja i Zarządzanie = Organization & Management |
Abstract
Introduction: The need to replace human interaction in financial sectors with robotic process automation (RPA) has led to advanced services that have boosted productivity in financial sectors. RPA has systematically improved the output quality of financial services with high efficiency, service effectiveness, human resource engagements, advanced personnel management supports66t, employee loyalty, and customer satisfaction. Objectives: The study aims to lay out systematic measures for potential customers to self- evaluate financial sectors before engaging in their services. Also, aims to understand how Fintech uses AI and RPA to advance the amalgamation of traditional financing into a digitized system with modern services. Problem: Fintech with the help of RPA has led to social interactions through advanced transformative financial services that have pushed potential customers into limbo. Method and Material: The study developed three pre-train deep learning techniques of digital evaluators called robotic process automation indicators. Bank satisfactory score survey was sample to validate and determine the study statistical data. Bank satisfactory score (BSAT) and bank effort score (BES) were used to examine banking services. Each robotic process automation indicator (RPAI) helps to evaluate five (5) financial service transformations identified by the study. Results: Based on the Bank satisfactory score survey validation statistics, the bank satisfaction score (BSAT) and bank effort score (BES) a score of 2.2 and 2.2 respectively was recorded which indicates that the said bank is very affordable for customers. Conclusion: The study concluded that Fintech is the best part of cognitive robotic process automation of intelligent systems empowered by cognitive computing technology that assists financial sectors and customers using best practice services.
