Published: Sept. 18, 2024
Language: Английский
International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(8), P. 2797 - 2818
Published: Aug. 30, 2024
The banking industry is undergoing a significant transformation driven by the integration of data-driven approaches aimed at enhancing customer experience. This evolution essential for banks to maintain competitive advantage, foster loyalty, and adapt rapidly changing digital landscape. By leveraging vast amounts data, can gain profound insights into behavior, preferences, needs, enabling delivery more personalized, efficient, secure services. review delves key techniques employed in consequential outcomes. Advanced data analytics allows segment their base distinct groups based on demographics, behaviours, financial needs. Techniques such as machine learning algorithms clustering identify patterns within facilitating creation targeted products For instance, solutions be specifically designed tech-savvy millennials, while tailored advice might suitable older customers. segmentation helps addressing unique needs different effectively. Utilizing historical predictive models forecast future behaviors trends. capability enables anticipate predicting which customers interested applying mortgage or those who could benefit from advisory Predictive also aids identifying risk churn, allowing implement retention strategies proactively. Through insights, offer highly personalized experiences. Recommendation systems, akin used leading e-commerce platforms, suggest relevant services individual profiles transaction histories. level personalization not only enhances experience but increases likelihood successful cross-selling upselling efforts. Employing Natural Language Processing (NLP) sentiment analysis, analyze feedback various sources, including social media, surveys, call center interactions. Understanding promptly address issues, improve service quality, build stronger, positive relationships with Data-driven are pivotal mitigating fraudulent activities. Machine detect anomalies indicative fraud. Real-time monitoring systems flag suspicious activities, thereby protecting maintaining trust system. Personalized proactive offerings significantly boost satisfaction. Customers appreciate experiences quick resolution higher levels satisfaction increased brand loyalty. Automation routine tasks maintenance streamline operations. efficiency reduces workload bank staff, them focus complex resulting cost savings improved delivery. real-time strengthen management capabilities. Banks better assess credit risks, manage fraud, ensure compliance regulatory requirements, thus safeguarding assets reputation. Effective targeting drive product uptake retention. Enhanced opportunities contribute revenue growth profitability, match Insights derived inform development new evolving continuous innovation keeps providing application advanced analytics, learning, NLP boosts loyalty drives operational efficiency, improves management, fosters growth. As continue innovate strategies, appears promising dynamic. ability evolve technological advancements will crucial customer-centric staying ahead sector. Keywords: Data-Driven, Banking, Customer Feedback.
Language: Английский
Citations
3
2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Journal Year: 2024, Volume and Issue: 11, P. 1 - 12
Published: May 23, 2024
Machine learning has been used by insurance companies for nearly a decade to identify potential risks and improve underwriting decisions. Nonetheless, there is lack of systematic survey articles on state-of-the-art (SoTA) machine techniques, especially with respect Society-oriented Environment models, developed tackle these problems. This article begins outlining the limitations current systems in this domain, including interpretability explainability constraints, privacy issues, credibility constructions along their solutions. It then provides an extensive review algorithms such as Explainable AI, Privacy-Preserving Techniques, Federated Transfer Learning, Sharpley, well others applied various Life Insurtech The also examines existing challenges future trends developing machine-learning-based decision approaches. We believe that can offer practical guidance building next-generation risk management systems.
Language: Английский
Citations
1
Administrative Sciences, Journal Year: 2024, Volume and Issue: 14(9), P. 190 - 190
Published: Aug. 26, 2024
Insurance companies are experiencing unprecedented growth due to several emerging technology functionalities that have transformed the industry’s operations. Through Three Horizons framework, this study explores technical skills required use artificial intelligence (AI) for sustainability of insurance companies. Methodologically, it was carried out in two stages: First, defining state-of-the-art, which included analysis current situation and studying technological surveillance. Second, their strategic prevalence were identified design each horizon. As a result, adoption AI allows them transform personal data-intensive processes into engines efficiency knowledge, redefining way sector offer services. This identifies immediate benefits It provides framework future innovation, emphasizing importance developing competencies ensure long-term sustainability.
Language: Английский
Citations
1
Emerald Publishing Limited eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 27 - 39
Published: Nov. 21, 2024
Introduction: In the fiercely competitive insurance landscape, embracing personalized risk management offers insurers a strategic advantage, enabling provision of innovative, customer-centric solutions tailored to individual policyholders. Purpose: The purpose study was identify AI factors that supports in sector assist investors and users manage their using different tools. Methodology: current study, primary data are collected by simple random sampling from 372 respondents. A questionnaire sent more than 500 respondents, but final sample size based on complete responses provided questionnaire. Target audience services/policy users. is empirical nature, analysis done factor analysis. Findings: It found there three major which can affect such as better management, acceptance well anticipation, customization be possible for customers utilizing AI-driven technology insurance. early identification fraudulent behavior enable loss reduction promoting price policy honest customers. An automative mechanism helpful adjusting premiums basis real-time assessment factors.
Language: Английский
Citations
1
Management & Innovation, Journal Year: 2024, Volume and Issue: 2(2), P. 1 - 13
Published: Aug. 6, 2024
This study investigates the role and challenges of insurance industry in high-quality development "Healthy China." begins by outlining background objectives Healthy China strategy. Subsequently, through a literature review case analysis, it explores historical roles healthcare domain, as well its positioning responsibilities within The further analyzes practical engagement construction China, including innovations promotions health products, collaborative models between institutions services. Additionally, identifies faced contributing to such market bottlenecks issues related product design alignment with demands. Finally, drawing from studies, this proposes recommendations enhance penetration coverage strengthen collaboration institutions, innovate products service models, future prospects for China.
Language: Английский
Citations
0
Published: Sept. 18, 2024
Language: Английский
Citations
0