Research on the Application of Digital and Intelligent Technologies in Customer Relationship Management of the Financial Industry

Full text

Text extracted from the article PDF for reading and search. Tables, figures and equations are in the PDF, which remains the authoritative version.

1. Introduction

Digital and Intelligent Technologies (DITs) integrate digital technologies with artificial intelligence, machine learning, and other intelligent technologies to support data analysis and intelligent decision-making. Customer relationship management (CRM) is an important management approach that uses information technology to coordinate customer interactions in marketing, sales, and services, thereby improving customer satisfaction and loyalty. Traditionally, many financial institutions focused more on product sales than on understanding customer needs, resulting in ineffective CRM practices. For example, the Tianjin Branch of the Agricultural Bank of China adopted a product-oriented marketing strategy for a long period, paid insufficient attention to customer information management, and consequently weakened customer relationship maintenance [1]. In addition, limited data analysis capability and the shortage of skilled professionals have become major obstacles to CRM optimization [2]. To address these issues, financial institutions need to leverage DITs to transform CRM from an experience-driven approach into a data-driven one. This paper analyzes the application of DITs in financial CRM, identifies challenges in customer profiling, technology integration, and data security, and proposes corresponding optimization strategies.

2. Current Status and Issues

2.1. Inaccurate Customer Profiling

Financial institutions are increasingly applying DITs, including big data and artificial intelligence, to customer relationship management and have established basic CRM platforms. However, customer information is still scattered across different business systems, where inconsistent data standards, limited interoperability, and untimely updates make it difficult to build accurate customer profiles [8]. Customer segmentation also relies mainly on static indicators such as asset size and deposit balances, while dynamic information, including consumption preferences, risk tolerance, and customer lifetime value, is not fully utilized, limiting precision marketing and personalized services [3, 4]. In addition, some institutions still use CRM mainly as a tool for recording business transactions rather than fully applying DITs to customer profiling and customer value analysis.

International Journal of Multidisciplinary Research and Growth Evaluation www.allmultidisciplinaryjournal.com

2.2. Insufficient Integration of DITs

DITs, including artificial intelligence, large language models, and natural language processing (NLP), have been widely introduced into the financial industry. However, their application in CRM is still concentrated on routine services such as intelligent customer support, transaction processing, and information inquiry, while their potential in customer acquisition, demand forecasting, and relationship management has not been fully explored [5]. Although many financial institutions have established CRM platforms, relationship managers still depend heavily on personal experience, resulting in homogeneous marketing strategies and inefficient decision-making [3]. In addition, some institutions focus on technology investment and system development but fail to integrate DITs into business processes, preventing CRM from achieving its full value [5, 6].

2.3. Prominent Data Security Risks

Financial institutions operate under strict regulatory requirements for data collection, storage, sharing, and application [7]. On the one hand, the confidential nature of financial data limits information sharing across departments and institutions [8]. On the other hand, the increasing application of DITs, particularly artificial intelligence and large language models, in customer recommendation, risk identification, and intelligent marketing has raised concerns about algorithm transparency, data privacy, and regulatory compliance. These challenges have become important constraints on the wider application of DITs in financial CRM.

3. Optimization Recommendations

3.1. Enhance Customer Profiling

Financial institutions should establish a unified customer data platform by integrating customer information, transaction records, product holdings, risk preferences, and service feedback from different business systems to support more accurate customer profiling [1, 8]. With the support of DITs, customer profiles should be updated dynamically according to behavioral changes, while multidimensional indicators such as customer value, growth potential, and activity should be adopted to improve customer segmentation and enable precision marketing and personalized services.

3.2. Deepen the Integration of DITs and CRM

Financial institutions should integrate DITs, including artificial intelligence, big data analytics, and natural language processing, into the entire customer lifecycle, covering customer identification, segmentation, demand forecasting, precision marketing, relationship maintenance, and customer churn prediction [5]. At the same time, frontline employees should receive continuous training to improve their ability to apply DITs in CRM. Marketing feedback mechanisms should also be strengthened so that operational data, such as customer response and purchase rates, can continuously optimize customer profiles and recommendation models, further improving CRM intelligence and service quality [3, 6].

3.3. Strengthen Data Governance

Financial institutions should establish a comprehensive data governance framework covering data collection, storage, sharing, and application while improving data classification and management mechanisms to facilitate secure data sharing [7]. They should also strengthen the governance of DITs by enhancing AI model review, improving algorithm transparency, and implementing full-process supervision for key models such as customer segmentation and product recommendation. Meanwhile, intelligent risk control and regulatory systems should be continuously improved to ensure data security while maximizing the value of DITs in financial CRM [8].

4. Conclusion

The continuous development of DITs is transforming CRM in the financial industry toward a more digital and intelligent model. This paper analyzes three major issues in financial CRM, namely inaccurate customer profiling, insufficient integration of DITs, and data security risks, and proposes corresponding optimization strategies. In the future, financial institutions should further strengthen data governance, promote the deeper integration of DITs with CRM, and continuously improve customer service quality and management efficiency while ensuring data security.