With the acceleration of digital transformation in the financial industry and the widespread adoption of information technology outsourcing (ITO), financial institutions’ demand for more refined and intelligent customer relationship management (CRM) is increasing. Traditional CRM models face significant bottlenecks in data processing, customer identification, and precision marketing, making them inadequate in handling the challenges of massive, high-dimensional, and unstructured financial data. Against this background, this paper proposes a “Big Data–Driven Financial CRM Outsourcing Ecosystem Construction and XGBoost Competitiveness Enhancement Model,” aiming to integrate big data technologies, machine learning algorithms, and outsourcing service ecosystems to achieve intelligent customer relationship management and systematic improvements in marketing effectiveness. This study first constructs a multi-level financial CRM outsourcing ecosystem covering the entire process of data collection, cleaning, modeling, application, and feedback, with an emphasis on data security, compliance, and cross-institutional collaboration mechanisms. On this basis, the XGBoost machine learning model is introduced to perform high-precision prediction and classification on financial customer behavioral data, providing intelligent support for key aspects such as potential customer identification, customer value segmentation, and churn warning. Compared with traditional algorithms such as K-Means, decision trees, and random forests, XGBoost demonstrates superior performance on key metrics such as AUC and F1 scores, showing stronger predictive capability and business adaptability. This paper further applies the SHAP interpretability framework to visualize and analyze the XGBoost model, clarifying the influence mechanisms of various feature variables on prediction results, thereby enhancing the model’s transparency and credibility in business scenarios. From the dimensions of organizational structure, technical support, talent development, and institutional evaluation, the paper proposes an implementation guarantee system to ensure the feasibility and effectiveness of the model in real financial ITO environments. This study not only provides financial institutions with a systematic solution for optimizing CRM under the ITO context but also offers theoretical support and practical pathways for the deep integration of big data and machine learning in financial marketing.

Xudong Liu. Big Data–Driven Financial Customer Relationship Management Outsourcing Ecosystem Construction and XGBoost Competitiveness Enhancement Model. International Journal of Neural Network (2025), Vol. 4, Issue 1: 12-22. https://doi.org/10.38007/NN.2025.040102.

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