Personalized Recommendation System of 51 Credit Card: AI‑Driven Model Platform and Implementation

Speaker introduction: Chen Bingqiang, senior algorithm expert at 51 Credit Card, previously served as algorithm R&D director at Nanjee and head of the ID‑Graph team at a leading big‑data company, focusing on ML and DL solutions for finance.

Presentation title: "Personalized Recommendation System of 51 Credit Card".

Content overview: AI is a key driver of transformation in the financial industry. After years of data accumulation and technological innovation, 51 Credit Card has successfully applied AI to user interaction, business management, precise marketing, and product optimization. The technical system consists of two main parts.

1. Model platform: includes automated feature selection, multi‑language model switching, automatic model deployment and publishing, model performance monitoring, and scenario preservation. The platform accelerates model iteration, reduces prediction degradation risk, enhances model reuse, and lowers development costs.

2. Personalized recommendation system: business modules cover new‑user intent recognition (wealth management, credit applications, loans, bill management), existing‑user preference calculation, precise credit‑card recommendation, article ranking, and real‑time user profiling. The algorithm stack combines traditional machine‑learning methods (Random Forest, SVM, MF, LR, XGBoost) with deep‑learning models (LSTM, RBM, CNN). The system now serves multiple product lines, delivering "thousands of faces for thousands of users," improving user experience and increasing conversion rates.

Company background: 51 Credit Card, a leading Chinese fintech listed on the Hong Kong main board in 2018, offers a suite of apps ("51 Credit Card Manager", "51 Renpin", "51 Renpin Loan", "Give You Flower", etc.) serving over 100 million activated users with an integrated personal finance ecosystem.

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Disclaimer: The content is provided for knowledge sharing only, without any guarantee of accuracy or completeness, and does not constitute investment advice.