Construction and Application of User Portraits in Credit Scenarios

The presentation introduces the concept of user portraits for credit scenarios, outlining the business objectives such as improving acquisition, risk control, customer satisfaction, compliance, and cost efficiency.

It describes the core tasks of portrait construction: integrating multi‑source internal and external data, converting raw information into structured tags through rule‑based and model‑based methods, and applying these tags across the entire credit lifecycle.

Four main technical challenges are identified: data collection and integration, privacy and security protection, validation and accuracy assessment, and real‑time updating of portrait data.

The portrait modeling workflow consists of five steps: understanding the portrait definition, data exploration and preparation, model building and tuning, evaluation against technical and business metrics, and iterative updating.

Practical examples of core portrait features are provided, including micro‑enterprise identity, industry classification, education level, property ownership, vehicle information, income, debt, and competitor usage.

The feature system is divided into basic portraits (age, gender, device preferences, location, travel habits) and detailed feature portraits (advertising request data, marketing behavior, event logs, and business‑specific behavior).

A technical framework is presented that combines traditional machine‑learning, deep learning, graph computation, and large language models to extract signals from structured and unstructured data.

Three data‑fusion strategies are discussed: feature‑level fusion, separate modeling with score fusion, and confidence‑based fusion, each with its own trade‑offs.

Deployment covers real‑time and offline production pipelines, integration into knowledge graphs, and support for downstream decision‑making.

Effectiveness and value are evaluated using model metrics (AUC, PSI), downstream performance gains (ΔAUC, ΔKS), and business‑level indicators such as accuracy, recall, coverage, and cost‑benefit analysis.

Management practices include standardization, unified definitions, multi‑source integration, version control, monitoring, knowledge‑base construction, and visual management tools.

Application scenarios span acquisition, risk management, and customer operation, with specific use cases for pre‑approval scorecards (A‑card), behavior scorecards (B‑card), and post‑loan monitoring (C‑card).

Future extensions explore graph‑based relationship modeling, knowledge‑graph enrichment, and NLP/large‑language‑model techniques for deeper textual analysis.

The Q&A section addresses model fusion, large‑model usage in collection and recovery, the meaning of A/B/C cards, output formats, and the benefits of segment‑wise modeling.