Ant Financial's Intelligent Middle Platform: AI Applications, Data Infrastructure, and Security Practices

Ant Financial shares a comprehensive overview of its intelligent middle platform, highlighting AI-driven scenarios including smart risk control, wealth management, joint lending, personalized marketing, intelligent insurance, and AI-powered customer service assistants.

The AI capability diagram is divided into two parts: (1) industry scenarios covering the aforementioned applications, and (2) the financial brain comprising knowledge graphs, NLP, robot platforms, visual platforms, algorithms (AutoML, graph reasoning, privacy‑preserving learning, optimization, unsupervised, online, reinforcement learning, model interpretability).

Case study "Claim Treasure" demonstrates a five‑fold efficiency gain over traditional claim processing by integrating three core platforms: perception (computer vision for image classification, OCR, anti‑fraud, multi‑model fusion), NLP & knowledge graph (health KG, billions of nodes/edges), and decision‑algorithm layer.

The smart middle platform framework is summarized as role‑based (algorithm, data, engineering) and architectural layers (model, development platform, underlying technology), emphasizing the critical role of a unified data foundation for model iteration, feature quality, and financial data security.

Key challenges of the financial intelligent data foundation include efficiency (distributed storage, copy overhead, low annotation speed), quality (annotation accuracy, rapid data refinement), and security (annotation safety, training safety). Solutions involve unified storage, format, analysis, and encoding standards.

Data flow is described as a closed‑loop covering collection, preprocessing, annotation, transformation, model training, and business deployment, governed by unified governance principles.

Active learning is introduced to reduce labeling effort by up to 90%, selecting the most informative samples for annotation and integrating entropy‑based and custom selection strategies into the data foundation.

Data augmentation techniques (copy, flip, crop, scale, Gaussian noise, blur, SMOTE, GAN) are employed to address class imbalance, with examples showing 3‑5% performance gains.

Smart annotation tools (AntLabel) support multi‑modal labeling (video, image, audio, text, map), offering features such as tilt correction, auto‑alignment + OCR, image segmentation, video person tracking, frame alignment, and workflow control (label → review → reject/accept → training).

Security requirements cover encrypted storage, access control, data‑in‑flight protection, and “data‑never‑land” policies where annotated data is destroyed after use, with minimal segmentation and SDK‑based data desensitization.

The overall data foundation framework integrates platform services, product encapsulation, data ingestion, labeling, processing, and capability components, forming a dual‑middle‑platform that feeds business insights back into the financial technology ecosystem.