AI Agent, multimodal interaction, and edge-cloud systems engineer
📖
This article is from CSDN original.
Click to read the original for a better reading experience.
Reading Guide
AI Research & Notes
Research-oriented notes for technical trends, model capabilities, and industry context.
Best For
Readers who need a quick technical or industry background
Engineers turning research notes into engineering judgment
Prerequisites
Basic AI or backend concepts are helpful
Focus on conclusions, boundaries, and implementation implications
Takeaways
Get topic background, key terms, and trend signals
Identify engineering questions that need deeper validation
This is a research or archive-style note. Treat it as background material and verify implementation details against current official documentation when applying it.
I. Financial Industry
Industry Characteristics and Data Requirements
PB-level data generated daily
Transaction data: hundreds of millions of transactions per day
Customer data: hundreds of millions of users
Market data: millisecond-level Tick data
Core Business Scenarios
Scenario
Technology
Effect
Credit scoring
Logistic Regression/XGBoost
Integrate PBOC credit data
Real-time fraud detection
Kafka+Flink+Redis
Latency <100ms
Intelligent investment advisory
KDB+Time-series database
Nanosecond-level market analysis
Claims automation
Image recognition
92% accuracy
Technical Architecture
Data source → Kafka → Flink → Spark → Redis/Elasticsearch → Hadoop → BI
