Automatically Tracking Fund Flows on Blockchain

System and Method Overview:

A system and method are proposed for receiving blockchain transaction data, applying artificial intelligence and machine learning techniques, and automatically tracking the flow path of funds on the blockchain.

System components include blockchain ledgers, transaction databases, blockchain ecosystem intelligence databases, risk classification engines, risk score regression engines, security control systems, etc.

Data Processing and Analysis:

The data processing process includes receiving transaction data from blockchain ledgers, extracting digital asset transfer information, building contextual relationships, and analyzing data through machine learning models to predict transaction behavior categories.

The machine learning model uses data inside and outside the blockchain to convert transaction data into behavior categories, create classified risk data, and assign risk scores.

Intelligent Tracking and Report Generation:

The intelligent tracking system automatically tracks the path of digital assets in blockchain transaction flows based on user-entered tracking parameters (such as target direction, tracking constraints, transaction filters, etc.).

The system generates reports containing statistical summaries of tracking destinations, which can be used to identify suspicious blockchain addresses and fund flow patterns.

Risk Management and Compliance:

The risk policy engine determines whether there is any violation of rules or standards based on classified risk data and rules, and the security control system takes corresponding actions (such as blocking transactions, freezing assets, suspending accounts, etc.) based on risk scores and rule deviations.

The system supports anti-money laundering (AML) and counter-terrorism financing (CFT) regulatory compliance, helping exchanges identify high-risk transactions and prevent illegal capital flows.

Machine Learning and Automation:

The system uses a variety of machine learning techniques, including decision trees, random forests, gradient boosting trees, deep learning, etc., and improves model training efficiency through automatic machine learning (AutoML).

The AutoML system automatically selects the best machine learning model and performs real-time predictions through an online prediction pipeline, supporting rapid analysis of large-scale transaction data.

User Interface and Visualization:

The system provides an interactive user interface, through which users can customize tracking parameters, view automatic tracking results and visualization charts, and conduct in-depth transaction analysis and investigation.

Visualization tools include topological sorting, time-bound tracking, value-bound tracking and other functions to help users intuitively understand the path and pattern of capital flow.

Application and extension:

The system is not only applicable to mainstream blockchains such as Bitcoin and Ethereum, but can also be extended to other digital currencies and legal currencies to track and support cross-currency transaction analysis.

The system has a wide range of application prospects, including anti-money laundering, network security, blockchain forensics, fraud detection and other fields.

These points comprehensively demonstrate the core functions, technical implementations, application scenarios and advantages of the blockchain fund flow automatic tracking system proposed in the paper, providing regulators, exchanges and financial institutions with powerful tools to improve the transparency and regulatory compliance of blockchain transactions.