Study Guide for Risk Management System for Blockchain Trading

What are the main challenges brought by cryptocurrency trading?

What problem is it trying to solve?

What roles do information on and off the blockchain play in the risk management system?

What is the function of the entity knowledge base engine?

How does the risk classification engine leverage machine learning models?

What is a risk score and how is it determined?

What are the advantages of AutoML in a risk management system?

Describe at least three categories of features that can be used to describe behavioral characteristics.

What is the role of time decay factor in risk scoring?

Explain the concept of gradient descent algorithm and its relationship to model optimization.

Answer

The main challenge of cryptocurrency trading is that its decentralized nature makes it difficult to track the source and destination of funds, increasing the risk of money laundering and terrorist financing.

It attempts to solve the problem of identifying high-risk transactions and accounts in cryptocurrency trading to help exchanges comply with anti-money laundering and anti-terrorist financing regulations.

Information on the blockchain includes transaction data, address information, etc., and information off the blockchain includes user identity, device information, etc. The risk management system integrates these two types of information for a more comprehensive risk assessment.

The entity knowledge base engine is responsible for integrating information from different sources, including blacklist databases, device information databases, etc., to provide contextual information for risk assessment.

The risk classification engine uses machine learning models to analyze transaction data and entity information, identify suspicious behavior patterns, and classify them into different risk categories.

Risk score is an indicator of the risk level of an entity or transaction. It can be calculated by a machine learning regression model, and the model input includes transaction features, entity features, etc.

AutoML can automatically complete multiple steps in the machine learning model construction process, such as feature selection, model selection, and hyperparameter optimization, thereby improving efficiency and reducing the requirement for professional knowledge.

(1) Statistical feature categories: such as the number of transactions, transaction amount, etc.; (2) Topological feature categories: such as the connection relationship between addresses, network centrality, etc.; (3) Time feature categories: such as transaction frequency, active time period, etc.

The time decay factor is used to reduce the impact of past behavior on the current risk score, because recent behavior is usually more reflective of the current risk level than historical behavior.

The gradient descent algorithm is an iterative optimization algorithm used to find the optimal value of the model parameters to minimize the model prediction error. In the risk management system, the gradient descent algorithm can be used to train the risk scoring model.

Paper title

Discuss the advantages and challenges of integrating on-chain and off-chain data in blockchain transaction risk management.

How does the application of AutoML change the traditional machine learning model development process? What impact does this have on the design and deployment of risk management systems?

Compare and contrast the application of three different machine learning models (e.g., decision trees, support vector machines, and deep neural networks) in blockchain transaction risk classification, and analyze their advantages and disadvantages.

As cryptocurrency trading continues to develop, new risks and challenges continue to emerge. Discuss how to continuously improve blockchain transaction risk management systems to meet these new challenges.

Explore the potential of blockchain technology itself in improving transaction risk management, such as how technologies such as smart contracts and zero-knowledge proofs can be applied to risk management.

Glossary

Term Definition Blockchain (Blockchain) A data structure that connects data blocks in chronological order and uses cryptography to ensure security and tamper-proof. Cryptocurrency (Cryptocurrency) A digital or virtual currency that uses cryptographic principles to ensure transaction security and control the creation of new units. Machine Learning (Machine Learning) A type of artificial intelligence that enables computer systems to learn from data and improve without being explicitly programmed. AutoML (Automated Machine Learning) Automated machine learning can automatically complete multiple steps in the machine learning model development process, such as feature engineering, model selection, and hyperparameter optimization. Risk Score is a measure of the risk level of an entity or transaction. Entity Knowledge Base is a database containing entity information related to blockchain addresses, such as exchanges, mining pools, individual users, etc. Risk Classification Engine is a system that uses machine learning models to classify blockchain transactions into different risk levels. Gradient Descent is an iterative optimization algorithm used to find the minimum value of a function.