Key Concepts

1. Address Identification

1.1 Entity

In the field of cryptocurrency, an entity refers to the real-world identity associated with a specific address or entity. It typically represents a specific institution, platform, or application, such as "Binance", "Circle". The identification of entities helps in tracking and categorizing cryptocurrency activities.

1.2 Category

The cryptocurrency category classifies entities based on their business nature or function. It provides information about the specific business type associated with the entity. For example, "Exchange" is a common category representing an entity that operates a digital asset trading platform.

1.3 ML (Money Laundering) Risk Score

The money laundering risk score for cryptocurrency is an assessment that measures the potential risk of money laundering associated with a specific address or entity. It takes into account various factors, including transaction patterns, associated addresses, and historical behavior, to calculate the risk score. Higher money laundering risk scores indicate a higher potential risk associated with the address or entity.

2. Transaction Insights

2.1 Exposure

Exposure refers to the funds or transaction volume received and sent by an address or entity within a specific time frame. It provides users with a quick overview of the financial activities associated with a particular address or entity, aiding in assessing its risk level.

By showcasing the distribution of funds inflow and outflow, Exposure reveals the sources and destinations of funds for an address or entity. This helps users identify abnormal or suspicious fund activities,. Exposure also distinguishes between direct and indirect fund flows, with the latter involving multiple hops of fund transfers, providing deeper insights into real risk behavior.

3. Product

3.1 AML (Anti-Money Laundering)

AML (Anti-Money Laundering) refers to a set of laws, regulations, and procedures designed to prevent and detect illegal money laundering activities. It involves financial institutions and other regulated entities taking measures to ensure compliance and reduce money laundering risks by monitoring and reporting suspicious transactions. AML requires institutions to conduct customer due diligence (KYC), monitor transaction activities, and establish internal control measures to identify and prevent potential money laundering.

3.2 KYC (Know Your Customer)

KYC is an identity verification and customer identification process used to verify and confirm the identity and relevant information of cryptocurrency users. KYC aims to prevent financial crimes and abuse, ensure compliance with regulations, and establish a trusted trading environment. KYC typically involves users submitting personal identification documents, proof of address, and other relevant information to verify their authenticity.

3.3 KYA (Know Your Address)

KYA is a cryptocurrency risk control product specifically designed for assessing and monitoring the risk of addresses. By analyzing transaction patterns, historical behavior, and associated entities, KYA provides risk assessments and insights about specific addresses to help users identify potential risks and take appropriate measures.

3.4 KYT (Know Your Transaction)

KYT is a risk control tool used for monitoring and analyzing cryptocurrency transactions. It monitors and analyzes transaction patterns, amounts, participants, and other related factors based on real-time or near-real-time transaction data to identify potential money laundering, fraud, or other illicit activities. KYT helps improve transaction compliance and security, protecting users and platforms from risks.

3.5 Anti-Fraud

Anti-Fraud is the set of preventive and investigative measures taken against fraudulent activities. It includes identifying and predicting fraud patterns, monitoring suspicious activities, implementing control measures, and developing response strategies. Anti-fraud measures aim to protect individuals, organizations, and systems from fraudulent behavior, reducing financial losses and risks. It typically involves the use of intelligent technologies, machine learning models, and data analysis tools to identify abnormal behavior, fraud patterns, and risk signals, and to take appropriate responses and defense measures.

3.6 Device Intelligence

Device intelligence refers to products that utilize advanced artificial intelligence and machine learning technologies to identify and prevent fraudulent activities. These intelligent devices analyze large amounts of data, detect abnormal behaviors and fraud patterns, and take corresponding actions based on predefined rules, such as issuing alerts or implementing preventive measures. They play a vital role in the field of anti-fraud by enhancing security, reducing fraud risks, and assisting anti-fraud teams in effectively combating fraud threats.