Google Patentsopen access

System and method for blockchain automatic tracing of money flow using … — Anchain.ai Inc. (US11907955B2)

ABSTRACT

Abstract

A method and apparatus that receive blockchain transaction data from a blockchain ledger to a transaction database, receive intelligence labels from a blockchain ecosystem intelligence database, select a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, receive input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, and generate a report including tracing destination summary statistics of the auto-traced path.

Description

This is a continuation-in-part of U.S. application Ser. No. 17/006,431 filed Aug. 28, 2020, which is incorporated herein by reference in its entirety.

BACKGROUND

Cryptocurrency offers a decentralized system to exchange funds in the form of digital assets. Due to the decentralized nature of the blockchain, tracing the origin of the funds and final destination of transactions may be difficult. However, as the value of cryptocurrency has risen, individuals and organizations have emerged that attempt to utilize this system to transfer funds to malicious entities or launder funds to hide their illicit origins. Due to the potential risks associated with cryptocurrency, regulations have been emplaced to prevent the money laundering and the funding of terrorism. Many legitimate cryptocurrency exchanges are required to comply with the regulations if they wish to continue to operate. These exchanges, however, struggle to identify transactions or accounts that may potentially violate these regulations. This places them at risk of not complying with the regulations. Therefore, a need exists for a way to identify transactions and/or accounts that carry an increased risk of violating financial regulations. There is further a need for a system and method to effectively and accurately trace money flow for investigative purposes.

BRIEF SUMMARY

In one aspect, a method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address, receiving input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, applying an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow, and generating a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses. The method additionally, on condition that the report includes a suspicious blockchain address: generates an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to the illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

In one aspect, a method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, and a digital account destination address, receiving input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter based on the transaction timestamps and the digital assets transferred, applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, generating an interactive journey board of the blockchain transaction flow based on the input trace parameters, including paths of the digital assets in the blockchain transaction flow between the blockchain addresses, where at least one of one or more of the paths, and portions of paths, may be selected for the journey board by a user.

In one aspect, a computing apparatus includes a processor. The computing apparatus also includes a memory storing instructions that, when executed by the processor, configure the apparatus to receive blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receive intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, select a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address, receive input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow, and generate a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses. The method additionally, on condition that the report includes a suspicious blockchain address: generates an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to the illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

FIG. 1 illustrates a system 100 in accordance with one embodiment.

FIG. 2 illustrates a process 200 in accordance with one embodiment.

FIG. 3 illustrates a method 300 in accordance with one embodiment.

FIG. 4 illustrates method 400 in accordance with one embodiment.

FIG. 5 illustrates method 500 in accordance with one embodiment.

FIG. 6 illustrates a graph 600 in accordance with one embodiment.

FIG. 7 illustrates a graph 700 in accordance with one embodiment.

FIG. 8 illustrates an AutoML pipeline 800 in accordance with one embodiment.

FIG. 9 illustrates a basic deep neural network 900 in accordance with one embodiment.

FIG. 10 illustrates an artificial neuron 1000 in accordance with one embodiment.

FIG. 11 illustrates a decision tree 1100 in accordance with one embodiment.

FIG. 12 illustrates a graph 1200 showing a best fit line.

FIG. 13 displays a set of graphs 1300 as examples of different learning rates.

FIG. 14 illustrates a blockchain system flow block diagram 1400 in accordance with one embodiment.

FIG. 15 illustrates a routine 1500 in accordance with one embodiment.

FIG. 16 illustrates a blockchain transaction flow block diagram 1600 in accordance with one embodiment.

FIG. 17 illustrates a suspect transaction path 1700 in accordance with one embodiment.

FIG. 18 illustrates a journey board 1800 in accordance with one embodiment.

FIG. 19 A illustrates a summary statistics report 1900 a in accordance with one embodiment.

FIG. 19 B illustrates a summary statistics report alert timeline 1900 b in accordance with one embodiment.

FIG. 20 illustrates a blockchain transaction process 2000 in accordance with one embodiment.

FIG. 21 illustrates a blockchain formation 2100 in accordance with one embodiment.

FIG. 22 illustrates a blockchain 2200 in accordance with one embodiment.

FIG. 23 depicts an illustrative system architecture and data processing device 2300 that may be used in accordance with one or more illustrative aspects described herein.

DETAILED DESCRIPTION

One challenge in tracing illicit cryptocurrency transactions is that the adversaries keep improving their money laundering tactics. There is no perfect solution to this problem, but artificial intelligence (AI) technology may be leveraged to improve investigators' efficiency. A system and method are disclosed to effectively and accurately trace money flow between entities, represented by digital account addresses, from data stored in a transaction database, using artificial intelligence and machine learning. Entities may be cryptocurrency addresses, cryptocurrency address clusters, fiat bank accounts, etc. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called cluster. Cryptocurrency address clustering may involve using a mapping function to associate multiple cryptocurrency addresses with a cluster identification (ID). Applicable use cases for the disclosed solution may include but are not limited to, anti-money laundering, cybersecurity blockchain forensics, investigation, and fraud detection.

An exemplary system for tracing money flow may include one or more of the following components:

a database system that may collect and extract, transform, and load (ETL) large volumes of transaction data, e.g., from a blockchain ledger; a machine learning model to predict behaviors from transaction data into a cryptocurrency mixer, exchange, etc. (these predicted categories may be augmented into the database); Automated Tracing (auto tracing): an Artificial Intelligence system that may trace down the money flow with set parameters and constraints for tracing, such as time range, number of hops or intermediate transactions, minimum amount to trace, etc.; a manual interactive system that allows more customized tracing and investigation; and real world entity information such as office geolocation, phone number, and email that may be associated with the traced address.

Auto tracing may be much more effective than conventional tracing techniques, especially when investigating complex blockchain and fiat money networks. Auto tracing may perform comprehensive, accurate searches in situati

This is a continuation-in-part of U.S. application Ser. No. 17/006,431 filed Aug. 28, 2020, which is incorporated herein by reference in its entirety.

BACKGROUND

Cryptocurrency offers a decentralized system to exchange funds in the form of digital assets. Due to the decentralized nature of the blockchain, tracing the origin of the funds and final destination of transactions may be difficult. However, as the value of cryptocurrency has risen, individuals and organizations have emerged that attempt to utilize this system to transfer funds to malicious entities or launder funds to hide their illicit origins. Due to the potential risks associated with cryptocurrency, regulations have been emplaced to prevent the money laundering and the funding of terrorism. Many legitimate cryptocurrency exchanges are required to comply with the regulations if they wish to continue to operate. These exchanges, however, struggle to identify transactions or accounts that may potentially violate these regulations. This places them at risk of not complying with the regulations. Therefore, a need exists for a way to identify transactions and/or accounts that carry an increased risk of violating financial regulations. There is further a need for a system and method to effectively and accurately trace money flow for investigative purposes.

BRIEF SUMMARY

In one aspect, a method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address, receiving input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, applying an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow, and generating a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses. The method additionally, on condition that the report includes a suspicious blockchain address: generates an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to the illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

In one aspect, a method includes receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receiving intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, selecting a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, and a digital account destination address, receiving input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter based on the transaction timestamps and the digital assets transferred, applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, generating an interactive journey board of the blockchain transaction flow based on the input trace parameters, including paths of the digital assets in the blockchain transaction flow between the blockchain addresses, where at least one of one or more of the paths, and portions of paths, may be selected for the journey board by a user.

In one aspect, a computing apparatus includes a processor. The computing apparatus also includes a memory storing instructions that, when executed by the processor, configure the apparatus to receive blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred, receive intelligence labels from a blockchain ecosystem intelligence database, where the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses, select a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, where the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address, receive input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses, apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow, and generate a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses. The method additionally, on condition that the report includes a suspicious blockchain address: generates an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to the illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

FIG. 1 illustrates a system 100 in accordance with one embodiment.

FIG. 2 illustrates a process 200 in accordance with one embodiment.

FIG. 3 illustrates a method 300 in accordance with one embodiment.

FIG. 4 illustrates method 400 in accordance with one embodiment.

FIG. 5 illustrates method 500 in accordance with one embodiment.

FIG. 6 illustrates a graph 600 in accordance with one embodiment.

FIG. 7 illustrates a graph 700 in accordance with one embodiment.

FIG. 8 illustrates an AutoML pipeline 800 in accordance with one embodiment.

FIG. 9 illustrates a basic deep neural network 900 in accordance with one embodiment.

FIG. 10 illustrates an artificial neuron 1000 in accordance with one embodiment.

FIG. 11 illustrates a decision tree 1100 in accordance with one embodiment.

FIG. 12 illustrates a graph 1200 showing a best fit line.

FIG. 13 displays a set of graphs 1300 as examples of different learning rates.

FIG. 14 illustrates a blockchain system flow block diagram 1400 in accordance with one embodiment.

FIG. 15 illustrates a routine 1500 in accordance with one embodiment.

FIG. 16 illustrates a blockchain transaction flow block diagram 1600 in accordance with one embodiment.

FIG. 17 illustrates a suspect transaction path 1700 in accordance with one embodiment.

FIG. 18 illustrates a journey board 1800 in accordance with one embodiment.

FIG. 19 A illustrates a summary statistics report 1900 a in accordance with one embodiment.

FIG. 19 B illustrates a summary statistics report alert timeline 1900 b in accordance with one embodiment.

FIG. 20 illustrates a blockchain transaction process 2000 in accordance with one embodiment.

FIG. 21 illustrates a blockchain formation 2100 in accordance with one embodiment.

FIG. 22 illustrates a blockchain 2200 in accordance with one embodiment.

FIG. 23 depicts an illustrative system architecture and data processing device 2300 that may be used in accordance with one or more illustrative aspects described herein.

DETAILED DESCRIPTION

One challenge in tracing illicit cryptocurrency transactions is that the adversaries keep improving their money laundering tactics. There is no perfect solution to this problem, but artificial intelligence (AI) technology may be leveraged to improve investigators' efficiency. A system and method are disclosed to effectively and accurately trace money flow between entities, represented by digital account addresses, from data stored in a transaction database, using artificial intelligence and machine learning. Entities may be cryptocurrency addresses, cryptocurrency address clusters, fiat bank accounts, etc. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called cluster. Cryptocurrency address clustering may involve using a mapping function to associate multiple cryptocurrency addresses with a cluster identification (ID). Applicable use cases for the disclosed solution may include but are not limited to, anti-money laundering, cybersecurity blockchain forensics, investigation, and fraud detection.

An exemplary system for tracing money flow may include one or more of the following components:

a database system that may collect and extract, transform, and load (ETL) large volumes of transaction data, e.g., from a blockchain ledger; a machine learning model to predict behaviors from transaction data into a cryptocurrency mixer, exchange, etc. (these predicted categories may be augmented into the database); Automated Tracing (auto tracing): an Artificial Intelligence system that may trace down the money flow with set parameters and constraints for tracing, such as time range, number of hops or intermediate transactions, minimum amount to trace, etc.; a manual interactive system that allows more customized tracing and investigation; and real world entity information such as office geolocation, phone number, and email that may be associated with the traced address.

Auto tracing may be much more effective than conventional tracing techniques, especially when investigating complex blockchain and fiat money networks. Auto tracing may perform comprehensive, accurate searches in situations where manual searches may fail to detect sophisticated money laundering paths. Machine learning used in auto tracing may predict the behavior categories of each address based on transaction history, and may thus provide comprehensive insights into the characteristics behind each address.

Blockchain transaction risk management using machine learning supports the above-disclosed system and method. The risk management machine learning model may generate real time predictions allowing a suspicious cryptocurrency transfer transaction from the user to an unknown wallet address that has observed strong linkage with a known terrorist to be suspended. The system and method can detect suspicious behaviors based on historic transactions such as frequent attempts to exploit the blockchain systems, which will generate a high risk score to block this transaction.

Monitoring decentralized systems such as a blockchains may be difficult due to their nature. As the monetary value of the digital assets increases, the need to identify suspicious transactions becomes more important to ensure an exchange is not being used for money laundering or financing terrorism. The systems and methods in this disclosure allow for regulatory compliance on Anti-Money Laundering, and Counter Terrorism Financing (AML/CFT) by identifying risky transactions associated with a cryptocurrency exchange to block the transaction, as well as freeze or suspend the accounts.

Existing applications such as blacklisted blockchain addresses or sanctioned terrorists' personal information lack adaptability to newly emerging risks. By leveraging machine learning to analyze the behaviors, combining on-blockchain and off-blockchain data sources, the methods and systems of this disclosure may be more accurate, have higher coverage, and may be more preventive.

A method of operating a risk management system for blockchain digital assets involves receiving digital on blockchain information and digital off blockchain information, wherein the receiving includes a digital asset intake engine. The method involves extracting digital data from the digital on blockchain information and the digital off blockchain information. In the method, an entity knowledge base contextualizes relationships based on the digital data and the digital off blockchain information and the digital on blockchain information. A risk classification engine including a machine learning model, analyzes the digital data and transforms the digital data to an identified behavior category, thereby creating classified risk data. A risk scoring regression engine with machine learning analyzes the classified risk data and assigns a risk score to each classified risk data. A risk policy engine analyzes the classified risk data and determines if any deviations from rules or standards have or will occur, wherein the risk policy engine is a rules based engine. A security control system takes an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score and any deviations from rules or standards. The action includes a security control system, where on condition the risk score is high, the action at least one of blocks a blockchain transaction, freezes user assets, or suspends user accounts related to the blockchain transaction. On condition the risk score is normal, the action approves the blockchain transaction.

In some configurations, the digital on blockchain information and digital off blockchain information include blockchain address, transaction identification, user information, device information, device IP address, business type, and exchange or custodian determination. Custodians are third-parties that store digital assets for users.

In some configurations, the entity knowledge base includes a blacklist intelligence database, a device intelligence database, a computer network intelligence database, and a blockchain ledger.

In some configurations, the risk classification engine includes a decision tree classification model.

In some configurations the machine learning model includes at least one of a machine learning classification model and a risk score model to calculate the risk score.

In some configurations, the machine learning classification model is an AutoML model. The operation of the AutoML model involves preparing a labeled dataset, pre-processing the labeled dataset, extracting AutoML features, transforming AutoML features, training an AutoML model, evaluating metrics of the AutoML model, selecting a best machine learning model using an automated selection process, and serializing the best machine learning model.

In some configurations, the AutoML model may be utilized as an offline training pipeline or an online prediction pipeline in the cloud or a decentralized blockchain node. The offline training pipeline may involve feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline may involve feature extraction and transformation, model prediction, and result formatting.

In some configurations, the machine learning classification model is a behavior based model and may involve recognizing behavioral characteristics in at least one feature category for an entity. These feature categories include a statistics feature category, a topology feature category, a temporal feature category, a temporal feature category, a linkage feature category, a derived feature category, a sequential feature category. The machine learning classification model may also identify exchange behavioral wallet addresses using exchange behavior characteristics, a money laundering behavioral address, a bot behavioral address, and a bad actor group cluster.

In some configurations, the machine learning classification model is a regression model for the risk score. The regression model may include input features comprising at least one of a blockchain transaction and an external information related to blockchain addresses. The regression model may also include parameters including a time decaying factor lambda. The regression model may also include an output including risk score of the address, reasons for the prediction, and a suspicious transaction summary.

A risk management system for blockchain digital assets comprises a digital asset intake engine, an entity knowledge base engine, a risk classification engine, a risk scoring regression engine, a risk policy engine, and a security control system. The digital asset intake engine is configured to receive digital on blockchain information and digital off blockchain information, and extract digital data from the digital on blockchain information and the digital off blockchain information. The entity knowledge base engine is configured to contextualize relationships based on the digital data and the digital off blockchain information and the digital on blockchain information. The risk classification engine includes a machine learning model and is configured to analyze the digital data and transform the digital data to an identified behavior category, thereby creating classified risk data. The risk scoring regression engine includes machine learning and is configured to analyze the classified risk data and assign a risk score to each classified risk data. The risk policy engine is a rules based engine. The security control system is configured to take an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score. The action by the security control system includes, on condition the risk score is high, at least one of blocking a blockchain transaction, freezing user assets, or suspending user accounts related to the blockchain transaction. On condition the risk score is normal, the action approves the blockchain transaction.

In some configurations, the digital asset intake engine includes blockchain address, transaction identification, user information, device information, device IP address, business type, exchange or custodian.

In some configurations, the entity knowledge base includes a blacklist intelligence database, a device intelligence database, a computer network intelligence database, and a blockchain ledger.

In some configurations, the knowledge base engine updates the information upon receiving threat intelligence to include a reentrancy vulnerability pattern.

In some configurations, the risk classification engine includes a decision tree classification model.

In some configurations, the risk scoring regression engine includes a machine learning classification model and a risk score model to calculate the risk score.

In some configurations, the machine learning model includes at least one of a machine learning classification model and a risk score model to calculate the risk score.

In some configurations, the machine learning classification model is an AutoML model involves preparing a labeled dataset, pre-processing the labeled dataset, extracting AutoML features, transforming AutoML features, training an AutoML model, evaluating metrics of the AutoML model, selecting a best machine learning model using an automated selection process, and serializing the best machine learning model.

In some configurations, the AutoML model is at least one of an offline training pipeline or an online prediction pipeline in the cloud or a decentralized blockchain node. The offline training pipeline involves feature extraction and transformation, parallel model training, model metric evaluation, and model selection. The online prediction pipeline involves feature extraction and transformation, model prediction, and result formatting.

In some configurations, machine learning classification model is a behavior based model comprising recognizing behavioral characteristics in at least one feature category for an entity and identifying a set of behaviors. The feature categories include a statistics feature category, a topology feature category, a temporal feature category, a temporal feature category, a linkage feature category, a derived feature category, and a sequential feature category. The set of behaviors includes exchange behavioral wallet addresses using exchange behavior characteristics, a money laundering behavioral address, a bot behavioral address, and a bad actor group cluster.

In some configurations, the machine learning classification model is a regression model for the risk score. The regression model includes input features comprising at least one of blockchain transaction and external information related to blockchain addresses. The regression model includes parameters including a time decaying factor lambda. The regression model includes outputs such as risk score of the address, reasons for the prediction, and suspicious transaction summary.

Different blockchains may be supported by this solution, including Bitcoin, Ethereum, Monero, etc. One natural extension of this solution may be investigating between multiple currencies in the same setting, such as:

Cryptocurrency+US Dollar tracing:

Bitcoin Address 1→ . . . →Bitcoin Address N→Cryptocurrency exchange→US dollar→Tom's account at Bank A→Jerry's account at Bank A

Cryptocurrency exchange plays an important role in connecting crypto currencies with fiat money. Most crypto currencies exchanges are need to have customer Know Your Customer (KYC) regulations, conceptually, by maintaining a database to map the crypto currency address to a real world identity, such as shown in the table below.

Cryptocurrency

Customer

Customer Bank

Address

Name KYC

Account (USD)

Bitcoin N

Tom

1234 at Bank A

Bitcoin X

Catherine

5678 at Bank B

Bitcoin Y

Adam

9877 at Bank C

Ethereum E

Victor

7777 at Bank D

With such KYC address mapping at the crypto exchange, the disclosed solution can now trace down the US Dollar through the banking system.

FIG. 1 illustrates a system 100 for blockchain transaction risk management. The system 100 comprises a digital asset intake engine 108 , a risk classification engine 102 , a risk scoring regression engine 104 , a risk policy engine 148 , a security control system 106 , an entity knowledge base engine 110 , and a blockchain ledger 118 . The risk classification engine 102 comprises a machine learning model 150 . The risk scoring regression engine 104 comprises a machine learning algorithm 152 . The entity knowledge base engine 110 comprise a black list intelligence database 112 , a device intelligence database 114 , a computer network intelligence database 116 . The system 100 also includes an auto tracing system 154 .

The digital asset intake engine 108 is configured to receive digital on blockchain information and digital off blockchain information, and extract digital data from the digital on blockchain information and the digital off blockchain information. The entity knowledge base engine 110 and blockchain ledger 118 are configured to contextualize relationships based on the digital data and the digital off blockchain information and the digital on blockchain information. The risk classification engine 102 includes a machine learning model and is configured to analyze the digital data and transform the digital data to an identified behavior category, thereby creating classified risk data. The risk scoring regression engine 104 includes machine learning and is configured to analyze the classified risk data and assign a risk score to each classified risk data.

The risk policy engine 148 is a rules based engine. The security control system 106 configured to take an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score.

In the system 100 , a user 120 performs a set of user actions 122 comprising a withdraw 124 , a deposit 126 , a swap 128 , and/or a transfer 130 , of funds or cryptocurrency. These actions are understood as digital on blockchain information and digital off blockchain information and are received by a digital asset intake engine 108 . The digital asset intake engine 108 extracts data (extracted data 132 ), which pulls out the digital data and the digital off blockchain information and the digital on blockchain information that includes a blockchain address 134 , a transaction identification 136 , a user information 138 , a device information 140 , a business type 146 , and a device IP address 142 , as well as the exchange or custodian information 144 , associated with the user 120 's actions. The data pulled by the digital asset intake engine 108 may be obtained from the cryptocurrency exchange where the user actions 122 were performed or other sources that track these actions. The extracted digital data and the digital off blockchain information and the digital on blockchain information is then contextualized by a risk classification engine 102 , which leverages information stored in entity knowledge bases that include the black list intelligence database 112 , device intelligence database 114 , and computer network intelligence database 116 of the entity knowledge base engine 110 , as well as the blockchain ledger 118 .

The risk classification engine 102 analyzes the information stored in the entity knowledge bases to transform the digital data into an identified behavior category, creating classified risk data. The classified risk data is then communicated to a risk scoring regression engine 104 that analyzes the classified risk data and assigns a risk score to each classified risk data. The classified risk data is then communicated to the risk policy engine 148 , which is a rules based engine. The classified risk data is then analyzed by the risk policy engine 148 to determine if any deviations from rules or standards have or will occur. The security control system 106 takes an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score and any deviations from rules or standards. For example, if the risk is high, the security control system 106 may block, freeze, or suspend the transaction and/or the account that is performing the actions. If the risk is viewed as normal, the security control system 106 may approve the transaction. The auto tracing system 154 may receive at least one of extracted data 132 , data from the entity knowledge base engine 110 , data from the risk classification engine 102 , and data from the risk policy engine 148 , but is not limited thereto.

Output from the risk classification engine 102 , entity knowledge base engine 110 , blockchain ledger 118 , extracted data 132 , and risk policy engine 148 may be input to the auto tracing system 154 . As disclosed herein, and described in greater detail with regard to FIG. 14 , the auto tracing system 154 may trace the flow of digital assets through a number of digital accounts, and may generate tracing reports 156 providing visual representations of asset flow. These tracing reports 156 may be used to assist in identifying illicit activities such as money laundering, fraud, and other activities of interest to cybersecurity blockchain forensic specialists.

FIG. 2 illustrates a flow chart for process 200 describing machine learning (ML) Entity categorization and risk engine processes. In the process 200 , blockchain address and device information/ IP 218 is received following user action on the blockchain or associated with the blockchain on a cryptocurrency exchange. At decision block 202 , the blockchain address and device information/ IP 218 is analyzed against a blacklist database 210 and a determination is made whether the blockchain address and device information/ IP 218 have been blacklisted from operating on the exchange. If the blockchain address and device information/ IP 218 has been blacklisted according to the blacklist database 210 , the process 200 moves to reject 204 the user action on the exchange. If the blockchain address and device information/ IP 218 has not been blacklisted the process moves to block 222 where a machine learning prediction is performed. The machine learning prediction utilizes an entity knowledge base comprising an identity verification database 212 , a computer network intelligence database 214 , a device intelligence database 216 , and the blockchain ledger 220 to predict entity category 224 and predict risk score 226 . The process 200 then moves to decision block 206 that determines if the predicted risk score is high. If the risk score is high, the process 200 moves to reject 204 the user action. If the risk score is not high, the process 200 moves to accept 208 the user action.

FIG. 3 illustrates a method 300 for managing risk to a block utilizing machine learning. The method 300 involves receiving digital on blockchain information and digital off blockchain information, wherein the receiving includes a digital asset intake engine (block 302 ). In block 304 , the method 300 extracts digital data from the digital on blockchain information and the digital off blockchain information. In block 306 , the method 300 contextualizes relationships based on the digital data and the digital off blockchain information and the digital on blockchain information, the contextualizing includes an entity knowledge base. In block 308 , the method 300 analyzes the digital data and transforms the digital data to an identified behavior category, thereby creating classified risk data, wherein the analyzing and transforming includes a risk classification engine including a machine learning model. In block 310 , the method 300 analyzes the classified risk data and assigns a risk score to each classified risk data, wherein the analyzing and assigning includes a risk scoring regression engine and machine learning. In block 312 , the method 300 analyzes the classified risk data and determines if any deviations from rules or standards have or will occur, wherein the analyzing and determining includes a risk policy engine that is a rules based engine. In block 314 , the method 300 takes an action on the digital on blockchain information and digital off blockchain information based on the assigned risk score and any deviations from rules or standards, wherein taking the action includes a security control system.

FIG. 4 illustrates a method 400 for training the machine learning classification model in accordance with on embodiment. The <figure-callout id="400" label="method" filenames="US11907955-20

CLAIMS

Claims ( 20 )

What is claimed is:

1. A method of generating an action for an illicit digital asset transaction, the method comprising:

receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred;

receiving intelligence labels from a blockchain ecosystem intelligence database, wherein the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses;

selecting a blockchain transaction flow comprising the blockchain transaction data associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, wherein the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address;

receiving input trace parameters, wherein the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred;

applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses;

applying an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow; and

generating a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses; and

on condition that the report includes a suspicious blockchain address:

generating an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to the illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

2. The method of claim 1 , further comprising applying machine learning based behavior prediction labels, based on the input trace parameters, to at least one of the digital account source address, the digital account intermediate addresses, and the digital account destination address, wherein the machine learning based behavior prediction label is based in part on the behavior of entities associated with at least one of crypto exchanges, crypto mixers, bots, hackers, and terrorists.

3. The method of claim 1 , further comprising:

adding address clustering labels to the blockchain addresses, wherein the address clustering labels are based on identifying similar addresses using an address transaction behavior analysis; and

grouping the similar addresses into cluster IDs.

4. The method of claim 3 , wherein the address transaction behavior analysis includes:

collecting address history blockchain transactions;

collecting real world entity information, the real world entity information including office geolocation, phone number, and email associated with the traced address;

creating address similarity features for the collected address history blockchain transactions;

calculating address similarities;

merging addresses, based on the address similarities, into an existing cluster ID or a new cluster ID; and

applying the cluster IDs to the blockchain transaction flow.

5. The method of claim 1 , wherein the artificial intelligence graph search algorithm is a heuristic based graph search algorithm.

6. The method of claim 5 , wherein the heuristic based graph search algorithms include at least one of:

a Greedy Breadth First Search algorithm;

a Dijkstra search algorithm;

an A* search algorithm;

a D* search algorithm; and

an IDA* algorithm.

7. The method of claim 1 , further comprising constructing a user interface with interactive visualization of a trace path, the interactive visualization including at least one of:

topological sorting to graph layout by the intermediate transactions from the digital account source address to the digital account destination address;

time-bound tracing, wherein the trace path is restricted by at least time and a number of intermediate transactions after a specific transaction;

value-bound tracing, wherein the trace path is based on values of digital assets transferred in the blockchain transaction flow; and

highlighting of at least a portion of the trace path.

8. The method of claim 1 , wherein the report includes at least one of:

a percentage of funding by labeled account address; and

visualization of the auto-traced path from the digital account source address to the digital account destination address, in an expandable interactive view.

9. The method of claim 1 , wherein the report includes visualization of all digital asset paths from the digital account source address to the digital account destination address, in an expandable interactive view, and wherein the transaction filter includes a time range, a number of intermediate transactions, and a minimum amount of transferred assets.

10. The method of claim 1 , further comprising creating an alert timeline, wherein in the alert timeline:

monitors the suspicious blockchain addresses, based on the intelligence labels, with wallets containing funds that have not been transferred; and

alerts the user requesting further investigation of the suspicious blockchain addresses with un-transferred funds.

11. The method of claim 1 , wherein the entities associated with the blockchain addresses include cryptocurrency addresses, cryptocurrency address clusters, accounts comprising Central Bank Digital Currency (CBDC), and fiat bank accounts.

12. A method comprising:

receiving blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred;

receiving intelligence labels from a blockchain ecosystem intelligence database, wherein the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses;

selecting a blockchain transaction flow comprising the blockchain transaction data associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, wherein the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address;

receiving input trace parameters, wherein the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter based on the transaction timestamps and the digital assets transferred;

applying the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses;

generating an interactive journey board of the blockchain transaction flow based on the input trace parameters, including paths of the digital assets in the blockchain transaction flow between the blockchain addresses, wherein at least one of one or more of the paths, and portions of paths, may be selected for the journey board by a user.

13. The method of claim 12 , further comprising applying machine learning based behavior prediction labels, based on the input trace parameters, to at least one of the digital account source address, the digital account intermediate addresses, and the digital account destination address, wherein the machine learning based behavior prediction label is based in part on the behavior of entities associated with at least one of crypto exchanges, crypto mixers, bots, hackers, and terrorists.

14. The method of claim 12 , further comprising constructing a user interface with interactive visualization of the journey board.

15. A computing apparatus comprising:

a processor; and

a memory storing instructions that, when executed by the processor, configure the apparatus to:

receive blockchain transaction data from a blockchain ledger to a transaction database, the blockchain transaction data including blockchain addresses, transaction identifications, transaction timestamps, and digital assets transferred;

receive intelligence labels from a blockchain ecosystem intelligence database, wherein the intelligence labels include known behavioral characteristics of entities associated with the blockchain addresses;

select a blockchain transaction flow comprising blockchain transaction data associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, wherein the intermediate transactions transfer the digital assets between the digital account source address and the digital account destination address;

receive input trace parameters, wherein the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred;

apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, thereby creating labeled account addresses;

apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, to determine an auto-traced path of the digital assets in the blockchain transaction flow;

generate a report including tracing destination summary statistics of the auto-traced path, the report including suspicious blockchain addresses; and

on condition that the report includes a suspicious blockchain address:

generate an action including at least one of alerting a user of the suspicious blockchain address, alerting the user to an illicit digital asset transaction associated with the suspicious blockchain address, and suspending transactions by the suspicious blockchain address.

16. The computing apparatus of claim 15 , wherein the memory includes at least one of server memory, cloud storage, key value stores, and distributed columnar databases.

17. The computing apparatus of claim 15 , wherein the instructions further configure the apparatus to construct a user interface with interactive visualization of a trace path, the interactive visualization including at least one of:

topological sort to graph layout by the intermediate transactions from the digital account source address to the digital account destination address;

time-bound trace, wherein the trace path is restricted by at least time and a number of intermediate transactions after a specific transaction;

value-bound trace, wherein the trace path is based on is based on values of digital assets transferred in the blockchain transaction flow; and

highlighting of at least a portion of the trace path.

18. The computing apparatus of claim 15 , wherein the report includes at least one of:

a percentage of funding by labeled account address; and

visualization of the auto-traced path from the digital account source address to the digital account destination address, in an expandable interactive view.

19. The computing apparatus of claim 15 , wherein the instructions further configure the apparatus to create an alert timeline, wherein in the alert timeline:

monitors the suspicious blockchain addresses, based on the intelligence labels, with wallets contain funds that have not been transferred; and

creates an alert requesting further investigation of the suspicious blockchain addresses with un-transferred funds.

20. The computing apparatus of claim 15 , wherein the report includes visualization of all digital asset paths from the digital account source address to the digital account destination address, in an expandable interactive view.

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