Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/kamalbuilds/goa-hh-idea


https://github.com/kamalbuilds/goa-hh-idea

Last synced: 3 days ago
JSON representation

Awesome Lists containing this project

README 

        
# goa-hh-idea



We will be pivoting to build  AI-assisted anti-money laundering (AML) tools for the web3 space:



## The Need for AI-Driven AML Solutions



As the adoption of cryptocurrencies and blockchain technology continues to grow, so does the potential for misuse by bad actors. Traditional AML methods often struggle to keep up with the speed and complexity of transactions on the blockchain. AI can help bridge this gap by providing real-time analysis and detection of suspicious activities.



## Key Features of an AI-Driven AML Tool



1. **Transaction pattern analysis**: Use machine learning algorithms to identify unusual transaction patterns that deviate from normal user behavior, which could indicate money laundering or other illicit activities.



2. **Entity risk scoring**: Develop AI models to assess the risk level of wallet addresses, contracts, and other entities based on their transaction history, connections, and other relevant factors.



3. **Anomaly detection**: Employ unsupervised learning techniques to identify outliers and anomalies in transaction data that may warrant further investigation.



4. **Explainable AI**: Ensure that the AI models provide clear explanations for their decisions, making it easier for compliance teams to understand and act upon the insights provided by the tool.



5. **Automated reporting**: Integrate the AI-driven AML tool with existing compliance workflows, enabling automatic generation of suspicious activity reports (SARs) and other regulatory filings.



## Potential Use Cases



1. **Cryptocurrency exchanges**: Help exchanges comply with AML regulations by providing real-time monitoring and detection of suspicious activities.



2. **Decentralized finance (DeFi) platforms**: Protect DeFi protocols from being exploited for money laundering by integrating AI-driven AML tools into their systems.



3. **Blockchain analytics firms**: Offer AI-powered AML solutions as a service to help businesses and organizations in the web3 space maintain compliance and mitigate risks.



4. **Regulatory bodies**: Assist government agencies and financial regulators in monitoring and enforcing AML regulations in the rapidly evolving web3 ecosystem.



By developing an AI-driven AML tool, we will help make the web3 space more secure, compliant, and attractive to mainstream adoption. 



The Hacker House Goa hackathon provides an excellent opportunity to showcase our innovative solution and potentially collaborate with industry leaders in this space.



## Key technologies that we are looking to utilise to build an AI-assisted anti-money laundering (AML) tool for the web3 space:



## Core Technologies



1. **Blockchain Data Ingestion**: Utilize APIs and protocols like Ethereum's JSON-RPC to continuously ingest and process transaction data from various blockchain networks.



2. **Data Preprocessing**: Clean, normalize, and enrich the blockchain data to prepare it for machine learning analysis, handling challenges like address obfuscation and data sparsity.



3. **Machine Learning Models**: Leverage a variety of supervised and unsupervised learning techniques, such as:

   - Anomaly detection: Isolation Forests, One-Class SVMs, Autoencoders

   - Transaction pattern analysis: Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs)

   - Entity risk scoring: Logistic Regression, Random Forests, XGBoost



4. **Explainable AI**: Incorporate techniques like SHAP values, LIME, or Grad-CAM to provide interpretable insights into the AI model's decision-making process.



5. **Automated Reporting**: Integrate the AI-driven AML tool with workflow automation tools, such as Zapier or IFTTT, to generate and submit suspicious activity reports (SARs) to regulatory bodies.



## Supporting Technologies



1. **Distributed Data Storage**: Use decentralized storage solutions like IPFS or Filecoin to securely store and access the large volumes of blockchain data required for the AML analysis.



2. **Serverless Computing**: Leverage serverless functions (e.g., AWS Lambda, Google Cloud Functions) to scale the data processing and model inference capabilities as needed.



3. **Containerization and Orchestration**: Package the AML solution as Docker containers and deploy them using Kubernetes or other container orchestration platforms for scalability and reliability.



4. **Monitoring and Alerting**: Implement robust monitoring and alerting systems to quickly identify and respond to any issues or anomalies detected by the AML tool.



5. **Secure API Access**: Provide secure API access to the AML solution, potentially using technologies like OAuth 2.0 or JSON Web Tokens (JWT) for authentication and authorization.



By combining these cutting-edge technologies, we will build a comprehensive, scalable, and secure AI-driven AML solution that can help make the web3 ecosystem more resilient against money laundering and other financial crimes.