Wodan AI provides security for high-stakes financial AI applications. Our solution enables secure collaboration and data analysis while ensuring complete privacy and protection against data breaches.
We safeguard sensitive information for both service providers and consumers, mitigating financial, legal, and reputational risks associated with data leaks in critical areas such as credit scoring, fraud detection, and portfolio management
Credit Scoring and Risk Analysis
Wodan AI allows providers of credit scoring and risk analysis platforms to use their customer’s data entirely encrypted.
Why Encrypted Data for Credit Scoring and Risk Analysis?
Enhanced Data Protection: Encrypt sensitive financial data used in credit scoring models, reducing the risk of exposing personal information during analysis.
Regulatory Compliance: Meet strict data protection requirements (e.g., GDPR, CCPA) while still leveraging AI for accurate credit assessments.
Broader Data Utilization: Potentially incorporate encrypted data from multiple sources to create more comprehensive risk profiles without compromising individual privacy.
Competitive Edge: Offer superior data protection in your credit scoring services, attracting privacy-conscious customers and partners.
Specific Use Cases in Credit Scoring and Risk Analysis
Secure Credit Score Calculation: Develop AI models that compute credit scores using encrypted financial data. This allows for accurate scoring without exposing raw financial details.
Multi-Party Risk Assessment: Collaborate with other financial institutions to enhance risk models by analyzing encrypted data from multiple sources.
Privacy-Preserving Default Prediction: Create AI models that predict loan default probability using encrypted data points.
Secure Alternative Data Analysis: Incorporate encrypted alternative data sources into your risk models. This can improve assessment accuracy for thin-file or underbanked individuals.
Confidential Financial Behavior Modeling: Develop AI models that analyze encrypted transaction data to assess spending patterns and financial behavior without exposing individual transactions.
Secure Cross-Border Credit Assessment: Perform credit risk analysis for international customers by securely processing encrypted financial data from various countries, ensuring compliance with different data protection laws.
Anti-Money Laundering and Fraud Detection
Enhanced AML and Fraud Detection with Encrypted Data
Encrypted data processing allows financial institutions to:
Protect sensitive customer information during analysis, reducing the risk of data breaches
Comply with stringent regulations like GDPR and CCPA while still leveraging AI for fraud detection
Securely collaborate with other institutions to enhance fraud detection models without exposing raw data
Improved Fraud Detection Capabilities
AI models can analyze encrypted data to:
Identify suspicious patterns in transactions without exposing individual details
Assess customer behavior and risk profiles using a wider range of encrypted data sources
Detect complex fraud schemes by analyzing encrypted cross-institutional data
Specific Use Cases in AML and Fraud Detection
Secure Transaction Monitoring: Develop AI models that analyze encrypted transaction data to identify potentially fraudulent activities without compromising individual privacy.
Multi-Party Fraud Detection: Collaborate with other financial institutions to enhance fraud detection by analyzing encrypted data from multiple sources, improving the ability to identify sophisticated fraud schemes.
Privacy-Preserving Customer Due Diligence: Create AI models that perform customer due diligence and risk assessments using encrypted personal and financial data, ensuring compliance with AML regulations while protecting customer privacy.
Secure Cross-Border AML Monitoring: Perform AML risk analysis for international transactions by securely processing encrypted financial data from various countries, ensuring compliance with different data protection laws.
Confidential Behavioral Analysis: Develop AI models that analyze encrypted customer data to assess spending patterns and financial behavior for fraud detection without exposing individual transactions.
Encrypted Alternative Data Analysis: Incorporate encrypted alternative data sources, such as social media activity or online transactions, into fraud detection models to improve accuracy while maintaining data privacy.
Algorithmic Trading with Encrypted Data
Encrypted data processing allows financial institutions to:
Protect proprietary trading strategies and algorithms during execution, reducing the risk of intellectual property theft
Comply with regulations like MiFID II while still leveraging AI for trading decisions
Securely collaborate with other institutions to enhance trading models without exposing sensitive data
Improved Trading Capabilities with Encrypted Data
AI models can analyze encrypted data to:
Identify market trends and patterns without exposing individual trade details
Assess market conditions and risk profiles using a wider range of encrypted data sources
Execute complex trading strategies by analyzing encrypted cross-institutional data
Specific Use Cases in Algorithmic Trading
Secure Market Data Analysis: Develop AI models that analyze encrypted market data to identify trading opportunities without compromising the privacy of individual trades or strategies.
Multi-Party Trading Collaboration: Collaborate with other financial institutions to enhance trading strategies by analyzing encrypted data from multiple sources, improving the ability to identify market inefficiencies and arbitrage opportunities.
Secure Cross-Border Trading: Execute trading strategies across multiple exchanges by securely processing encrypted financial data from various markets, ensuring compliance with different regulatory frameworks.
Encrypted Alternative Data Analysis: Incorporate encrypted alternative data sources, such as satellite imagery or web scraping data, into trading models to improve accuracy while maintaining data privacy and competitive advantage.
KYC, KYB and KYT solutions
Enhanced KYC, KYB and KYT solutions with Encrypted Data
Encrypted data processing allows financial institutions to:
Protect sensitive customer and business information during verification processes, reducing the risk of data breaches
Comply with stringent regulations like GDPR, CCPA, and AML/CFT requirements while still leveraging AI for identity verification
Securely collaborate with other institutions to enhance verification models without exposing raw data
Improved Verification Capabilities with Encrypted Data
AI models can analyze encrypted data to:
Identify suspicious patterns in customer and business profiles without exposing individual details
Assess customer and business risk profiles using a wider range of encrypted data sources
Detect complex fraud schemes by analyzing encrypted cross-institutional data
Specific Use Cases in KYC/KYB/KYT
Secure Identity Verification: Develop AI models that analyze encrypted personal data to verify customer identities without compromising individual privacy.
Multi-Party Business Verification: Collaborate with other financial institutions to enhance business verification by analyzing encrypted data from multiple sources, improving the ability to identify fraudulent entities and complex ownership structures.
Privacy-Preserving Ultimate Beneficial Owner (UBO) Identification: Create AI models that perform UBO identification and risk assessments using encrypted personal and financial data, ensuring compliance with AML regulations while protecting privacy.
Secure Cross-Border Transaction Monitoring: Perform transaction monitoring for international transfers by securely processing encrypted financial data from various countries, ensuring compliance with different data protection laws.
Encrypted Alternative Data Analysis: Incorporate encrypted alternative data sources, such as social media activity or online transactions, into verification models to improve accuracy while maintaining data privacy.
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