ML Fraud Detection for Financial Credential Attachment
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Solution Overview
Problem
Cybercrime, particularly through third-party software, poses a significant threat as malicious users exploit vulnerabilities in network security by using bots to automatically attach to multiple accounts, making it difficult for providers to distinguish between legitimate and malicious access attempts, leading to potential data theft and financial loss.
Innovation Solution
Implementing a machine learning model within the third-party software to analyze features such as unverified email addresses and account creation metrics, calculating a probability score to determine the likelihood of malicious activity, and taking security actions based on this assessment to prevent unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If third-party software allows automatic account attachment without verification, then ease of operation is improved, but security reliability deteriorates due to fraudulent access attempts
Solution Approach 1:
The system performs preliminary verification actions by checking multiple features (email address validation, account creation metrics, device fingerprinting) before allowing account attachment. This preliminary assessment prevents fraudulent access attempts while maintaining smooth operation for legitimate users through automated risk evaluation.
Solution Approach 2:
An intermediary machine learning-based risk assessment system is introduced between the account attachment request and the actual access grant. This intermediary evaluates multiple features and metrics to determine the risk level, mediating between the desire for easy access and the need for security reliability.
2Reliability
If manual verification of each account attachment request is implemented, then security reliability is improved, but device complexity and processing time increase
Solution Approach 1:
The system performs self-service verification by automatically evaluating multiple features (email validity, account creation patterns, device information) through integrated machine learning models. This eliminates the need for manual verification processes while maintaining high security reliability through automated risk assessment.
Solution Approach 2:
Manual mechanical verification processes are replaced with automated machine learning-based verification systems. The ML models process multiple features and metrics automatically, substituting manual inspection with intelligent automated assessment that maintains security without increasing operational complexity.
3Reliability
If multiple security verification steps are added to account attachment, then security reliability is improved, but productivity deteriorates due to increased processing time
Solution Approach 1:
The system maintains continuity of useful action by performing multiple verification checks simultaneously and in parallel rather than sequentially. The machine learning models evaluate multiple features (email, account metrics, device information) concurrently, maintaining security reliability while avoiding processing time delays through parallelized verification operations.
Solution Approach 2:
The system changes verification parameters by dynamically adjusting the depth and type of verification based on risk assessment. Low-risk accounts receive streamlined verification while high-risk accounts undergo more thorough checks, optimizing the balance between security reliability and processing speed through adaptive parameter adjustment.
Data Source
AI summary
A method for increasing computer network security. A first request to attach a first user account for a software program to an external secure account is received. The first request includes a first set of user credentials for accessing the external secure account and a first unverified email address, the external secure account controlled by a second external computer. After receiving the first request, features having metrics is received. The metrics describe at least creation of the first user account and include at least the first unverified email address. A machine learning model processing the features calculates a probability score that the first request is fraudulent. The probability score is compared to a threshold to form a comparison result. A security action is performed with respect to attaching the first user account to the external secure account in response to the comparison result.