Machine Learning Authentication with Geo-Location Pattern Detection
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Solution Overview
Problem
Conventional systems for detecting unauthorized activity and user authentication are not predictive and do not proactively identify expected user transactions, leading to inefficiencies and potential security vulnerabilities.
Innovation Solution
A machine learning model is trained using historical user data to generate expected user patterns, which are compared with real-time transaction details and geo-location data to authenticate users and detect unauthorized activity, minimizing user input and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication systems are used, then user authentication can be performed, but the systems are not predictive and do not proactively identify expected user transactions, leading to security vulnerabilities and inefficiencies
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical user data to generate expected user patterns before actual authentication occurs. These patterns include typical transaction amounts, frequencies, locations, and timings. When a transaction request is received, the system compares it against pre-established patterns to proactively identify expected transactions and detect anomalies, enabling predictive authentication rather than reactive verification.
2Measurement precision
If machine learning models are implemented to predict user patterns, then authentication accuracy improves, but system complexity increases
Solution Approach 1:
The machine learning model operates autonomously to continuously learn from historical user data and automatically update expected user patterns without requiring manual intervention. The system self-trains on incoming transaction data, adjusts pattern parameters, and adapts to changing user behaviors automatically. This self-service capability maintains high measurement precision while reducing the operational complexity burden on system administrators.
3Reliability
If geo-location data is collected and analyzed in real-time, then unauthorized activity detection improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing geo-location data along with other transaction parameters in structured formats during off-peak periods. Historical geo-location patterns are pre-analyzed and stored as reference data. When a real-time transaction occurs, the system quickly retrieves and compares current geo-location data against pre-prepared patterns, enabling fast unauthorized activity detection without extensive real-time computational overhead.
4Reliability
If multiple data parameters are analyzed for each transaction, then authentication security improves, but processing speed decreases
Solution Approach 1:
The system applies local quality by analyzing different data parameters with varying depths of scrutiny based on their risk profiles. High-risk parameters such as geo-location deviations and transaction amount anomalies receive intensive analysis, while low-risk parameters undergo lighter verification. The machine learning model dynamically adjusts the analysis depth for each parameter based on the specific transaction context, maintaining high authentication security while optimizing processing throughput by avoiding uniform deep analysis of all parameters.
Data Source
AI summary
Arrangements for providing frictionless unauthorized activity detection and user authentication are provided. In some aspects, user data, such as transaction data may be received and used to train a machine learning model. In some examples, the machine learning model may be executed to generate one or more expected user patterns. In some arrangements, a request for transaction may be received. The request for transaction may include transaction details. In response, the system may request current geo-location data of a user. In some examples, the transaction details and geo-location data may be analyzed (e.g., compared to the expected user patterns) to generate an authentication output. The authentication output may then be transmitted to one or more systems to process the requested transaction, prevent transaction processing, or the like.