ML Sequence Model for Fraudulent Financial-Account Access Prediction
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Solution Overview
Problem
Current fraud detection systems in financial institutions are inadequate for real-time prediction of fraudulent financial-account access, as they focus on transactional data and fail to detect sophisticated attacks during the access stage, leading to delayed detection and increased financial losses. They are also inefficient due to reliance on slower programming languages like Python and lack scalability in processing sequences of activities.
Innovation Solution
A computerized method using a Machine Learning (ML) sequence model is developed to predict fraudulent financial-account access by encoding sequences of non-financial activities into vectors, training the model, and generating a fraud probability score through a forward propagation routine, which can be integrated into a Fraud Management System for real-time detection, utilizing a programming language like Java for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current fraud detection models use transactional data and expert rules, then they can detect fraud in execution stage, but they fail to detect sophisticated attacks in access stage and have limited performance
Solution Approach 1:
The system performs preliminary analysis of access stage activities (login attempts, device enrollments, password resets, email changes) before monetary execution occurs. By evaluating sequences of non-financial activities in advance, the system identifies suspicious patterns early and prevents fraudulent transactions before money is lost, rather than reacting after execution.
Solution Approach 2:
The system transitions from analyzing single transactional data points to evaluating multi-dimensional sequences of non-financial activities. It processes chronological sequences of events (device information, location data, activity types) as vectors, adding temporal and behavioral dimensions to fraud detection beyond traditional transactional features.
2Ease of manufacture
If fraud detection systems are built using Python, then they are easy to develop, but they are 25 times slower than compiled languages like Java and Scala in real-time applications
Solution Approach 1:
The system copies the trained ML model architecture and training methodology from Python (where development is easy) to Java (where execution is fast). The model structure, vectorization approach, and sequence processing logic are replicated in Java to achieve both development ease and real-time performance.
Solution Approach 2:
The system replaces the Python runtime environment with a Java compiled execution environment. This substitution maintains the ML model's functionality while achieving 25x speed improvement for real-time fraud detection, trading the interpreted nature of Python for the compiled efficiency of Java.
3Loss of time
If the system analyzes sequences of non-financial activities, then it can detect fraud earlier, but it increases the complexity of data processing and model requirements
Solution Approach 1:
The system changes the parameter representation by encoding sequences of non-financial activities into fixed-length vectors. Each activity sequence is transformed from variable-length textual descriptions into standardized numerical vectors, enabling efficient ML processing while maintaining the ability to detect sophisticated fraud patterns.
Solution Approach 2:
The system segments the fraud detection problem into distinct components: (1) collecting non-financial activity data, (2) vectorizing activity sequences, (3) training ML models on vectors, and (4) deploying for real-time prediction. This segmentation manages complexity by breaking down the overall system into manageable modules.
4Loss of information
If the system provides fraud probability scores instead of binary alerts, then it gives better risk assessment, but it requires more sophisticated model output interpretation
Solution Approach 1:
The system provides partial probability scores rather than complete binary decisions. By outputting fraud probability values between 0 and 1, it gives risk assessment granularity that allows financial institutions to set their own threshold levels, providing enough information without overwhelming complexity.
Data Source
AI summary
A computerized-method for predicting a probability of fraudulent financial-account access, is provided herein. The computerized-method includes a. building a Machine Learning (ML) sequence model; b. implementing a forward-propagation-routine in an encapsulated environment that runs applications to mimic a process of the ML sequence model. The forward-propagation-routine is mimicking processing of a chronical-sequence of a preconfigured number of non-financial activities sequence vector, layer by layer to generate a fraud probability score and using weights and biases which were extracted from each layer of the trained ML sequence model; and c. exporting the extracted weights, biases to a persistent storage and converting the forward propagation routine to an executable for integration with a Fraud Management System that is operating the integrated executable to predict a probability of fraudulent financial-account access by providing a fraud probability score to each chronical-sequence of a preconfigured number of non-financial activities.