Digital Risk Score Aggregation for Real-Time Fraud Detection
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Solution Overview
Problem
Current cybersecurity systems lack real-time detection and prevention mechanisms for cyber attacks and identity fraud, particularly in online transactions, and fail to provide a holistic approach to identifying malicious patterns and behaviors across digital channels.
Innovation Solution
A system and method for online fraud detection that utilizes a Digital Risk Score (DRS) system, which aggregates multiple risk factors into a single value through machine learning models, providing real-time risk assessment and alert generation, and includes a risk signal processing model with weighing factors to detect abnormal behavior and generate alerts or lock accounts associated with compromised user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity systems are used, then system simplicity is maintained, but real-time detection capability and fraud prevention effectiveness deteriorate
Solution Approach 1:
The system segments fraud detection into multiple independent risk components (device risk, network risk, behavioral risk, etc.), each evaluated separately by dedicated models. This modular approach enables real-time comprehensive analysis without overwhelming system complexity, as each segment can be processed independently and aggregated into an overall risk score.
Solution Approach 2:
The system performs preliminary risk assessment by pre-establishing risk models and evaluation frameworks before actual transactions occur. Risk signals are continuously monitored and pre-processed, allowing the system to quickly evaluate new transactions against established patterns without complex real-time computation during critical moments.
2Measurement precision
If comprehensive multifactor analysis is implemented, then fraud detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial analysis by focusing on the most critical risk factors for each transaction type. Rather than evaluating every possible parameter equally, the system identifies and prioritizes key risk indicators relevant to specific transaction contexts, achieving high detection accuracy with reduced processing overhead.
Solution Approach 2:
The system replaces complex mechanical computation with machine learning models that have been pre-trained on historical data. These models automatically weigh and evaluate multiple risk factors simultaneously, providing accurate fraud detection without requiring extensive real-time computational resources for manual analysis.
3Productivity
If real-time risk assessment is implemented, then fraud prevention capability improves, but system resource consumption increases
Solution Approach 1:
Risk models and evaluation criteria are established in advance through training on historical fraud data. This preliminary preparation allows the system to perform rapid real-time assessments by simply applying pre-computed models to new transactions, rather than building complex analysis frameworks during each evaluation, thus reducing real-time resource consumption.
4Ease of operation
If multiple risk components are aggregated into a single score, then decision-making simplicity improves, but loss of detailed information deteriorates
Solution Approach 1:
The system maintains segmented risk component scores (device risk, network risk, behavioral risk, etc.) alongside the aggregated overall risk score. This segmentation preserves detailed information about specific risk sources while providing a simplified aggregate score for quick decision-making, allowing both detailed analysis and simple operational decisions.
Solution Approach 2:
The aggregated risk score serves multiple functions: it provides a simple threshold-based decision metric for automated systems, enables quick visual assessment for human operators, and can be broken down into component scores when detailed analysis is needed. This multi-functionality maintains information availability while simplifying operational decisions.
Data Source
AI summary
System and method for signal processing for cyber fraud detection are disclosed. The method may include: receiving a trigger signal for fraud detection, the trigger signal comprising an event indicator and entity data associated with an entity profile stored in a database; determining, based on the trigger signal, a risk signal processing model comprising a plurality of risk components, each risk component associated with a respective weighing factor; computing, based on the risk signal processing model, a respective risk signal for each of the plurality of risk components; processing the respective risk signal for each of the plurality of risk components in real time or near real time to generate an aggregated risk signal; and generating, based on the aggregated risk signal, a fraud or cyber security alert signal.