Two-Tier Transaction Prediction for Application-Specific Risk Scoring
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Solution Overview
Problem
Existing systems fail to provide a comprehensive and application-specific scoring of transactional relationships, particularly in internet-based transactions, as they lack the ability to measure legitimacy, intent, and capacity of transactional entities while being agnostic to transaction details, leading to challenges in assessing risk and reward effectively.
Innovation Solution
A risk/reward scoring system that utilizes entrant traits, factors, and outcomes, combined with predictive modeling, to generate evaluative measures considering legitimacy, intent, and capacity, while ensuring data privacy and anonymity, using a two-tier modeling architecture for platform-wide intelligence and application-specific adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a risk/reward scoring system uses comprehensive evaluative considerations including legitimacy, intent, and capacity of transactional entities, then the measurement precision of transaction evaluation is improved, but the device complexity of the scoring system increases
Solution Approach 1:
The patent segments the risk/reward evaluation into three distinct modules: legitimacy assessment (verifying identity and authorization), intent assessment (analyzing transaction purpose and motivation), and capacity assessment (evaluating ability to complete transaction). Each module processes specific data types and produces separate scores that are then combined, allowing comprehensive evaluation while managing system complexity through modular design
Solution Approach 2:
The patent introduces an intermediary scoring engine that acts as a mediator between raw transaction data and final risk/reward assessment. This engine collects data from multiple sources, processes it through the three assessment modules, and produces a synthesized score that reflects all evaluative considerations without requiring direct complex interactions between all system components
2Reliability
If the system collects and processes detailed transactional entity data to generate comprehensive risk/reward scores, then the reliability of transaction evaluation is improved, but the loss of information due to data privacy concerns increases
Solution Approach 1:
The patent applies local quality by processing different types of personal information with different levels of scrutiny and protection. Sensitive data such as identity verification information is processed only for legitimacy assessment and then discarded, while less sensitive transactional data is retained for intent and capacity analysis. This allows comprehensive evaluation while minimizing privacy intrusion by applying appropriate data handling quality to each information type
Solution Approach 2:
The patent extracts only the necessary evaluative features from complete personal data sets. Instead of storing and processing entire identity records, the system extracts specific legitimacy indicators (e.g., verification status), intent signals (e.g., transaction purpose categories), and capacity metrics (e.g., financial standing ranges). This extraction process maintains evaluation reliability while reducing data privacy risks by removing unnecessary personal information
3Adaptability or versatility
If the system provides application-specific scoring for different types of transactions, then the adaptability of the scoring system is improved, but the device complexity increases due to multiple modeling requirements
Solution Approach 1:
The patent implements a universal three-module assessment framework (legitimacy, intent, capacity) that can be applied across all transaction types. Each module is designed to handle multiple application scenarios through configurable parameters and data sources. For example, the legitimacy module can verify different entity types (individuals, businesses, organizations) using the same verification principles, and the capacity module can assess different financial capacities through standardized metrics. This universal structure provides adaptability to different applications while avoiding the complexity of creating separate models for each transaction type
Data Source
AI summary
Systems and methods for two-tier modeling for transaction outcome prediction are disclosed. The two-tier transaction prediction system comprises a first-tier platform predictive intelligence model trained by leveraging transactions from a plurality of application-specific applications, and further comprises a second-tier having a plurality of application-specific models, wherein the plurality of application-specific models are individually trained using training inputs generated by the platform predictive intelligence model such that application-specific transaction inputs are modeled in a given application-specific model against corresponding application-specific transaction outcomes by inputting the application-specific inputs into the platform predictive intelligence model which in turn outputs the inputs to the given application-specific model which is then trained against the given application-specific transaction outcomes.