Predictive Model for Financial Transaction Scoring

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Solution Overview

Problem

Existing financial transaction systems struggle to accurately assess the value of complex financial transactions with multiple variables, such as payment streams and incentives, requiring extensive expertise and manual calculations, and lack the ability to compare multiple scenarios effectively.

Innovation Solution

A predictive analytical model that receives and processes independent variables representing attributes of commercial financial transactions, scales, normalizes, and applies weightings to these variables, analyzing industry and portfolio data to predict scores for specific transactions, enabling users to model scenarios, compare alternatives, and optimize financial outcomes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual calculations and extensive expertise are used to assess complex financial transactions, then measurement precision can be improved, but loss of time and device complexity increase

Engineering Contradiction:

Improveaccuracy of financial transaction assessmentVSAvoidtime required for manual calculations

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical calculations with an automated machine learning model that processes financial transaction data. The model uses historical data training to automatically assess complex financial transactions, eliminating the need for manual calculations while maintaining high measurement precision through algorithmic processing of multiple variables including payment streams, incentives, and penalties.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing users to input financial transaction parameters and automatically receiving assessed values through the machine learning model. The model autonomously processes complex calculations, compares scenarios, and provides recommendations without requiring user expertise in financial analysis, thus reducing time loss while maintaining accuracy.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If multiple financial variables and scenarios are analyzed, then measurement precision improves, but device complexity increases

Engineering Contradiction:

Improveaccuracy of financial value assessmentVSAvoidcomplexity of transaction evaluation system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model provides multi-functionality by handling diverse financial transaction types and variables within a single unified system. It can process payment streams, incentives, penalties, and other financial elements simultaneously, comparing multiple scenarios and providing comprehensive assessments without requiring separate specialized tools for each variable type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system manages complexity by dynamically adjusting parameters based on the specific financial transaction being analyzed. The machine learning model automatically selects and weights relevant variables from the input data, adapting the analysis parameters to match the unique characteristics of each transaction while maintaining a consistent and manageable system architecture.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If predictive analytical model is used, then productivity improves, but measurement precision may worsen without proper data processing

Engineering Contradiction:

Improvespeed of transaction assessmentVSAvoidaccuracy of transaction score prediction

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-processing input data through scaling and normalization operations before feeding it to the machine learning model. This preliminary data transformation ensures that the model receives standardized, properly formatted data, which maintains measurement precision while enabling rapid automated processing and improving overall productivity.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20230252504A1Predictive analytical model for financial transactions

Publication Date: 2023.08.10 GEOSPATIAL ANALYTICS INC

AI summary

Systems and methods for forecasting commercial financial transaction scores are disclosed. The systems and methods receive a series of independent variables that represent attributes of a specific commercial financial transaction. The systems and methods scale and normalize the series of independent variables. Additionally, the systems and methods assemble the scaled and normalized series of independent variables. The systems and methods also apply weightings to the assembled scaled and normalized series of independent variables and predict a score for the specific commercial financial transaction.