Risk Preference Index Determination via Transaction Data Analysis

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Solution Overview

Problem

Current methods for determining user risk preference, such as questionnaires, are inefficient and fail to accurately reflect users' actual risk situations, leading to inaccurate risk preference assessments in online transactions.

Innovation Solution

A method involving the collection of user data from risk-related transactions, determination of characteristic values for variables affecting risk preference, and inputting these values into a risk preference model to generate a risk preference index, which can be used to recommend suitable financial products.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If questionnaire survey method is used to determine user risk preference, then the process is simple to implement, but the efficiency is low and accuracy is poor

Engineering Contradiction:

Improveease of implementationVSAvoiddetermination efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system automatically collects user data from transaction records and behavioral information without requiring users to manually fill out questionnaires. The risk preference determination is performed automatically by the system based on objective data, making the process self-service oriented and eliminating manual user participation while maintaining ease of implementation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical questionnaire survey method with an automated data processing system that uses machine learning models and algorithms to analyze user transaction data and behavioral patterns. This substitution transforms the manual, low-efficiency questionnaire process into an automated, high-efficiency computational system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If questionnaire survey method is used to determine user risk preference, then the implementation is straightforward, but the accuracy of risk preference assessment is poor

Engineering Contradiction:

Improveease of implementationVSAvoidrisk preference assessment accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary data processing layer that collects and analyzes multiple dimensions of user data (transaction records, behavioral information, device information) before determining risk preference. This intermediary layer processes objective data to generate accurate risk preference assessments, bridging the gap between simple data collection and precise measurement.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the parameters used for risk preference determination from subjective questionnaire responses to multiple objective data parameters including transaction amounts, frequency, types of transactions, device information, and behavioral patterns. This parameter transformation enables more accurate and comprehensive risk preference assessment.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If user data from risk-related transactions is collected and analyzed through multiple variables and modeling, then the measurement precision of risk preference is improved, but the device complexity increases

Engineering Contradiction:

Improverisk preference measurement accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the risk preference determination system into distinct functional modules: data collection module, data processing module, model training module, and risk preference determination module. Each module handles specific tasks independently, making the complex system more manageable and maintainable while achieving high measurement precision through coordinated operation of specialized components.

Inventive Principle:

Principle #1Segmentation

4Reliability

If comprehensive user data is collected and multiple variables are analyzed to determine risk preference, then the reliability of risk preference determination is improved, but the loss of time for data processing increases

Engineering Contradiction:

Improverisk preference determination reliabilityVSAvoiddata processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-collecting and storing user data from various sources (transaction records, behavioral information) as users interact with the platform. Data is prepared and organized in advance, and the risk preference model is trained beforehand, so that when risk preference determination is needed, the processing time is minimized while maintaining high reliability through comprehensive pre-gathered data.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10783457B2Method for determining risk preference of user, information recommendation method, and apparatus

Publication Date: 2020.09.22 ADVANCED NEW TECHNOLOGIES CO LTD

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining risk preference are provided. One of the methods includes: obtaining user data generated during a risk-related transaction of a user; determining a characteristic value of the user under each variable in a plurality of variables according to the user data, the plurality of variables comprising at least one variable affecting a risk preference of the user; and inputting the characteristic value of the user under each variable into a risk preference model to determine an output of the risk preference model as a risk preference index indicating a level of the risk preference of the user.