Table 1 Survey-Based vs.... | Emerald Publishing

Table 1

Survey-Based vs. Open-Finance–Based Approaches to Measuring Financial Well-Being

Argument	Survey limitations	Open finance advantages
Real-Time, Comprehensive Data	Surveys rely on self-reported data, prone to recall bias and only provide a snapshot of financial well-being at a specific point in time	Aggregates real-time financial data across institutions and products, enabling continuous and dynamic monitoring
Objective Behavioral Insights	Primarily captures subjective perceptions, which may not align with actual financial behaviors	Integrates transactional data to combine objective financial behaviors with subjective insights for a holistic view
Broader Coverage of Financial Dimensions	Constrained by the range of questions, focusing on a subset of financial products or experiences	Consolidates data from diverse financial domains, offering a multidimensional perspective on financial well-being
Granularity and Customization	Insights are often aggregated at a population or demographic level, limiting individual-level analysis and recommendations	Provides granular, client-level data that enables tailored financial well-being scores and personalized recommendations
Longitudinal Analysis	Conducted periodically, making it difficult to track long-term financial trends or changes for individuals	Facilitates longitudinal tracking, identifying patterns and trends in financial behaviors over time
Reduced Reliance on Subjective Bias	Responses may suffer from misinterpretation of questions or lack of financial literacy, leading to inaccurate answers	Automatically captures and standardizes data, minimizing errors due to misunderstanding or subjectivity

Argument	Survey limitations	Open finance advantages
Real-Time, ComprehensiveData	Surveys rely on self-reported data, prone to recall bias and only provide a snapshot of financial well-being at a specific point in time	Aggregates real-time financial data across institutions and products, enabling continuous and dynamic monitoring
Objective Behavioral Insights	Primarily captures subjective perceptions, which may not align with actual financial behaviors	Integrates transactional data to combine objective financial behaviors with subjective insights for a holistic view
Broader Coverage of FinancialDimensions	Constrained by the range of questions, focusing on a subset of financial products or experiences	Consolidates data from diverse financial domains, offering a multidimensional perspective on financial well-being
Granularity and Customization	Insights are often aggregated at a population or demographic level, limiting individual-level analysis and recommendations	Provides granular, client-level data that enables tailored financial well-being scores and personalized recommendations
Longitudinal Analysis	Conducted periodically, making it difficult to track long-term financial trends or changes for individuals	Facilitates longitudinal tracking, identifying patterns and trends in financial behaviors over time
Reduced Reliance on Subjective Bias	Responses may suffer from misinterpretation of questions or lack of financial literacy, leading to inaccurate answers	Automatically captures and standardizes data, minimizing errors due to misunderstanding or subjectivity

Source(s): Authors’ own work