Harnessing Machine Learning and AI to Analyze the Impact of Digital Finance on Urban Economic Resilience in the USA
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, the urban economies in the United States have witnessed the entry of digital finance as a revolutionary force, significantly transforming the way economic activity and the conduct of financial transactions are accomplished. This study discusses the increasing influence of digital finance, in the shapes of cell phone-based banking, fintech innovations, and digital means of making payments, in urban economic resilience. This research project deployed the tools of machine learning and artificial intelligence to analyze the impact of digital finance on the construction of urban economic resilience. The overall research objective is to develop predictive models to assess the economic adaptability and financial solidity in major American metropolises, considering the various urban area-specific traits and the various ways digital finance is used. The dataset captured a vast pool of digital finance transaction data, economic indicators, and economic health parameters to research the urban economic resilience nexus and the effect of digital finance. The digital finance transaction data captured parameters, including the size of the transactions, the type of the transactions (for instance, investments, payments), and the users' profile, from various fintech applications employed to carry out mobile banking and digital payments. The dataset was accompanied by the economic indicators extracted from the fiscal documents of the government to provide macroeconomic trends, including GDP rate, employment rate, and inflation. In the first stage of the analysis, we centered around the selective selection of the most significant economic and financial indicators, the selection of which is essential in comprehending the economic resilience dynamics. The indicators used are digital transactions, access to credit, GDP growth, the rate of unemployment, and the inflation rate because, through them, the overall economic climate could be comprehensively reviewed. We employed three machine learning algorithms for model selection to provide a detailed investigation into economic resilience, notably, Logistic Regression, Random Forest, and XG-Boost algorithms. The results from the Random Forest Classifier reveal a significant improvement in predictive performance over the baseline Logistic Regression model, achieving an impressive accuracy score. Equally, the results from the XG-Boost Classifier indicated that it is the second most accurate model for predicting urban economic resilience, with a relatively high accuracy score closely following the Random Forest Classifier. The integration of artificial intelligence (AI) in urban fiscal planning offers tremendous promise to support decision-making and optimal use of available funds. Through the algorithms in AI, city planners, and fiscal administrators are in a position to scan vast amounts of data to uncover trends and patterns that are less evident through conventional means.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it