Big Data Analytics and Its Usage on Financial Fraud Detection in the USA
Bibliographic record
Abstract
Big data analytics has emerged as a transformative tool in the financial services industry, particularly in the United States, where institutions manage trillions of dollars in daily transactions. This study explores how financial institutions leverage big data analytics for risk management, with a specific focus on fraud detection and prevention. By integrating advanced technologies such as machine learning and artificial intelligence, big data analytics enables the real-time processing of vast datasets to uncover hidden patterns, identify anomalies, and predict potential threats. Traditional fraud detection methods often fail to address the growing complexity and sophistication of financial crimes. In contrast, machine learning models like Logistic Regression, Decision Trees, and Random Forests provide robust solutions by offering enhanced predictive accuracy and adaptability to evolving fraud tactics. This study examines a dataset comprising demographic, transactional, and geographical features, which are analyzed using machine learning algorithms. In order to guarantee fair and reliable fraud detection systems, the report emphasizes the need to strike a balance between regulatory compliance and technical improvements. The results highlight how crucial it is to include big data analytics into financial risk management plans in order to improve operational security and client confidence. To further increase the effectiveness of fraud detection, future research should concentrate on improving machine learning models, correcting biases, and investigating cutting-edge technologies like blockchain. This study confirms that big data analytics is an essential part of the continuous development of financial security and risk mitigation in the digital age, in addition to being a potent instrument for preventing fraud. Case studies from leading U.S. financial institutions, including JPMorgan Chase and PayPal, illustrate the real-world applications of big data in combating fraud. By integrating diverse data sources and leveraging advanced analytic techniques, these organizations have achieved notable reductions in fraudulent activities. The study concludes that big data analytics is not only a cornerstone of innovation and efficiency but also an essential component of modern risk management strategies. Future research should focus on addressing implementation challenges and exploring emerging technologies like blockchain to further enhance fraud detection capabilities.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".