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Benford's Law Explained: Insights for Quants and Developers in Predictive Analytics. O6.0 TRANSFORM DATA
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- FormatePub
- ISBN8232937065
- EAN9798232937065
- Date de parution17/10/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurHamza elmir
Résumé
Discover the hidden order in datasets with Benford's Law. This phenomenon reveals that digit 1 appears ~30% of the time, while 9 trails at ~6%. Experts leverage this to detect anomalies in finance, physics, and AI. "Benford's Law Explained" provides the depth needed to apply this phenomenon in real-world scenarios. Battle-Tested Applications- Financial Forensics: Identify cooked books, detect anomalies in ledger entries, expense reports, and stock volumes.
Implement SEC-compliant workflows.- AI/ML Safeguards: Validate synthetic data generators, GAN outputs, and audit training datasets for biases or manipulation.- IoT & Sensor Analytics: Identify malfunctioning sensors, filter noise from industrial telemetry streams, and detect anomalies.- Compliance & Auditing: Automate Benford screens for anti-money laundering (AML) and procurement fraud. Quantify "reasonable suspicion" for regulatory evidence.
Technical Deep Dives- Code Libraries: Python (with benford_py/custom Pandas), R (benford.analysis), SQL (window functions), and Scala/Spark for petabyte-scale data.- Advanced Metrics: Kullback-Leibler divergence, Mantissa arc tests, and sequential analysis.- Edge Cases Demystified: When to avoid Benford (assigned IDs, bounded ranges).- Scalability Tactics: Approximate algorithms for streaming data and distributed systems.
Real-World Case Studies- Quant Fund: Detecting spoofed trades in limit order books.- E-Comm Platform: Uncovering fake reviews via rating distributions.- Health Tech: Validating clinical trial data integrity. For Whom?- Quants & Traders: Screening market data for manipulation.- Data Engineers: Building validation layers in ETL pipelines.- MLOps/Data Scientists: Stress-testing model inputs/outputs.- Auditors & Risk Officers: Automating forensic workflows.- Academic Researchers: Statistical foundations and extensions.
This book provides a comprehensive guide to applying Benford's Law in real-world scenarios, with code-ready insights and technical deep dives.
Implement SEC-compliant workflows.- AI/ML Safeguards: Validate synthetic data generators, GAN outputs, and audit training datasets for biases or manipulation.- IoT & Sensor Analytics: Identify malfunctioning sensors, filter noise from industrial telemetry streams, and detect anomalies.- Compliance & Auditing: Automate Benford screens for anti-money laundering (AML) and procurement fraud. Quantify "reasonable suspicion" for regulatory evidence.
Technical Deep Dives- Code Libraries: Python (with benford_py/custom Pandas), R (benford.analysis), SQL (window functions), and Scala/Spark for petabyte-scale data.- Advanced Metrics: Kullback-Leibler divergence, Mantissa arc tests, and sequential analysis.- Edge Cases Demystified: When to avoid Benford (assigned IDs, bounded ranges).- Scalability Tactics: Approximate algorithms for streaming data and distributed systems.
Real-World Case Studies- Quant Fund: Detecting spoofed trades in limit order books.- E-Comm Platform: Uncovering fake reviews via rating distributions.- Health Tech: Validating clinical trial data integrity. For Whom?- Quants & Traders: Screening market data for manipulation.- Data Engineers: Building validation layers in ETL pipelines.- MLOps/Data Scientists: Stress-testing model inputs/outputs.- Auditors & Risk Officers: Automating forensic workflows.- Academic Researchers: Statistical foundations and extensions.
This book provides a comprehensive guide to applying Benford's Law in real-world scenarios, with code-ready insights and technical deep dives.
Discover the hidden order in datasets with Benford's Law. This phenomenon reveals that digit 1 appears ~30% of the time, while 9 trails at ~6%. Experts leverage this to detect anomalies in finance, physics, and AI. "Benford's Law Explained" provides the depth needed to apply this phenomenon in real-world scenarios. Battle-Tested Applications- Financial Forensics: Identify cooked books, detect anomalies in ledger entries, expense reports, and stock volumes.
Implement SEC-compliant workflows.- AI/ML Safeguards: Validate synthetic data generators, GAN outputs, and audit training datasets for biases or manipulation.- IoT & Sensor Analytics: Identify malfunctioning sensors, filter noise from industrial telemetry streams, and detect anomalies.- Compliance & Auditing: Automate Benford screens for anti-money laundering (AML) and procurement fraud. Quantify "reasonable suspicion" for regulatory evidence.
Technical Deep Dives- Code Libraries: Python (with benford_py/custom Pandas), R (benford.analysis), SQL (window functions), and Scala/Spark for petabyte-scale data.- Advanced Metrics: Kullback-Leibler divergence, Mantissa arc tests, and sequential analysis.- Edge Cases Demystified: When to avoid Benford (assigned IDs, bounded ranges).- Scalability Tactics: Approximate algorithms for streaming data and distributed systems.
Real-World Case Studies- Quant Fund: Detecting spoofed trades in limit order books.- E-Comm Platform: Uncovering fake reviews via rating distributions.- Health Tech: Validating clinical trial data integrity. For Whom?- Quants & Traders: Screening market data for manipulation.- Data Engineers: Building validation layers in ETL pipelines.- MLOps/Data Scientists: Stress-testing model inputs/outputs.- Auditors & Risk Officers: Automating forensic workflows.- Academic Researchers: Statistical foundations and extensions.
This book provides a comprehensive guide to applying Benford's Law in real-world scenarios, with code-ready insights and technical deep dives.
Implement SEC-compliant workflows.- AI/ML Safeguards: Validate synthetic data generators, GAN outputs, and audit training datasets for biases or manipulation.- IoT & Sensor Analytics: Identify malfunctioning sensors, filter noise from industrial telemetry streams, and detect anomalies.- Compliance & Auditing: Automate Benford screens for anti-money laundering (AML) and procurement fraud. Quantify "reasonable suspicion" for regulatory evidence.
Technical Deep Dives- Code Libraries: Python (with benford_py/custom Pandas), R (benford.analysis), SQL (window functions), and Scala/Spark for petabyte-scale data.- Advanced Metrics: Kullback-Leibler divergence, Mantissa arc tests, and sequential analysis.- Edge Cases Demystified: When to avoid Benford (assigned IDs, bounded ranges).- Scalability Tactics: Approximate algorithms for streaming data and distributed systems.
Real-World Case Studies- Quant Fund: Detecting spoofed trades in limit order books.- E-Comm Platform: Uncovering fake reviews via rating distributions.- Health Tech: Validating clinical trial data integrity. For Whom?- Quants & Traders: Screening market data for manipulation.- Data Engineers: Building validation layers in ETL pipelines.- MLOps/Data Scientists: Stress-testing model inputs/outputs.- Auditors & Risk Officers: Automating forensic workflows.- Academic Researchers: Statistical foundations and extensions.
This book provides a comprehensive guide to applying Benford's Law in real-world scenarios, with code-ready insights and technical deep dives.
