Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale - Softcover
Synopsis
Reactive Publishing
Applied Fraud Detection with Python is a practical, systems-level guide to building modern fraud, anomaly detection, and AML infrastructure at scale.
Designed for analysts, data scientists, engineers, and financial professionals, this book goes beyond toy examples to focus on real operational constraints: noisy data, evolving fraud patterns, regulatory pressure, and the need for explainable, auditable models. You’ll learn how Python is used in production environments to detect suspicious behavior across transactions, users, networks, and time.
The book covers the full fraud detection lifecycle, from data ingestion and feature engineering to statistical baselines, machine learning models, and real-time monitoring systems. Emphasis is placed on anomaly detection techniques, behavioral modeling, graph-based fraud analysis, and scalable pipelines suitable for banks, fintech platforms, payment processors, and compliance teams.
Rather than treating fraud detection as a single model problem, this book frames it as an adaptive system, one that must balance precision, recall, latency, and regulatory transparency. Python’s ecosystem is used throughout to connect analytics, modeling, and deployment into cohesive AML and risk platforms.
What you’ll learn:
Designing fraud and AML systems as end-to-end pipelines
Statistical and machine learning approaches to anomaly detection
Feature engineering for transactional and behavioral data
Detecting fraud using time-series and network analysis
Building scalable, auditable fraud detection architectures
Managing false positives, drift, and model decay in production
Integrating fraud analytics into compliance and risk workflows
Who this book is for:
Fraud and AML analysts
Data scientists and machine learning engineers
Financial engineers and risk professionals
Developers building transaction monitoring systems
Anyone designing large-scale trust, risk, or compliance platforms
This book is not about quick wins or black-box models. It is about building durable fraud detection systems that survive scale, scrutiny, and adversarial pressure, using Python as the connective tissue between analytics, automation, and real-world financial operations.
"synopsis" may belong to another edition of this title.
Search results for Applied Fraud Detection with Python: Analytics, Anomaly...
Stock Image
Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 52604066-n
Stock Image
Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Print on DemandSeller: California Books, Miami, FL, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # I-9798241998897
Stock Image
Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 52604066
Stock Image
Applied Fraud Detection with Python
Print on DemandSeller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9798241998897
Buy New
£ 29.85
£ 5.02 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
Applied Fraud Detection with Python
Print on DemandSeller: PBShop.store US, Wood Dale, IL, U.S.A.
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9798241998897
Stock Image
Applied Fraud Detection with Python (Paperback)
Print on DemandSeller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Reactive PublishingApplied Fraud Detection with Python is a practical, systems-level guide to building modern fraud, anomaly detection, and AML infrastructure at scale.Designed for analysts, data scientists, engineers, and financial professionals, this book goes beyond toy examples to focus on real operational constraints: noisy data, evolving fraud patterns, regulatory pressure, and the need for explainable, auditable models. You'll learn how Python is used in production environments to detect suspicious behavior across transactions, users, networks, and time.The book covers the full fraud detection lifecycle, from data ingestion and feature engineering to statistical baselines, machine learning models, and real-time monitoring systems. Emphasis is placed on anomaly detection techniques, behavioral modeling, graph-based fraud analysis, and scalable pipelines suitable for banks, fintech platforms, payment processors, and compliance teams.Rather than treating fraud detection as a single model problem, this book frames it as an adaptive system, one that must balance precision, recall, latency, and regulatory transparency. Python's ecosystem is used throughout to connect analytics, modeling, and deployment into cohesive AML and risk platforms.What you'll learn: Designing fraud and AML systems as end-to-end pipelinesStatistical and machine learning approaches to anomaly detectionFeature engineering for transactional and behavioral dataDetecting fraud using time-series and network analysisBuilding scalable, auditable fraud detection architecturesManaging false positives, drift, and model decay in productionIntegrating fraud analytics into compliance and risk workflowsWho this book is for: Fraud and AML analystsData scientists and machine learning engineersFinancial engineers and risk professionalsDevelopers building transaction monitoring systemsAnyone designing large-scale trust, risk, or compliance platformsThis book is not about quick wins or black-box models. It is about building durable fraud detection systems that survive scale, scrutiny, and adversarial pressure, using Python as the connective tissue between analytics, automation, and real-world financial operations. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798241998897
Stock Image
Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 52604066-n
Buy New
£ 29.84
£ 15 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 52604066
Buy Used
£ 32.36
£ 15 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
Applied Fraud Detection with Python (Paperback)
Print on DemandSeller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. Reactive PublishingApplied Fraud Detection with Python is a practical, systems-level guide to building modern fraud, anomaly detection, and AML infrastructure at scale.Designed for analysts, data scientists, engineers, and financial professionals, this book goes beyond toy examples to focus on real operational constraints: noisy data, evolving fraud patterns, regulatory pressure, and the need for explainable, auditable models. You'll learn how Python is used in production environments to detect suspicious behavior across transactions, users, networks, and time.The book covers the full fraud detection lifecycle, from data ingestion and feature engineering to statistical baselines, machine learning models, and real-time monitoring systems. Emphasis is placed on anomaly detection techniques, behavioral modeling, graph-based fraud analysis, and scalable pipelines suitable for banks, fintech platforms, payment processors, and compliance teams.Rather than treating fraud detection as a single model problem, this book frames it as an adaptive system, one that must balance precision, recall, latency, and regulatory transparency. Python's ecosystem is used throughout to connect analytics, modeling, and deployment into cohesive AML and risk platforms.What you'll learn: Designing fraud and AML systems as end-to-end pipelinesStatistical and machine learning approaches to anomaly detectionFeature engineering for transactional and behavioral dataDetecting fraud using time-series and network analysisBuilding scalable, auditable fraud detection architecturesManaging false positives, drift, and model decay in productionIntegrating fraud analytics into compliance and risk workflowsWho this book is for: Fraud and AML analystsData scientists and machine learning engineersFinancial engineers and risk professionalsDevelopers building transaction monitoring systemsAnyone designing large-scale trust, risk, or compliance platformsThis book is not about quick wins or black-box models. It is about building durable fraud detection systems that survive scale, scrutiny, and adversarial pressure, using Python as the connective tissue between analytics, automation, and real-world financial operations. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798241998897
Buy New
£ 33.49
£ 37 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available