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Uncovering Fraud Profitable Shorting Ai Techniques For Spotting Red Flags In Public Companies(1st Edition)
Authors:
Jamie Flux
Cover Type:Hardcover
Condition:Used
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Book details
ISBN: B0DFVLGMLV, 979-8337999463
Book publisher: Independently published
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Book Summary: Discover The Pathway To Lucrative Profits By Mastering The Art Of Detecting Financial Fraud With Advanced AI Techniques. This Comprehensive Guide Offers You Cutting-edge Strategies To Spot Red Flags In Public Companies, Equipping You With Powerful Tools To Capitalize On Insights In The Financial World. With In-depth Python Code Examples In Every Chapter, This Book Is An Essential Asset For Data Scientists, Financial Analysts, And Anyone Eager To Harness Technology For Profit-driven Fraud Detection. Turn Knowledge Into Financial Gains And Stay Ahead Of The Curve With This Indispensable Resource.Key Features:- Master Anomaly Detection And Time Series Analysis To Discover Hidden Fraud Patterns- Utilize Natural Language Processing (NLP) To Interpret Financial Documents- Apply Both Supervised And Unsupervised Machine Learning Models Effectively- Explore Deep Learning, Including CNNs, RNNs, And GANs, For Sophisticated Pattern Recognition- Learn From Real-world Examples And Code Implementations In Python- Assess Risks With Monte Carlo Simulations And Bayesian Networks- Implement Cloud-based And Edge Computing Solutions For Scalable Fraud DetectionWhat You Will Learn:- Detect Anomalies In Financial Data Using AI Techniques- Uncover Fraudulent Patterns With Time Series Analysis- Analyze Textual Financial Data For Fraud Indicators Using NLP- Implement Supervised Learning For Classifying Fraudulent Activities- Use Clustering Techniques To Identify Fraudulent Data Sets- Employ Deep Learning Models For Financial Pattern Recognition- Utilize RNNs For Analyzing Sequential Financial Data- Apply CNNs To Interpret Image-like Financial Datasets- Generate Synthetic Fraud Scenarios With GANs For Robust Training- Extract Crucial Features Using Autoencoders For Fraud Risk Areas- Classify Data With Decision Trees To Distinguish Fraud- Increase Detection Accuracy Using Random Forests- Leverage XGBoost For High-performance Fraud Detection- Utilize SVMs To Effectively Separate Fraudulent Transactions- Identify Hidden Data Structures With Hierarchical Clustering- Apply PCA For Reducing Data Dimensionality, Preserving Key Indicators- Use ICA For Separation Of Signals Related To Fraud- Assess Fraud Risk Through Probabilistic Modeling With Bayesian Networks- Implement Markov Chains For Predictive Fraud Analysis- Enhance Fraud Strategy Using Game Theory- Design Real-time Systems For Rapid Fraud Detection In Financial Operations- Scale AI Models To Handle Extensive Financial Data- Secure Data Management In AI-driven Fraud Systems
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