Detecting fraud starts with having a clear picture. And traditional fraud detection methods aren’t cutting it, says Data Scientist Garrett Pedersen: “We need to change the way that we are solving problems in order to stay ahead of fraud and abuse.” The typical approach: Try to spot fraud on relational tables in Excel or SQL. A better approach: Graph data. Whether you’re familiar with graph data or new to it, Garrett breaks down the basics in this video. 𝗔 𝗙𝗲𝘄 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮 ⛛ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Relational tables can be complex and expensive, but graph data simplifies the process of spotting connections. ⛛ 𝗖𝗹𝗲𝗮𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Graphs show how entities (nodes) are connected through relationships (edges) more clearly than relational tables, allowing for better visualization and understanding of data. ⛛ 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Graph data can reveal potential fraud scenarios that might be hidden in traditional relational tables. If you want to learn more about fraud detection methods, check out our articles at elderresearch.com/blog. #DataScience #DataAnalytics #FraudDetection
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Looking for a cost-effective, scalable alternative to SAS Visual Investigator? DataWalk offers advanced analytics and seamless security intelligence to enhance your fraud detection efforts. #FraudDetection #SecurityIntelligence #DataAnalytics https://hubs.li/Q02NN-lL0
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Cybersecurity Evangelist | Offensive Security Consultant (Red Team Operator) | Information Security Analyst | Penetration Tester | Grey Hat Hacker
Introduction to forensic data carving https://lnkd.in/eWXueYBw
Introduction to Forensic Data Carving | – #1 Hacking in... simple Greek –
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📊 Exploratory Data Analysis on Credit Card Fraud Dataset 🕵️♂️💳 I recently conducted an in-depth analysis on a credit card fraud dataset, aiming to understand patterns and trends within the data. Here's a brief overview of the steps I took: Data Cleaning: Checked for missing values and handled them appropriately. Ensured consistency in data types and formats. Removed any duplicate records. Addressed any outliers or anomalies in the dataset. Exploratory Data Analysis (EDA): Explored the distribution of transaction amounts and identified potential outliers. Analyzed the temporal patterns of transactions over time. Investigated the distribution of fraud and non-fraud cases. Examined correlations between features and the target variable. Visualized the characteristics of transactions associated with fraud. Insights and Discoveries: Discovered interesting patterns in transaction amounts and frequencies. Identified potential time-based trends that could be indicative of fraud. Gained insights into the distribution of fraud cases and their characteristics. Uncovered correlations that may aid in feature selection for modeling. Next Steps: Planning to implement machine learning models for fraud detection. Considering feature engineering and additional preprocessing steps. Open to collaborations or discussions on further analysis. Your comments and insights on this analysis are highly appreciated! Let's continue the conversation.#DataAnalysis #CreditCardFraud #MachineLearning #DataScience #FraudDetection #DataCleaning #ExploratoryAnalysis #TechInnovation #DataInsights #DataScientists #CodeAnalytics
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|Business Analyst | Data Analysis | Data Engineering | Licensed Realtor | Collating | Python | R | SAS | SQL | Cloud | VBA | Tableau | Power BI | reporting analyst| MS Office |
Data Visualization Target Variable Visualization (Class) : fraud = len(data[data['Class'] == 1]) / len(data) * 100 nofraud = len(data[data['Class'] == 0]) / len(data) * 100 fraud_percentage = [nofraud,fraud] fig,ax = plt.subplots(nrows = 1,ncols = 2,figsize = (20,5)) plt.subplot(1,2,1) plt.pie(fraud_percentage,labels = ['Fraud','No Fraud'],autopct='%1.1f%%',startangle = 90,colors = colors, wedgeprops = {'edgecolor' : 'black','linewidth': 1,'antialiased' : True}) plt.subplot(1,2,2) ax = sns.countplot('Class',data = data,edgecolor = 'black',palette = colors) for rect in ax.patches: ax.text(rect.get_x() + rect.get_width() / 2, rect.get_height() + 2, rect.get_height(), horizontalalignment='center', fontsize = 11) ax.set_xticklabels(['No Fraud','Fraud']) plt.title('Number of Fraud Cases'); The data is clearly highly unbalanced with majority of the transactions being No Fraud. Due to highly unbalanced data, the classification model will bias its prediction towards the majority class, No Fraud. Hence, data balancing becomes a crucial part in building a robust model.
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🚀 Excited to Share My Latest Project: Exploratory Data Analysis on Credit Card Fraud Detection! 💳🔍 I’m thrilled to announce the completion of my recent project focused on Exploratory Data Analysis (EDA) in the realm of credit card fraud detection. This project involved: Data Cleaning: Addressing missing values and outliers to ensure the quality of data. Feature Analysis: Identifying key features that contribute to fraudulent transactions. Visualization: Creating insightful visualizations to understand patterns and trends in fraudulent activities. Statistical Analysis: Applying statistical methods to detect anomalies and trends in transaction data. 🔎 Key Insights: •Model Accuracy and Real-time Performance: Ensure the fraud detection model is highly accurate with low false positives (avoiding unnecessary customer friction) and low false negatives (avoiding missed frauds). •Regular Audits and Adaptation: Continuously monitor and audit model performance, and adjust detection criteria based on new data. This project was a deep dive into data analysis techniques and how they can be applied to enhance security in financial transactions. I'm excited about the potential of these insights to contribute to more robust fraud detection systems. I want to be thankful to GeeksMetrics The working of project you can be see in my GitHub Repository : https://lnkd.in/g8yUsjGq #DataScience #ExploratoryDataAnalysis #CreditCardFraud #DataVisualization #MachineLearning #Analytics #GeeksMetrics #Project Here the project pdf :
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Graph Analytics: The New Game-Changer For Fighting Fraud Graphs fundamentally change how users interact with data, enabling them to understand the context extracted from the relationships between entities, places, accounts, and so forth. Graph is increasingly critical in fighting frauds, and in this DataWalk white paper you will learn: - How graph analytics is different from the traditional approach. - Why graph analytics is better for Anti-Fraud investigations - How graph analytics enables you to detect new types of frauds based on connections in your data https://lnkd.in/ewkgK2uc
Whitepaper - Graph Analytics: The New Game-Changer For Anti-Fraud
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Just finished reading a great article by Jesse La Grew on the importance of keeping enrichment data fresh. Regularly updating and verifying data sources is essential for accurate threat detection. Thanks to Jesse for sharing this valuable info https://lnkd.in/dATnZBs3
Enrichment Data: Keeping it Fresh
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THE LANGUAGE OF DATA FORENSICS: What can data forensics monitoring uncover? 1- STATISTICAL ANOMALIES: Unusual patterns in the data that may or may not reflect violations or fraud. Example: Examinees demonstrating two different levels of proficiency. 2- TESTING IRREGULARITIES: Out-of-the-ordinary events occurring during testing, not always violations. Example: A fire alarm disrupts test takers during the testing window. 3- SECURITY VIOLATIONS: Events that violate testing protocol and could potentially threaten test security. Example: The seats in the testing room are too close together. 4- SECURITY BREACHES: Exam integrity has been jeopardized, though not necessarily on purpose. Example: Administrator accidentally discloses exam content to examinees. 5- TEST FRAUD: An intentional act that threatens the integrity of an exam. Example: Administrator intentionally discloses answers to examinees. https://lnkd.in/dAfyd2dt
Certificate for 4. Caveon Data Forensics
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This article is a good read for the anti-fraud minds. It gives a quick overview of data driven anti-fraud techniques 👮♂️ . https://lnkd.in/e3UTrW_a
7-wonders-of-anti-fraud-analytics
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Business Mentor | CEO and Founder of ParJenn Technologies | CyberSecurity 🛡️| Cloud ☁️| Managed Services 🛎️
Exploring the Impact of Human Error on Data Spillage Incidents
Exploring the Impact of Human Error on Data Spillage Incidents
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3motalking about fraud, I just received a recruiting email from [email protected], can you confirm this is from legit hr source?