Machine_Learning and Fraud Detection . pptx

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1. FRAUD Detection Using ML

2. A genda Definition Of ML 01 Uses Of ML 03 Fraud Detection Using ML 04 Fraud Detection Events 05 Definition Of ML 02 Outro 07 DataSet Analysis 06

3. Machine Learning Machine Learning (ML) is a subset of artificial intelligence (AI) that allows computers to learn from data and make decisions or predictions without being explicitly programmed. Instead of following specific instructions for every task, machine learning algorithms analyze large datasets, identify patterns, and improve their performance over time as they are exposed to more data.

4. Natural language processing e.g., chatbots, sentiment analysis Image and speech recognition e.g., facial recognition, voice assistants Autonomous vehicles e.g., self-driving cars Healthcare e.g., disease diagnosis, personalized treatments Finance e.g., stock market prediction, personalized banking Uses Of Machine Learning

5. Fraud Detection Evolving Tactics : Fraud tactics continuously adapt, making detection a moving target. Data Overload: The vast volume of transactions can overwhelm traditional detection systems. False Positives : Misidentifying legitimate transactions as fraudulent can harm user experience. Speed vs. Accuracy: Balancing rapid detection with accuracy remains a critical challenge.

6. 01 02 03 PayPal: Uses ML to analyze customer behavior, detecting fraudulent transactions with high accuracy. JPMorgan Chase began integrating machine learning algorithms to identify fraudulent credit card transactions. The bank used historical transaction data to train their models, which helped them spot anomalies and potential fraud faste r than traditional methods. Facebook implemented machine learning models to detect compromised accounts and fraudulent activities, such as fake accounts and phishing attacks PayPal: Facebook Some Popular Use Cases Of ML In Fraud Detection ~~

7. High volume of transactions and the difficulty of manual monitoring. Sophistication of fraudulent activities. The need for real-time detection. Challenges in Traditional Fraud Detection Feature Engineering in Fraud Detection Add your words here,according to your need . ~~

8. Add your title Add your words here Add your words here Add your title Add your words here Add your words here DataSet Analysis

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11. Compiled && Presented By Name: Instructor: Field Of Study: