Fraud Detection on Financial Transactions with Machine Learning on Google Cloud

Fraud Detection on Financial Transactions with Machine Learning on Google Cloud

Fraud Detection on Financial Transactions with Machine Learning on Google Cloud Taught in English Instructor: Google Cloud Training Included with • Learn more Project Build in-demand job skills with step-by-step instructions 3.8 (12 reviews) Intermediate level Some related experience required 1.5 hours Learn at your own pace No downloads or installation required Only available on

Description

Fraud Detection on Financial Transactions with Machine Learning on Google Cloud

In recent years, fraud detection has become increasingly important in the financial industry. With the rise of online transactions and digital payments, financial institutions are facing new challenges in safeguarding customer data and preventing unauthorized access to sensitive information. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool in detecting and preventing fraud in financial transactions. With the advent of cloud computing services like Google Cloud, financial institutions now have access to advanced machine learning algorithms and tools to enhance their fraud detection capabilities.

Machine learning uses algorithms to analyze large volumes of data and identify patterns that may indicate fraudulent activity. These algorithms can process millions of transactions in real-time, making it possible to detect suspicious behavior and flag potentially fraudulent transactions before they

Fraud Detection on Financial Transactions with Machine Learning on Google Cloud

Taught in English

Google Cloud Training

Instructor: Google Cloud Training

Included with Coursera Plus

Project

Build in-demand job skills with step-by-step instructions

3.8

(12 reviews)

Intermediate level
Some related experience required
1.5 hours
Learn at your own pace
No downloads or installation required
Only available on desktop
Hands-on learning

What you’ll learn

  • Load data into BigQuery and explore and create new features in BigQuery.

  • Bbuild an unsupervised model for anomaly detection.

  • Build supervised models (with logistic regression and boosted tree) for fraud detection.

  • Evaluate and compare the models and select the champion and use the selected model to predict the likelihood of fraud on a test data.

Skills you’ll practice

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