Remove addressing-fraud-with-machine-learning-how-why
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Addressing Fraud with Machine Learning: How & Why

Dataiku

For the financial services industry (as well as many others that deal with data security and other types of non-monetary fraud), anomaly detection is hands down the most important system in operation. Yet many organizations still use more traditional modeling for fraud or anomaly detection instead of making the shift to machine learning.

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6 trends framing the state of AI and ML

O'Reilly on Data

O’Reilly online learning is a trove of information about the trends, topics, and issues tech leaders need to know about to do their jobs. Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Unsupervised learning is growing.

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Streamlining supply chain management: Strategies for the future

IBM Big Data Hub

Here’s how companies are using different strategies to address supply chain management and meet their business goals. Why supply chain management matters Supply chain management involves coordinating and managing all the activities involved in sourcing , procurement, conversion and logistics.

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What is synthetic data? Generated data to help your AI strategy

CIO Business Intelligence

Synthetic data can also be a vital tool for enterprise AI efforts when available data doesn’t meet business needs or could create privacy issues if used to train machine learning models, test software, or the like. Which is unlikely to be obvious because then we’d use that as our fraud detector.”.

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Enhance your Lending with Predictive Analytics

BizAcuity

Important issues like ensuring customer loyalty, retaining and attracting different types of customers or cross-selling products suited to them, fraud detection, application screening, have been areas of concern in the unsecured consumer lending business. Predictive analytics has played a pivotal role in streamlining the lending process.

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What is a phishing simulation?

IBM Big Data Hub

In some cases, employees who click on the mock malicious link are brought to a landing page indicating that they fell prey to a simulated phishing attack, with information on how to better spot phishing scams and other cyberattacks in the future. Why phishing simulations are important Recent statistics show phishing threats continue to rise.

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Cyber recovery vs. disaster recovery: What’s the difference? 

IBM Big Data Hub

Through the development of cyber recovery plans that include data validation through custom scripts, machine learning to increase data backup and data protection capabilities, and the deployment of virtual machines (VMs) , companies can recover from cyberattacks and prevent re-infection by malware in the future.