Machine learning has proven useful because it can resolve problems at a rate and scale that cannot be reproduced by the human mind alone. Massive computational capacities make it possible to train a machine to identify patterns and relationships between input data and to automate routine processes.
Data Security: Machine learning models can identify data security vulnerabilities before they are breached. By reviewing experiences, machine learning models can predict future high-risk activities, so the risk can be proactively mitigated.
Finance: Banks, brokerages and fintech companies use machine learning algorithms to automate trading and to provide financial consulting services to investors. Bank of America uses the Erica Chatbot system to automate customer support.
Healthcare: Machine learning is used to analyze massive health datasets to speed up the discovery of treatments and remedies, improve patient outcomes, and automate routine processes to prevent human errors. For instance, IBM Watson uses data mining to provide clinicians with data they can use to personalize patient treatment.
Fraud detection: Artificial Intelligence is used in the financial and banking industry to autonomously analyze numerous transactions in order to detect fraudulent activities in real time. Technology services company Capgemini claims hat machine learning and analytical fraud detection systems reduce fraud investigation time by 70% and improve detection accuracy by 90%.
Retail: Artificial Intelligence researchers and developers use machine learning algorithms to develop Artificial Intelligence recommendation engines that provide relevant product suggestions based on the past choices of buyers, their historical data, geographical and demographic information.
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