Explaining the Basics of Machine Learning



Financial institutions are no strangers to Machine Learning. Many institutions are investing heavily in this technology to improve cyber security, customer segmentation, and marketing campaign management.

To simplify the discussion, think of machine learning as self-driving cars, practical speech recognition, and effective web searches. Machine learning is the science of getting computers to act and learn from data without being explicitly programmed. Machine learning works with data and processes it to discover patterns that can be later used to analyze new data. Industry experts refer to machine learning as “training” that requires sending large amounts of data to the algorithm (a process or set of rules to be followed in calculations or other problem-solving operations) and allowing the algorithm to adjust itself and improve.

There is no way a human can look at large volumes of data and make sense out of it. Even if it is possible, the data would be peppered with errors. Precisely why machine learning is considered a significant technological development that is so widely used in everyday situations that you probably experience without even realizing it. For example, have you visited an online store and looked at a product but didn’t buy it — and then saw digital ads across the web for that exact product for days afterward? Has your credit card been declined while you were traveling or on vacation? You may have been on the receiving end of machine learning.

Machine Learning Use Cases

Machine learning’s capabilities are proving to be particularly useful in identifying patterns across large volumes of customer and user data and helping drive better outcomes. Here are a few use case examples of its impact on certain industries.

Fraud Detection: Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. PayPal uses tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.(1)

Automotive Industry: Self-driving vehicles could lead to a safer, cleaner, more efficient future for transportation. Software developers use machine learning algorithms to power computer vision that allows the vehicle to make decisions in ways that are similar to human decision making.(2)

Health Care: In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist medical experts in making a diagnosis. Machine learning has recently made headlines by helping to identify cancerous tumors on mammograms and to identify skin cancer. In a trade medical report, the results of a deep machine-learning algorithm helped diagnose diabetic retinopathy in retinal images. (3) Additionally, machine learning can be used to understand risk factors for disease and assist physicians by more effectively diagnosing and treating patients.

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