Increase your Profitability by 38%
In this white paper, you will learn:
- How machine learning reduces your operating costs and boosts productivity
- The advantages of machine learning algorithms in streamlining process
- How machine learning will disrupt and create new opportunities of healthcare, financial, legal, and manufacturing industries
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Machine Learning Use Cases are Infinite
Machine Learning is so versatile that almost any gripe you have, whether it is an operational workflow or customer retention strategy, can be improved through its use. Machine Learning enables us to work on more important tasks while letting machines take care of the legwork. Below is a sample of the uses of Machine Learning that you’ll find in our Free white paper.
For the majority of healthcare, AI and ML are set to improve work-life balance, the patient experience, and results of invasive surgeries. Diagnosing will also look different in the next decade; costs should decrease, while ML improves accuracy rates. Right now, ML algorithms are making correct diagnoses at excellent rates, recommending medications, predicting patient re-admissions, and alerting doctors to high-risk patients.
And the algorithms don't need any identifying information to make these predictions -- all data is anonymized.
Smarter Financial Analysis
The finance industry has no shortage of data; it's probably the industry responsible for creating the most data since the beginning of time. All of this multi-dimensional data is a treasure trove for AI and ML applications. Algorithms are already being used in algorithmic trading, fraud detection, and portfolio management. Algorithms will also fuel the adoption of chatbots and chat interfaces for customer service, sentiment analysis, and security in financial institutions like banks and advisory firms.
Machines building Machines
In manufacturing, AI and ML are helping factories and enterprises take charge of cheaper, more frequent predictive maintenance over corrective and preventative maintenance. ML can pinpoint which machines often have a high unexpected failure rate by using historical machine data, analyzing the data against a dynamic environment, visualizing workflows, and creating better operational feedback loops.