Businesses and financial institutions are facing a profound challenge. They’re gathering much more data, from their apps, social media, IoT sensors, etc., than they could possibly process and act upon. Lacking analytical capabilities, and trained staff, they can’t help throwing significant value out the window and failing to monetize a significant asset at their disposal.
This has been a problem ever since the advancement of the technology for generating data outpaced the tools for mining it. And for a few years now, it’s been becoming worse.
The remedy, however, seems about to emerge; it lies within the increasingly trendy AI and Machine Learning technology. In this post, we’ll explore some of the use cases for Artificial Intelligence in finance and discuss, in moderate detail, the three existing types of machine learning in finance.
Machine Learning (ML) is the science of getting computers to “evolve by themselves,” without being explicitly programmed. It’s built around two principles – training computers by feeding data to them and letting algorithms to find ways to answer questions based on patterns they spot within datasets.
In traditional software engineering, if there is such a thing at all, we write programs, with explicit instructions drafted in code, that we input to our computers. These programs then take data of some sort and produce for us appropriate outputs.
In the case of Machine Learning, however, we turn this process on its head and start with feeding the output to our machines. We show them examples of labeled data or, say, some characterizations of different classes of items thus demonstrating the results we want to obtain. We let the computers figure out the program that can produce the necessary outcomes and later use that program to infer information from other instances of data.
Machine Learning and AI, in general, are being adopted for a wide range of applications in finance, excelling especially at fraud detection and stress testing. The ML algorithms banks now use, build upon familiar data science methods, such as linear regression, to handle millions of outputs, and utilize statistical methods to compress and summarize huge datasets. They show vast promise when their pitfalls, such as the lack of auditability, are accounted for and managed properly.
Related: How Perfectial Helps Ayasdi Utilize the Power of Machine Learning, Topological Data Analysis and Big Data
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The post The Three Types of Machine Learning in Finance and How to Apply Them appeared first on Software Development Company Perfectial.