Machine Learning - Finance

Machine learning is one of those technologies that seems to have a limitless capacity to affect change. It’s a sort of technological King Midas, able to turn everything it touches into algorithmic gold. Recent innovative implementations include everything from fraud prevention to agricultural systems that enable farmers to manage crops on a plant-by-plant basis.

Part of machine learning’s appeal lies in its fundamental agnosticism; it can be used in virtually any field, and towards virtually any purpose. Because of this, demand for those with machine learning skills is growing at a remarkable pace. Machine learning strategies are also emerging as an effective way for companies to gain marketplace advantage, thus rendering machine learning talent all the more sought-after. Additionally, new startups powered by machine learning and related technologies are launching with increasing frequency, further serving to widen the impact. We’ll highlight one such story below, in which a graduate of Udacity’s Machine Learning Nanodegree program used the skills he learned in the program to launch a new financial services startup.

Implementing Machine Learning

A recent survey conducted by MIT Technology Review Custom and Google Cloud surfaced a number of key themes about machine learning’s growing impact. The following is excerpted from the survey:

  • ML is happening now. The majority of respondents (60 percent) have already implemented ML strategies, and nearly one-third considered themselves to be at a mature stage with their initiatives.
  • ML provides marketplace advantage. According to respondents, a key benefit of ML is the ability to gain a competitive edge, and 26 percent of current ML implementers felt they had already achieved that goal.
  • Organizations are investing in ML. Among current ML implementers, some 26 percent reported that more than 15 percent of their IT budgets was dedicated to ML initiatives.
  • Early adopters are realizing ML’s biggest potential benefits. The top hoped for benefit among ML implementers and planners is the ability to extend data analysis efforts and increase data insights. Some 45 percent of respondents report success in meeting that goal. In addition, more than half of both early-stage and mature-stage users say their ML efforts have resulted in demonstrable return on investment (ROI).
  • ML implementers are pursuing a broad range of projects. The most common projects among current ML implementers are image recognition, classification, and tagging (47 percent); emotion/behavior analysis (47 percent); text classification and mining (47 percent); and natural language processing, or NLP (45 percent).

Powering Up With Machine Learning

In addition to those instances where an existing organization moves to implement machine learning strategies, we are also starting to see new companies launching because of machine learning. Udacity recently published a post about just such a company. Gil Akos, the post’s author, is the co-founder of a new financial intelligence startup called Astra. He is also a graduate of Udacity’s Machine Learning Nanodegree program. In his post, entitled Launching Astra: How Deep Learning helped us launch our Financial Intelligence startup (the first in his series on going from Udacity student to startup founder), Gil describes how he went from dreaming of new financial tools to building them, and how enrolling in Udacity’s program was a critical step on the path towards launch.

We’ll be keeping a close eye on Astra in the coming days. They’re fielding a booth in Startup Alley at TechCrunch disrupt, and vying for a coveted Wild Card place in the Startup Battlefield competition!

Machine Learning Skills

Whether you’re looking to land a job as a machine learning engineer, or launch a startup powered by machine learning strategies, you’re going to need to master some key skills. Arpan Chakraborty, Content Developer for Machine Learning and Artificial Intelligence courses at Udacity, highlights some of the most important of these in a post entitled 5 Skills You Need to Become a Machine Learning Engineer:

  1. Computer Science Fundamentals and Programming
  2. Probability and Statistics
  3. Data Modeling and Evaluation
  4. Applying Machine Learning Algorithms and Libraries
  5. Software Engineering and System Design

Arpan’s post concludes with these words:

The world’s challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems. If this is YOUR future, then there’s no time like the present to start mastering the skills and developing the mindset you’re going to need to succeed.

Going back and reading the conclusion to Gil’s post about the origin story of Astra, you’d almost think he’d read Arpan’s article:

Udacity gave us the skills to build a more powerful tool — a telescope. An instrument that lets us see financial stars more clearly, and interpret them more confidently.

Sam and I founded Astra as a financial intelligence company with a mission to make your finances more tangible, empowering, and personal. We believe that developing smart technology, powered by deep learning, can help everyone improve their financial health.

The Future of Machine Learning

If you’re interested in understanding the full measure of how machine learning can and will impact our world, a highly recommended report is the McKinsey Global Institute’s 2016 study The Age of Analytics: Competing In A Data-Driven World, published in collaboration with McKinsey Analytics. At 136 pages it’s a deep dive, but you can get a free download of the Executive Summary that sums up the findings in a mere 28 pages. If that’s still a bit much for you, you might want to just tune in to Startup Battlefield at TechCrunch Disrupt. There will be a great deal of machine learning, deep learning, and artificial intelligence on display, and judging by the track records of previous winners, the King Midas effect should be in full effect.

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