How the Future Looks like for Machine Learning in Finance
Machine Learning in Finance
Machine learning has long fascinated finance practitioners, beginning with the “program traders” who first applied computers to stock picking. While they did not carry the title of machine learning developers, early program traders were, in fact, applying artificial intelligence to finance in the 1990s.
Now, advances in machine learning allows banks to detect fraud better and manage their funds more efficiently. In reality, machine learning is more than just a feature for financial institutions and players. The application of machine intelligence has led to fundamentally new businesses combining technology and finance.
History of Machine Learning
The field of machine intelligence emerged early in the history of computing. In the 1950s and on into the 1970s, artificial neural networks were already being used to solve certain problems. In 1997, machine intelligence claimed a great victory when a computer was able to beat the world chess champion in game-play. This was IBM’s Deep Blue computer, which beat Garry Kasparov and raised the profile of machine learning in technology circles.
When Application of Machine Learning Entered Finance
Machine learning in finance was eagerly sought after on Wall Street where huge sums of money were deployed in trading. The 90s coincided with the rise of “quants”, practitioners of statistical machine intelligence. Quant hedge funds like Long Term Capital Management deployed computer models to trade assets based on machine intelligence. Long Term Capital Management later went bust, but other hedge funds and investment banks would go on to use machine intelligence techniques in their trading.
Most Interesting Financial Applications of Machine Learning
Since the 1990s, the use of machine intelligence has spread throughout the finance industry. A number of advances are responsible for this adoption of these techniques.
First, the rise of big data technology has changed the computing power available to large financial companies. Big data allows the storage and machine processing of vast quantities of data in a very short amount of time. Working with these large datasets allows financial companies to test their strategies and refine them better than ever.
Secondly, the modeling techniques that are used in machine intelligence have improved in the last two decades.
Machine Learning Finance Techniques
For example, some of the techniques that have seen improvements in implementation across the board include:
- Naive Bayes classification – algorithms using Bayes’ Theorem
- Support vector machines – a classification technique that separates data sets along a machine-determined plane
- Monte Carlo simulations – algorithms that use repeated sampling of a data set to determine the long term potential of different states
- Markov chains – models in which the state of a system is determined by what occurs in the prior state
- Statistical arbitrage – financial trading strategies that use computational models to predict mean-reversion outcomes in asset prices
These techniques are now being employed broadly in areas of finance such as asset pricing, trading, and risk analysis.
At the same time, VCs have taken a keen interest, over the last 8 years, in startups that are advancing machine intelligence.
In 2017, as Crunchbase data shows, inflows of VC funding into machine learning startups totaled $5 billion. Back in 2010, this figure was less than half a billion dollars.
5 Companies’ Interesting Machine Learning Applications in Finance
Below, we take a look at how 5 companies are applying machine intelligence in 5 different sectors of finance.
#1 Investment Advisory Machine Learning and Finance
Schwab is one company that is applying machine learning to investment advisory and personal finance bots. The company offers a finance machine intelligence product called Schwab Intelligent Portfolios. This “robo-advisor”, according to the company, “builds, monitors and automatically rebalances a portfolio of low cost exchange-traded funds.” In other words, the computer algorithms automatically make smart recommendations to help end users achieve better investment results.
#2 Risk Management Machine Learning Applications
Kount is one of the companies applying financial machine intelligence to the areas of risk management and fraud detection. The company’s technology uses patented machine intelligence to identify and stop fraudulent transactions. In the process, it mitigates risks for a variety of businesses, including banks, online merchants, credit card companies, and others.
#3 Algorithmic Trading and Machine Learning in Banking
Walnut Algorithms is among the companies applying machine learning to algorithmic trading. The company provides statistical intelligence strategies that use smart algorithms to trade the financial markets.
On Wall Street, investment bank JP Morgan is among those applying machine intelligence to deal-making. According to BusinessInsider, JPMorgan has been spending big on predictive recommendation technology that smartly identifies the optimal equity strategies for clients. Using this technology, client can issue or sell equity at the best possible time to make the most money from deals. Other investment banks like Goldman Sachs are also making forays into applying machine learning for financial engineering work.
In the insurance area, Innovation Enterprise reports that adoption of machine learning helps lower costs. For example, the Japanese insurance company Fukoku Mutual Life Insurance developed an AI system that can intelligently derive insurance payouts. The company then reduced its staff size and was projected to save over 140 million Yen per year. The spend on machine learning developer salary is less than what the company saved by reducing its staff elsewhere due to the new technology.
#5 Bank Customer Service Application of Machine Learning in Finance
Yet another area in which finance has made use of machine intelligence is in bank customer service. AI company Kasisto has developed a machine intelligence product called KAI Banking. With this product, banks can use smart bots to provide bank customer service just as if the customer were talking to actual bank reps. The AI uses algorithms and speech recognition techniques to solve customers’ problems.
5 of the Most Promising Machine Learning Trends in Finance for 2019
JP Morgan has conducted research into how machine learning and big data will change the financial landscape. The report goes into significant depth and will be of great interest to practitioners in the field. It touches on the likely applications of advanced techniques such as Convolutional Neural Networks and Multi-layer Perceptrons to finance. This research sheds light on some of the developments that will occur in the field into 2019, 2020 and beyond.
Finance and Machine Learning Trends
Going forward to 2019, finance practitioners and technologists can look forward to the following trends:
- Big data investment strategies – There will be more trading approaches based on insights drawn from big data deployments. The progress in tools like Mahout, Spark, and Hadoop will make this easier.
- Deep learning and reinforcement learning – Artificial neural networks will be used to solve complex problems with computer intelligence.
- Unsupervised learning – This technique will be used to surface insights from unstructured data such as that encountered in parts of financial analysis.
- Classification and Regression Methods – These methods will be used to make predictions of financial trends based on sample data.
- Alternative data – The report points to the adoption of alternative datasets such as those generated in regular business processes. This data will be different from traditional market data but will contain hidden insights that machine intelligence algorithms can extract.
JPMorgan’s report singles out a number of programming languages as being important in the future of finance machine learning. Chief of these is R, followed by C++, Python, and Java.
When it comes to individual libraries and frameworks for machine intelligence in finance, JP Morgan points to the following. These are recommended for professionals to pay attention to, and their adoption is likely to increase in the industry:
- SciKit-Learn – A Python computer intelligence library built on top of SciPy.
- TensorFlow – Numerical computation library for artificial intelligence, available in languages like C++ and Python.
- NLTK – AI software to work with human language data.
- OpenCV – A C++ computer vision AI package.
- Caffe – Fast, deep-learning library built on C++.
- Weka – Collection of deep learning algorithms in the Java programming language.
- MXnet – Neural Networks in R.
PhD Machine Learning Finance Algorithms
A few techniques of computer intelligence are fast becoming standard fare in not only finance, but in academia and everywhere this new field is being applied. That means whether in finance, healthcare, self-driving cars, or other AI apps, developers need to be able to implement standard algorithms in a mix of programming languages. Generally, as alluded to, these would be in C++, Java, R, or Python. Useful algorithms to know include K-Means Clustering, Decision Trees, Neural Networks, Hidden Markov Model, Logistic Regression and Random Forests.
Skills of a Financial Developer
It stands to reason that a well-rounded computer intelligence developer should know some of the above languages and libraries. In addition, job postings on Indeed indicate what many financial companies are looking for in the perfect job candidate. Effective machine learning developers in finance are typically expected to show some combination of the following:
- Strong academic background in Computer Science, Math, Physics or Engineering.
- Prior experience and knowledge of financial markets
- Knowledge of APIs for working with market data
- Experience with artificial intelligence algorithms and techniques
- Knowledge of calculus, stochastic processes, and deep learning
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