Machine Learning: Finance and IT’s New Equilibrium
Whether it’s determining whether or not to give a consumer a hefty loan or denying access to an account to prevent a cyberattack, machine learning can definitely be pinpointed as the “mastermind behind it all”. However, as we begin to unveil the true capabilities of artificial intelligence, we can begin to explore deep learning - a new way to bring FinTech .
Machine Learning and its role in AI
Machine Learning (ML) is a branch of Artificial Intelligence that focuses on the ability of a computer to perform a task without having been explicitly programmed for said task. Machine Learning has 3 key components: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised learning is the notion where the machine is taught certain commands and tasks and the machine is expected to perform tasks by classifying input data. Unsupervised learning is the opposite where the machine isn’t given commands but instead is given algorithms to analyze unclustered datasets which can assist in customer segmentation and exploratory data analysis. Reinforcement Learning can be classified as a method of learning where the computer is placed in a more free range environment where it is “rewarded” or “punished” with desired results based on its actions. Human behavior is more closely related to reinforcement as this is how humans are psychologically apt to behave.
Machine Learning’s Role in Finance
Machine learning can play a key role in algorithmic trading decisions. Traders typically construct models to monitor news coming in from the business world and it can detect causes for the upward or downward trends of security prices (prices determined by supply and demand). Another advantage is that with their immense capability to analyze mass volumes of data, computers can then make quick decisions in the market, giving them an additional leverage. With machine learning, computers can make objective decisions based on facts, restricting human desires/biases to come into play when making trading decisions. Trading can utilize reinforced learning as the trading market is more exploratory and it can adapt with the knowledge it gained through time and experience. As far as the ‘reward’ aspect would go for reinforcement learning in trading, the computer would use profit margins.
Fraud detection is another aspect that fintech and financial firms are continuing to make more and more efficient. Doing so would save these corporations billions of dollars. Unsupervised learning can use outlier detection to spot and alert about any atypical behaviors/patterns. Different types of outliers can be identified. For example, point outliers are data points far skewed from the trend. Contextual outliers can be classified as abnormal background noise for speech recognition encryption.
Deep neural network based algorithms can calculate credit scores which banks can later use to credit bureaus. Probability of Default (PD) prediction is calculated prior to when customers go to a bank and request to take out a loan. DNNs can also partake in risk assessment for investors. Robo advisors are digital tools that can utilize an investor's financial goals and current data about the investor’s financial status to then present options that would optimize the investor’s objectives.
What does this mean for the future
The demand for the utilization of this $1.4 billion market isn’t projected to fall. As time progresses, ML will have more volumes of data to be able to learn and adapt , which can contribute to more in depth techniques like hyperparameter tuning. While Machine Learning is only being seen at the corporate level, it is sure to play an important role in consumers’ lives in the years to come.