Before we cover some Machine Learning finance applications, let’s first understand what Machine Learning is. Machine Learning (ML) is a part of data science that uses different models to analyze data and make predictions.
The cool thing about machine learning is that, just like how babies learn to walk and speak through experience, Machine Learning Software also learns how to analyze data from experience. You don’t need to explicitly teach it anything. You simply give it a set of data for which results are already known and let it process it to identify patterns in the data and corresponding results. Different ML models process this data in different ways. So, you have to decide on the right ML model to use in order to get the quickest and best results. Once it is done learning, you then feed new data into the model and let it automatically adjust itself to improve outcomes each time.
It’s needless to say that the more data you supply to your ML model, the more experience it gains and the better results it gives. Over time, these models have worked on enough data-result relationships to be able to give close to accurate results.
Machine Learning Finance Applications
The finance industry has been a pioneer in using AI technology. Since the 70s, Wall Street has been analyzing stock data to predict market prices. Machine Learning stock market applications are gaining momentum and continue to add more and more profitable features. Banks too have started using Machine Learning finance applications to meet their ever-growing needs.
There has been an explosion in the amount of data at our disposal. We not only have stock data and sales data, but also data from social media posts, data about people’s sentiments and personal preferences, and so forth. This data, if harnessed through the right AI techniques, can prove to be highly profitable to the Finance industry.
Application Areas and Use Cases
Today, the finance industry has a number of areas where ML is being applied. Here are just a few:
Risk Assessment and Fraud Detection: Companies like Mastercard are using ML for their ‘Decision Intelligence’ projects to discover patterns from historical shopping and spending habits of cardholders to detecting fraudulent activities. Thus, ML algorithms can detect anomalies that could easily go unnoticed by human analysts. They can also help improve the accuracy of real-time approvals and reduce false declines (which can incur bigger losses than actual fraud itself).
Process Automation: ML has now made it possible to replace manual work and automate repetitive tasks. Some areas where ML is being used in ‘Process Automation’ include Chatbots, call center automation, paperwork automation, and simulations for employee training. For example, Parascript uses OCR to process receipts and create data sets. They then use ML to automatically classify, locate and extract all key data so as to manage expenses, file taxes and analyze purchases.
Credit Scoring: Banks and insurance companies amass large amounts of data about consumers and their transactions. They can also acquire datasets from telecom and utility companies and then use all of this data to train ML models. These are then used to help employees to quickly complete their underwriting tasks or identify new credit-worthy borrowers. Examples of startups that provide ML credit scoring services include Zest Finance and Destacame.
Algorithmic Trading: Organizations like Sentient Technologies are applying ML in stock trading. They use ML to analyze news and trade results in real-time, detect patterns that may cause stock prices to go up or down, and then make appropriate trading decisions.
Robo-advisory / Portfolio Management: This is an interesting application of ML, which involves using algorithms to help individuals plan investments and analyze risks based on their financial portfolio. Using these, users enter their goals (eg: to retire at age 60 with 100,000 dollars in savings), age, income, and current financial assets. The robo-advisor then advises on the best areas to invest, based on this data, over the person’s lifetime. An example of such a robo-advisor would be Responsive.ai.
How to harness the power of Machine Learning in finance
Today, there are a bunch of stock market datasets available online, like Quantopia, Google Finance, and Kaggle. These can be combined with scraped data from social media and news sites to train ML models like Tensorflow, Keras, Scikit-learn (an introductory course I highly recommend), among others, to make predictions. There are a number of pre-trained ML models available too, like textblob and NLTK. These trained models make the process of training faster and help us get more accurate results.
The Finance Market is expected to grow from 1.3 billion in 2017 to 7.4 billion in 2022. In other words, this market is ripe and growing, and many organizations are already including ML integration in their company roadmaps. So, if you have an interest in both Machine Learning and finance, now is the best time to hop on the bandwagon and use your expertise to benefit both the finance industry and yourself.
If you’re looking for some resources to get you started with Machine Learning, take a look at these courses:
An Introduction to Scikit-learn
