Machine Learning in Finance
Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power.
Machine learning in finance is reshaping the financial services industry like never before.
Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimize portfolios, decrease risk and underwrite loans amongst other things.
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.
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.
