Machine learning has been a part of our world for years, but it’s presence and awareness (in our daily life) in the last 18 months has grown tremendously.  Finance is the industry that has most benefited from its insights in present-day applications, with even more dynamic innovation coming beyond 2018.

Industries with high volumes of data, and the need for transaction history are prime areas that can be enhanced by machine learning.  Within finance, artificial intelligence (the larger field that includes ML) has multiple current uses from loans, portfolio management, to risk management.  

-  Portfolio management has evolved to “robo-advising” in which companies, most notably Fintechs (e.g. Betterment, Wealthfront) use algorithms customized to users goals and risk levels. Customer would enter their specific data (such as age, income, goals, etc.), which would generate a recommended package of investments; the choices can self-adjust by market conditions and manually due to changes in the client’s life/goals.  

-  Algorithmic trading utilizes AI models to make quick trades (up to millions per day, referred to as HFT for high-frequency trading); the specific structure used are unknown but include both machine learning and deep learning. 

 -  Fraud detection has been upgraded by machine learning, using systems able to learn from vast amounts of data to uncover patterns / activities with high potential for fraud. The millions in losses from numerous paths in committing fraud, have created a huge need for technology to help save these costs. 

-  Loan / insurance underwriting uses ML algorithms (fed with volumes of customer and industry specific data) to determine lending and insurance decisions. Companies  most leveraging this technology have the budget to afford data centers and providers to properly train models for actual business use.  

As for future applications of machine learning being created now: 

-  Customer service ‘chat bots’ becoming quality voice assistants are seen as the next, upcoming application of AI, with companies adding natural language processing to data on servicing interactions, as an alternative to customers using online search or contact centers for help. 

- Security enhancements, beyond username and password credentials, will come in the form of biometric data such as voice or facial recognition; 

- Sentiment analytics are being created with machine learning in creating predictive models of human “intuition” for responses to social / economic factors in financial decisions (which impact stock market volatility).

As fintech companies focus on keeping up with new technology in their products and platforms, applying artificial intelligence will be critical.  The first companies to compile and properly feed data into machine learning models will lead this movement for new insights and improved customer experiences and help establish the industry standard.

 Thanks for reading!  Here are some other articles on machine learning for you to check out.