Over the last decade, machine learning (ML) technology has become an invaluable tool in many industries, but it has become especially crucial in the finance industry.
But why does the finance industry have a particular interest in machine learning?
Just like in other fields when financial companies employ machine learning in their operations, they improve their efficiency and as a result, increase their profits. More specifically, machine learning allows companies to replace outdated and inefficient processes and helps them to develop new and more effective solutions.
This article will delve into some crucial ways that machine learning is changing the financial sector with examples of how companies are using ML applications within their businesses.
Security & Fraud Prevention
Security is one of the most critical concerns for financial companies. With more transactions, more users, and third-party app integrations, security threats are becoming more and more common.
As a result of this increasing threat, the finance industry has turned to machine learning as this technology is highly effective at detecting possible threats and fraudulent behaviour.
Banks that utilize machine learning techniques can simultaneously monitor and review thousands of transactions from every client’s bank account. For every activity that occurs on a client's account, the algorithm can determine if that action is characteristic of the account owner.
Machine Learning Techniques
Because ML systems learn the patterns of behaviour of each client, they can very quickly and with extreme accuracy identify fraudulent activity, i.e., when specific actions don’t follow the client's previous patterns. When this occurs, the system responds by requesting further information to verify the identity of the user, or blocks the transaction from being completed.
Within seconds, machine learning software can assess all bank account activity and prevent fraud from occurring in real-time before it is too late.
Some well-known fintech companies that heavily invest in security machine learning include Adyen, Payoneer, Paypal, Stripe, and Skrill.
Automation of Multiple Procedures
One of the most common applications for machine learning is process automation. Because ML programs have extensive learning capabilities, they can be virtually taught anything which allows them to replace and automate many manual processes.
By using ML algorithms to automate processes, companies can boost their productivity, create a better customer experience and optimize their costs.
Below are the most common examples of how machine learning is used for automation in the finance industry:
- Bookkeeping and processing paperwork.
- Gamification of employee training.
Insurance Underwriting and Credit Scoring
- ZestFinance uses their platform Zest Automated Machine Learning (ZAML) which has thousands of data points to help financial institutions re-assess loan applicants who would typically be classified as high risk due to very little or non-existing credit history.
- The insurance company Lemonade employs machine learning as well as chatbots to perform various tasks like processing insurance claims. By downloading their app to their mobiles, customers can file a complaint, pay their insurance and modify their policy. According to Lemonade, their ML algorithm allows the app to take only 90 seconds to insure someone and only 3 minutes to pay out a claim.
- They have unrealistic expectations of machine learning and how it can contribute to their organization.
- The research and development stage that is required to implement ML is costly.
- The demand for DS/ML engineers far exceeds the supply.