Machine learning is transforming the finance industry with next-gen innovations and major changes in its operations. The finance sector is implementing machine learning to automate elaborate processes, enhance security, and provide a better customer experience.
Financial services are now integrating Artificial Intelligence due to its ability to perform tasks just like humans, even when lots of raw unstructured data is involved. Machine Learning is most commonly employed in finance as it can handle large amounts of data and automate it in particular financial tasks.
Machine learning and artificial intelligence contribute to enhanced accuracy, automate data analysis, lower costs, interpret financial procedures, and improve customer experiences in accounting and finance. Today, AI and ML are undergoing tremendous technological advancements, where the machines are integrated with functions similar to humans, making every task easy.
The success of machine learning in financial systems depends on creating efficient infrastructure, collecting the correct datasets, and using accurate algorithms.There are a variety of ML algorithms and tools capable of handling financial data. Moreover, financial companies today can afford to spend on such sophisticated technologies, adding value to the financial sector. That is why many financial companies are investing heavily in Machine Learning.
This article will walk you through major machine learning use cases in the finance sector. Read on!
Machine learning use cases in finance
Process Automation
Process automation automates complex business procedures. It enables automating processes, replacing manual work, and enhancing productivity. In the process, machine learning limits the bottlenecks, reduces errors, optimizes costs, and improves customer experiences. It also aids in augmenting transparency, processing speed, and communication across all the departments.
Algorithmic trading
Algorithmic trading uses a computer program that follows a specific set of instructions for better trading decisions. Using algorithmic trading can generate profits by analyzing multiple data sources simultaneously which is practically impossible for a human. The instructions are based on price, timing, quantity, and a mathematical model. Machine learning algorithms give humans an edge over the market average, generating huge profits in these trading activities.
Robo-advisory
Robo-advisors are online services that provide automated financial advice to investors, without any human supervision. Portfolio management establishes a portfolio depending on the user’s financial assets and goals, and risk tolerance, and then allocates the assets across investment opportunities. The Robo-advisors offer enhanced security features, better customer service, robust goal planning, and portfolio management.
Loan Underwriting
Underwriting is the process where the lender verifies the assets, income, debt, and property details for the final approval of the loan application. These underwriting and credit-scoring tasks in finance are processed using machine learning algorithms. Large companies have massive consumer data, to train machine learning models for financial lending and insurance results. Such systems aid humans to work faster and accurately. It looks like machines will soon replace the underwriters.
Fraud detection
Fraud is a big problem in finance and insurance companies accounting for huge losses every year. Large amounts of financial data are online, giving scope to various security breaches. With technological advancements, machine learning is increasingly used to deal with such frauds. In the past, financial fraud detection systems revolved around a robust set of rules, which fraudsters managed to override easily. Therefore, most companies nowadays use machine learning to deal with fraudulent financial transactions.
Finally
With technological advancements, artificial intelligence and machine learning are transforming the financial sector. Financial companies will remain competitive if they focus on these innovations. The use of machine learning in finance is resulting in enhanced accuracy, better decision making, faster analysis, fewer human errors, and augmented consistency. While using artificial intelligence and machine learning algorithms, financial companies need to consider a system that is capable of protecting the data against cyber-attacks and use it for effective execution of the further processes, providing the users’ confidence in the safety of their data.
