Thanks to the wealth of data that are increasingly available to banks and the general public, sophisticated algorithms are enabling improved processes in many areas of finance.
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A subfield of artificial intelligence (AI), machine learning (ML) enables systems to learn and improve independently without the need for explicit programming or human involvement. But ML only works when it has access to enormous volumes of data, allowing machines to be trained rather than meticulously programmed through line-by-line coding.
To do this, ML utilizes data on outcomes to figure out how to improve, make predictions, and describe information, which has led to major breakthroughs in almost every industry across the globe. Machine learning technology frees up a considerable amount of resources that would otherwise be spent on manual, repetitive tasks while increasing productivity, reducing errors, automating processes, and identifying trends and patterns.
Technologies such as the internet of things (IoT) and cloud computing are all growing implementations of ML. As a result, technology is changing the way financial businesses operate, as things that were once thought unimaginable have now been brought into the realms of possibility.
Unsurprisingly, one of the primary use cases for this new tech is in the financial sector, which greatly benefits from the ability to crunch huge data sets to secure important insights into market trends and forecasting fluctuations in financial assets.
With that said, the financial industry is finding a wide variety of use cases for AI and machine learning, from predicting cash flow events to detecting fraud and even improving the customer experience. On that note, let’s take a look at a few of the most widely implemented applications.
Machine learning and artificial intelligence (AI) solutions are transforming risk management in the financial sector. With this technology, banks and financial institutions can significantly reduce their risk levels by analyzing a massive volume of data sources to identify potential problem areas and make better, more informed decisions.
Banks, for example, employ machine learning to evaluate vast amounts of personal data to improve the accuracy and effectiveness of credit scoring, analyzing data sets such as prior lending operations, debts, marital status, financial behaviour of applicants, and more to help them determine whether or not to issue loans and open lines of credit.
Personalization and customer service
Artificial intelligence (AI) solutions can enhance customer experiences in the finance industry via chatbots, search engines, mobile banking, and financial health analytics. All of this helps provide more value to the customer, improve application processes, answer queries quickly, and reduce waiting times when trying to fix a problem.
AI solutions can also provide automated portfolio management and personalized product recommendations with little to no human supervision.
Making investment predictions
Through the use of sophisticated stock intelligence tools, machine learning-enabled technologies are able to provide advanced market insights that surface advanced data signals. These tools are far more efficient (and quicker) than traditional investment models, leading them to dramatically disrupt the investment banking industry.
Interestingly, as this technology becomes more widely available, it is no longer exclusive to hedge fund managers and larger financial institutions. Now, everyday traders are incorporating ML-based investment strategies in order to better predict the market and spot opportunities that would have been previously impossible to unearth at scale.
Robotic process automation
In the financial industry, robotic process automation (RPA) is an extremely useful tool that banks and other financial institutions use to replace human labour by automating repetitive activities with intelligent processes, leading to increased business productivity. This is one of the most widely used applications of AI and ML in the fintech sector and has been assisting businesses in gaining a competitive advantage over their competitors for quite some time. It is feasible to improve nearly any business activity by implementing this technology, resulting in improved customer experience, cost savings, and the capacity to scale up services.
In addition, according to McKinsey’s research, we are about to enter the second phase of AI-enabled automation. It’s predicted that machines and software bots will carry out 10% to 25% of tasks across various bank processes, increasing total capacity and allowing employees to focus on higher-value projects and initiatives.
As AI and ML technologies continue to improve, it’s almost certain that we will begin to see them play an increasingly important role in different aspects of the financial industries, such as managing portfolios and predicting market movements, fine-tuning the customer experience, and preventing fraud and reducing risk.
Some experts even predicted that one day we could live in a world with a fully automated financial system, but it seems at this point we still have some way to go before that can be fully achieved.