How machine learning and AI are transforming the financial industry
With the wealth of data increasingly available to banks and the general public, sophisticated algorithms are helping to improve processes in many areas of finance.
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A subfield of artificial intelligence (AI), machine learning (ML) allows systems to learn and improve independently without the need for explicit programming or human intervention. But ML only works when it has access to huge volumes of data, allowing machines to be trained rather than meticulously programmed through line-by-line coding.
To do this, ML uses outcome data to determine how to improve, make predictions, and describe insights, which has led to major breakthroughs in nearly every industry across the globe. Machine learning technology frees up a tremendous 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 once thought unimaginable have now become possible.
Unsurprisingly, one of the primary use cases for this new technology is in the financial industry, which greatly benefits from the ability to process huge datasets to gain important insights into market trends and predict market fluctuations. Financial assets.
That said, the financial industry finds a wide variety of use cases for AI and machine learning, from predicting cash events to detecting fraud and even improving the customer experience. On that note, let’s take a look at some of the most widely implemented apps.
Machine learning and artificial intelligence (AI) solutions are transform 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 issues and make better, more informed decisions.
Banks, for example, are using machine learning to assess large amounts of personal data to improve the accuracy and efficiency of credit scoring, by analyzing datasets such as past loan transactions, debts, marital status, financial behavior of applicants, etc. to help them determine whether or not to issue loans and open lines of credit.
Personalization and customer service
Artificial intelligence (AI) solutions can improve customer experience in the financial industry through chatbots, search engines, mobile banking and financial health analytics. All of this helps deliver more value to the customer, improve application processes, respond quickly to queries, and reduce wait times when resolving an issue.
AI solutions can also provide automated portfolio management and personalized product recommendations with little or no human supervision.
Make investment forecasts
Through the use of sophisticated technologies stock market information tools, machine learning based technologies are capable of delivering advanced market insights that surface advanced data signals. These tools are much more efficient (and faster) than traditional investment models, leading to them being a major disruptor to the investment banking industry.
Interestingly, as this technology becomes more widely available, it is no longer reserved for hedge fund managers and large financial institutions. Today, day traders are incorporating ML-based investment strategies to better predict the market and identify opportunities it would have previously been impossible to unearth on a large scale.
Automation of robotic processes
In the financial industry, robotic process automation (RPA) is an extremely useful tool that banks and other financial institutions are using to replace human labor with automate repetitive activities with smart processes, leading to increased business productivity. It is one of the most widely used AI and ML applications in the fintech industry and has been helping businesses gain a competitive edge over competitors for some time. Almost any business activity can be improved by implementing this technology, resulting in improved customer experience, cost savings, and the ability to expand services.
Moreover, according to McKinsey Research, we are about to enter the second phase of AI-based automation. It is expected that machines and software robots will perform 10% to 25% of tasks in various banking processes, increasing total capacity and allowing employees to focus on higher value projects and initiatives.
As AI and ML technologies continue to improve, it is almost certain that we will start to see them play an increasingly important role in different aspects of the financial industries, such as the management of portfolios and forecasting market movements, improving customer experience and preventing fraud and reducing risk.
Some experts have even predicted that one day we could live in a world with a fully automated financial system, but it seems that at this point we still have a long way to go before this can be fully realized.