Machine Learning In Finance
Up until recently, the main users of AI and ML in finance were only hedge funds, but in recent years, ML applications have begun to spread to a variety of other industries, including banks, fintech, regulators, and insurance companies, to name a few.
The various use cases of Artificial Intelligence and Machine Learning are having a significant impact on the financial sector. From speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, etc.
Banks, trading firms, and fintech firms are rapidly deploying machine algorithms to automate time-consuming, mundane processes and provide a far more streamlined and personalised customer experience.
Artificial Intelligence in Finance
In finance, artificial intelligence (AI) and machine learning cover everything from chatbot assistants to fraud detection and task automation. According to Insider Intelligence’s AI in Banking report, most banks (80%) are well aware of the potential benefits of AI.
Financial Institutions are adopting Artificial Intelligence for technological advancements like, increased user acceptance, and shifting regulatory frameworks. Banks that use AI can greatly improve the customer experience by providing 24/7 access to their accounts and financial advice services.
How Does Machine Learning Work in Finance?
Machine Learning extracts meaningful insights from raw data sets and produces accurate results. It uses the data to solve complex and data-rich problems critical to the banking and finance industries.
Furthermore, machine learning algorithms can learn from data, processes, and techniques used to discover new insights.
Future Prospects of Machine Learning In Finance
Some machine learning applications in banking and finance are well known and readily apparent. These are chatbots and mobile banking apps, the ML algorithms and technology. These technologies are now gradually used for innovative future applications, by accurately extracting historical data from customers and projecting their future.
Aside from the well-established use cases of machine learning in finance, as discussed in the preceding section, there are a number of other promising applications that ML technology can provide in the future. While only a few of these have relatively active applications today, others are still in their infancy.
Future of AI in Financial Services
With rising consumer demand for digital offerings and the threat of tech-savvy startups, financial institutions are rapidly adopting digital services—by 2021, global banks’ IT budgets will reach $297 billion.
With millennials and Gen Zers quickly becoming the largest addressable consumer group for banks in the United States, financial institutions are pushing to increase their IT and AI budgets in order to meet higher digital standards. These younger customers prefer digital banking channels, with 78% of millennials never visiting a branch if possible.
Because of the expanding opportunities among consumers who are digital natives, traditional banking channels were already moving toward online and mobile banking prior to the pandemic. However, as stay-at-home orders were implemented nationwide and customers sought out more self-service options, the coronavirus dramatically accelerated the transition.
According to Insider Intelligence, online and mobile banking adoption among US consumers will rise to 72.8% and 58.1%, respectively, by 2024.
What’s Next for Machine Learning in Finance?
It includes asset management, risk assessment, investment advice, dealing with financial fraud, document authentication, and much more.
While dealing with a plethora of tasks, ML algorithms are constantly learning from massive amounts of data. This bridges the gap and bringing the world closer to a fully automated financial system.
A skilled machine learning services partner can develop and implement the appropriate models by focusing on particular data and business domains after a thorough understanding of the expected output that will be extracted from various sources, transformed, and produce the desired results. This is where the majority of financial institutions must start by identifying the right set of use cases.
How is AI driving continuous innovation in finance?
Financial processes such as data entry, data collection, data verification, consolidation, and reporting have traditionally relied heavily on manual labour. All of these manual activities contribute to the finance function being expensive, time-consuming, and slow to adapt. Simultaneously, many financial processes are consistent and well defined, making them ideal candidates for RPA in Finance.
With the introduction of ERP systems, businesses were able to centralise and standardise their financial functions. It could be subject to a set of rules for handling. While these systems automate financial processes, they necessitate extensive manual maintenance, are slow to update, and lack the agility of today’s AI-powered automation. In contrast to rule-based automation, AI can handle more complex scenarios, such as the complete automation of mundane, manual processes.
Increased automation means greater accuracy in your financial processes. In humans, high volume, mundane processes such as invoice entry can cause fatigue, burnout, and error. Computers, on the other hand, do not share these constraints. They can also process significantly more transactions in a given time period. As a result, the finance team has better data to work with and more time to focus on putting that data to use.
How to apply ML and AI in Finance?
Artificial intelligence and machine learning in finance will become more technically advanced and adaptable to business processes as technology advances. When selecting new technologies it is best to start with a single element, sort it out, and then add another.
Analyze your business processes, identify problem areas, and start there. Remember that AI and ML are complex technologies that are just getting started. They will be critical in the coming years for any company seeking to advance and gain market leadership
