Key Takeaways:
Machine learning enables financial institutions to analyze data and create an automated model around it
With ML companies can leverage increased productivity, fraud detections, better compliance, algorithmic trading and more
Key use cases of machine learning in finance include monitoring, algorithmic trading and more
Table of contents
What Is Machine Learning in Finance?
Machine learning in the financial services industry is the use of statistical models aimed to help collect and evaluate datasets, extract insights, apply algorithms, and make predictions. Machine learning (ML), by definition, is a method of data analysis that interprets and automates model building.
In finance, the rapid acceleration of technology has placed machine learning front and center. Quickly-advancing software and hardware have propelled financial services companies to rely heavily on ML applications as they lead to better operations and bring improved results.
With this in mind, machine learning could be used in finance by selecting certain models and feeding them with data. The data will be used to automatically adjust the model and improve the outcome. In that sense, the more data you can collect and analyze, the better and more accurate results you will get.
Applications of Machine Learning
Some of the reasons why banks, investment funds, and other financial services companies should consider using machine learning include:
Better, quicker, and more efficient scaling;
Increased productivity;
Automated trading activities;
Improved user experience;
Fraud detections and better compliance;
Low maintenance cost once implemented.
Challenges for Machine Learning in Finance
Key challenges for the fusion between machine learning and finance include:
Lack of understanding and unrealistic expectations;
Relatively high development cost due to a global shortage of data scientists and engineers.
Financial services companies are greatly in favor of machine learning as they want to exploit the opportunity and build on it. But unrealistic expectations and lack of a proper vision are among the main reasons why some companies fail in this aspect.
Next to the difficulties on how machine learning can be implemented is the relatively high cost of R&D involved in the development process. Companies need to implement new processes for collecting, cleansing, governing, and using their data as the starting point for successful Machine Learning models. Preparing all this data for a machine learning processes is a task often carrying a high price tag. However, once developed and implemented, machine learning can significantly bring down costs by leveraging process automation, algorithmic trading and other key benefits.
Nevertheless, machine learning is increasingly popular among financial services companies. From hedge funds to investment banks and fintech, this technology offers some attractive benefits in a wide array of use cases. Let’s find out what they are.
Machine Learning Use Cases in Finance
Process automation
A common use case of machine learning is process automation. This technology allows to save time, cost and effort by replacing manual work and repetitive tasks with automated processes. Not only does this increase productivity, but it also raises the quality of the operations as it eliminates the human error factor.
Process automation through machine learning could be applied to a number of services including:
Chatbots
Call-center automation
Processes automation like claims processing and marketing optimization
Financial monitoring
Machine learning algorithms can greatly boost financial monitoring. Moreover, through financial monitoring supported by machine learning, data scientists could stay up to date with the functioning of systems and operations.
Financial monitoring would easily detect red flags such as money laundering attempts, frauds, or cybersecurity attacks.
Investment decisions
Machine learning in investing could help financial companies get better results as they base their investment decisions on moldable and flexible data. With this in mind, a report by US investment bank JPMorgan says that “machine learning is entering a new era.”
“While previous algorithms were hard-coded with rules,” the study says, “J.P. Morgan is exploring the next generation of programming, which allows machine learning to independently discover high-performance trading strategies from raw data.”
Machine learning enables institutional investors to work with advanced market insights and take advantage of price changes before the rest of market participants can react.
Algorithmic trading
Machine learning in trading is done through automated algorithms that take away the stress and emotional burden when dealing with the forces of the financial markets.
Algorithmic trading is now present in market strategies used by a growing number of fund managers and large-scale investors.
Automated solutions and trading models allow for quicker and more efficient market reactions. An automated trading strategy could be tailored, for example, to react to news that could move prices by precisely specified margins.
Further, machine learning could analyze hundreds of data sources, charts, and price movements in a matter of seconds. As a result, the outcome of this process could offer a distinct advantage when participating in the financial markets.
Some key benefits from algorithmic trading include:
Higher probability of being right
Optimized risk-reward ratio
Human errors are reduced to a minimum
Could be applied to any market at any time
Risk management and security
Machine learning techniques help bring risk levels down. Banks and other financial institutions that transact billions of dollars every day need to have resilient systems that can withstand cyber-attacks, fraud attempts and a whole spate of online threats.
To this end, machine learning can analyze significant volumes of data and quickly flag any potential threats or high-risk situations.
In practice, machine learning algorithms can be trained to detect financial frauds, suspicious account behavior or other crime-related activity.
Additionally, through large volumes of data sources, machine learning techniques could be used to track customers at risk for defaulting on their loans.
Summary
Machine learning plays a key role in the shaping of today’s financial world. From managing assets, to servicing customers and collecting raw data to spot inefficiencies, automated algorithms have powered a big technological leap forward.
This said, machine learning algorithms are constantly working with data and improving their own performance. As a result, they become better and more reliable while the financial system becomes stronger and more automated.
In this light, finding the right machine learning services partner will likely determine the level of your success. By utilizing data science, advanced analytics, machine learning (ML), and Artificial Intelligence (AI) algorithms, Scalefocus supports financial companies in their efforts to extract valuable information from their data. With the right tools, enterprises can make result-oriented decisions, get deeper insights, deploy resources more effectively, and improve customer experience. Across the financial services industry, our teams have developed numerous systems and platforms underpinned by machine learning algorithms and advanced analytics.
We are working with financial institutions of various sizes and manage a number of machine learning projects, all with unique use cases. We have successfully reached major milestones such as improved revenue, cost optimization, and risk reduction. Scalefocus employs its technological know-how to deliver reliable offshore and onshore development services for financial companies.
Our custom-built software, web and mobile applications drastically improve customer experience. This helps our partners build their own software solutions that fit their business models and benefit from significant cost savings and business processes optimization.
To learn more and discuss how you can successfully employ machine learning in your projects, contact us today.