Machine Learning in Finance

5 Benefits of Using Machine Learning in Finance

Machine Learning (ML) is a component of artificial intelligence that processes large data sets to detect patterns, generate predictions and recommendations, and improve effectiveness over time. This ability to improve accuracy and speed in human decision-making and work effectively has the potential to transform the finance industry, and it already is doing so. Let’s take a look at the benefits, use cases, future, and potential careers in machine learning in finance. 

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Benefits of Machine Learning in Finance

Implementing machine learning in finance has several benefits, including the following. 

1. Minimizing Human Errors

Human error is unavoidable. However, for the financial sector, even the smallest error can have a severe impact and result in the loss of millions of dollars. By replacing or complementing machine learning algorithms, human errors can be substantially reduced and ensure accurate data processing. 

2. Time-Efficient

Machine learning is less time-consuming and offers real-time solutions. It can speed up the manual process and help make complex decisions and accurate predictions. 

3. Cost-Effective

AI and ML technology improve with time. Besides initial investment and operational costs, it can prove to be a valuable investment in the long run. 

4. Reduced Workload

Machine learning can do complex, repetitive, and time-consuming tasks efficiently and help reduce workload. 

5. Free of Bias

As compared to human judgment, machine learning algorithms are generally more reasonable in data selection and decision-making. Such bias-free and transparent judgment by machine learning are imperative for the finance industry. 

Professional Certificate in Machine Learning and Artificial Intelligence at Imperial CollegeHow Can Machine Learning Be Used in Finance?

Some of the most widely adopted applications of machine learning in finance include fraud detection, risk management, process automation, data analytics, customer support, and algorithmic trading. The use of machine learning in finance is evolving and aiming to move towards autonomous finance.

According to Gartner’s 2022 finance executives survey, finance has heavily invested in technology: general ledger technology, a financial close solution, or workflow automation. These technologies have already been deployed in over half of the functions. The study also notes that most executives are aiming for a touchless financial close by 2025, meaning that the entire financial close process is being executed autonomously without intervention from human employees. Clearly, artificial intelligence and machine learning are fast becoming the future of financial services. 

Why Should We Use Machine Learning in Finance?

Choosing the right technology that can deliver value is paramount to investing. Here are seven use cases of machine learning in finance that you should consider. 

Financial Forecasting

Machine learning is unparalleled in predicting financial trends. By analyzing large data sets, it can predict future trends and identify forthcoming risks and opportunities that drive better investment decisions. 

Advanced Customer Support

Machine learning can greatly improve customer experience and support with the help of chatbots. These chatbots provide instant support and personalized recommendations, financial advice, and resolve basic queries. This advanced customer support use case is especially valuable for businesses with a large customer base. 

Portfolio Management (Robo Advisors)

Robo-advisors assist in creating and managing an investor’s financial portfolio. These online applications use algorithms to oversee investments and optimize clients’ assets in accordance with their risk preferences and desired financial goals. 

Fraud Detection and Prevention

Machine learning algorithms help design fraud detection frameworks to detect and prevent fraudulent activities in Fintech companies. With the surge of digital transactions, ML can be a valuable asset in catching suspicious activities in real time and freezing them to minimize loss.   

Algorithmic Trading

Algorithmic trading is a time-effective and bias-free substitute to manual trading, which helps make better trading decisions. It analyzes thousands of data sources and market conditions to detect patterns, develop strategies, and ultimately boost the chances of higher profits.  

Underwriting and Credit Scoring

Machine learning facilitates the underwriting process. By training algorithms on large amounts of customer data, the system can make quick underwriting and credit scoring decisions and help employees work effectively. 

Process Automation

Process automation is perhaps one of the most common but effective use cases of machine learning in the finance industry. Automation, such as call center automation, chatbots, gamification of employee training, and paperwork automation, can allow companies to replace manual work, improve services, and enhance business productivity.  

Reasons Why Finance Companies are Turning to Machine Learning and Artificial Intelligence?

Finance companies move billions of dollars across the world every day and generate a high volume of big data that needs to be processed to provide valuable insights. Collecting and analyzing such a diverse data set is where AI has made a significant difference. Let’s take a look at some of the reasons why financial companies are turning to ML and AI. 

Why Finance Companies Need ML and AI

  • Gaining insights from massive volumes of data 
  • Streamlining and automating processes to increase productivity 
  • Enhancing customer experience 
  • Providing personalized customer service  
  • Strengthening security 
  • Detecting and preventing fraud 
  • Lowering risk levels

Future Prospects of Machine Learning in Finance

The future use cases of machine learning in finance are likely to evolve with the finance sector investing in AI and AI adding value to the services. As we move into the AI-powered digital age, machine learning has become one of the most vital needs for the financial sector. The profound amount of data generation in finance is also proving to be a valuable training environment for AI. This training and improvement of ML algorithms result in the continuous advancement of technology, thus bringing it closer to a fully automated financial future. 

Careers in Machine Learning in the Finance Sector

Alongside such tremendous technological development, the demand for qualified professionals with AI and ML skills is also rising. The U.S. Bureau of Labor Statistics (BLS) categorizes machine learning under the computer and information research scientists field; the jobs in this field are projected to grow 21 per cent between 2021 and 2031, making it one of the fastest-growing occupations. 

Jobs Titles in Machine Learning in Finance (With Salaries)

Machine learning in finance can provide lucrative career and earning opportunities to highly skilled professionals with a diversity of expertise. Here are some machine learning jobs in finance and their average annual salary in the United States, according to Glassdoor

Machine Learning Careers in Finance Annual Salary 
Machine Learning Engineer  $130,019 
Machine Learning Data Analyst $111,402 
Data Scientist in Finance $117,709 
Quantitative Research Analyst $119,653 
Machine Learning Modeler $143,532 

How Can Emeritus Boost Your Career in Machine Learning in Finance?

Artificial Intelligence is the present and the future of the finance industry. The capacity of machine learning to improve accuracy and control risks has become critical to the growth of the financial sector. Machine learning in finance is a relatively new and emerging technology to stay at the top of the field. Finance professionals can greatly benefit from short-term online courses that can equip them with the knowledge and credentials necessary to advance their careers. If you wish to upskill, check out Emeritus’ diverse catalog of machine learning courses taught by experts from the world’s best universities.

Written by Krati Joshi

Write to us at content@emeritus.org 

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