In today’s digital age, staying up to date on technological advancements is essential for businesses to outwit the competition and achieve desired business growth.
Because of improved software and hardware, the pace of disruptive technologies such as AI and ML in finance has accelerated in recent years.
The finance sector, in particular, has seen a significant increase in the use of machine learning applications to improve outcomes for both consumers and businesses.
In this article, you will get to see 12 use cases of machine learning in finance. Keep reading!
About Machine Learning In Finance
Until recently, hedge funds were the primary users of AI and ML in finance, but in recent years, applications of ML have spread to a variety of other areas, including banks, fintech, regulators, and insurance firms, 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, and market impact analysis to offering alternative credit reporting methods.
Banks, trading firms, and fintech firms are rapidly deploying machine algorithms to automate time-consuming, mundane processes and provide a far more streamlined and personalized customer experience.
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How Does Machine Learning In Finance Work?
Machine Learning extracts meaningful insights from raw data sets and produces accurate results. This data is then used 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.
Why Use Machine Learning in Finance?
Despite the challenges, here are some of the reasons why banking and financial services firms should consider using Machine Learning:
- Increased revenues as a result of improved productivity and user experience
- Process automation results in lower operational costs
- Increased security and compliance.
Best Use Cases of Machine Learning in Finance
Below are the 12 best use cases of machine learning in finance:
1. Financial Monitoring
Machine learning algorithms have the potential to improve network security significantly. Data scientists are constantly developing training systems to detect red flags, such as money laundering techniques, which can be avoided through financial monitoring.
Machine learning technologies could power the most advanced cybersecurity networks in the future.
2. Making Investment Predictions
Because machine learning-enabled technologies provide advanced market insights, fund managers can identify specific market changes much earlier than traditional investment models.
The disruption in the investment banking industry is clear, with renowned firms such as Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors.
3. Process Automation
Machine Learning-powered solutions enable finance firms to completely replace manual labor by automating repetitive tasks with intelligent process automation, resulting in increased business productivity.
Chatbots, paperwork automation, and employee training gamification are some examples of machine learning process automation in finance.
This allows financial institutions to improve their customer experience, cut costs, and scale up their services.
4. Secure Transactions
Machine Learning algorithms excel at detecting transactional fraud by analyzing millions of data points that humans often miss.
Furthermore, machine learning reduces the number of false rejections and improves the precision of real-time approvals. These models are typically based on the client’s internet behavior and transaction history.
Aside from detecting fraudulent behavior with high accuracy, ML-powered technology is also capable of identifying suspicious account behavior and preventing fraud in real time rather than detecting it after the crime has already occurred.
5. Risk Management
Banks and financial institutions can significantly reduce risk levels by analyzing a massive volume of data sources using machine learning techniques.
Unlike traditional methods, which are typically limited to essential information such as credit scores, ML can analyze large amounts of personal data to reduce risk.
6. Algorithmic Trading
Another excellent example of an effective use case in the finance industry is machine learning in trading. In fact, Algorithmic Trading (AT) has emerged as a dominant force in global financial markets.
Trading companies can make better trading decisions by closely monitoring trade results and news in real-time to detect patterns that can cause stock prices to rise or fall.
7. Financial Advice
There are several budget management apps that use machine learning to provide customers with highly specialized and targeted financial advice and guidance.
Customers can use these apps to not only track their spending on a daily basis but also to analyze this data to identify spending patterns and areas where they can save.
8. Customer Data Management
The massive volume and structural diversity of financial data, ranging from mobile communications to social media activity to transactional details and market data, make manual processing difficult even for financial specialists.
Using machine learning techniques to manage such large amounts of data can improve process efficiency while also providing the benefit of extracting real intelligence from data.
Data analytics, data mining, and natural language processing are examples of AI and ML tools that can help businesses gain valuable insights from data.
Machine Learning algorithms can be used by banks and financial institutions to analyze both structured and unstructured data.
Customer requests, social media interactions, and various internal business processes, for example, discover trends (both useful and potentially dangerous) to assess risk and assist customers in making accurate decisions.
10. Customer Service Level Improvement
Customers can use an intelligent chatbot to get all of their questions answered about their monthly expenses, loan eligibility, affordable insurance plan, and much more.
Furthermore, there are a number of machine learning-based applications that, when linked to a payment system, can analyze accounts and allow customers to save and grow their money.
11. Customer Retention Program
Credit card companies can use ML technology to predict at-risk customers and retain only a subset of them. They can easily know user behavior and design offers specifically for these customers based on user demographic data and transaction activity.
The application uses a predictive, binary classification model to identify customers at risk, followed by a recommender model to determine the best-suited card offers that can help retain these customers.
AI and Machine Learning models’ ability to make accurate predictions based on past behavior makes them excellent marketing tools.
Machine learning algorithms can assist finance companies in developing a robust marketing strategy by analyzing mobile app usage, web activity, and responses to previous ad campaigns.
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Challenges Faced by Finance Companies While Implementing Machine Learning Solutions
While developing machine learning solutions, financial services companies frequently run into the following issues:
Lack of understanding of business KPIs
Financial services firms want to capitalize on this fantastic opportunity. Still, they frequently fail due to unrealistic expectations and a lack of understanding of how AI and Machine Learning work (and why they need it).
The high cost of R&D
Financial services companies frequently struggle with data management because they have fragmented chunks of data stored in various locations, such as reporting software, regional data hubs, CRMs, and so on.
Getting this data ready for data science projects is a time-consuming and costly task for businesses.
The combination of all of these challenges leads to unrealistic estimates and consumes the entire project budget. This is why finance firms must set realistic expectations for each machine learning services project based on their specific business objectives.
Future Prospects of Machine Learning In Finance
While some applications of machine learning in banking and finance are well-known and visible, such as chatbots and mobile banking apps, ML algorithms and technology are now gradually being used for innovative future applications as well by accurately drawing out historical data of customers and predicting 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.
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Machine Learning is now used in many aspects of the financial ecosystem, including asset management, risk assessment, investment advice, dealing with financial fraud, document authentication, and much more.
Most financial institutions must begin by identifying the right set of use cases with an expert machine learning services partner, who can create and implement the right models by concentrating on specific data and business domains after a thorough understanding of the expected output that will be extracted from various sources, transformed, and yield the desired results.
This article has listed some of the best use cases of machine learning in finance. I hope you find them helpful.
Frequently Asked Questions
Machine learning algorithms are used in finance to detect fraud, automate trading activities, and provide financial advice to investors.
Simply put, there isn’t enough history. The financial crisis is an extreme case – there is only one data point from which we can learn. This makes applying automated learning approaches extremely difficult.
Excel, once you’ve mastered the fundamentals, can be a valuable addition to your arsenal of machine-learning tools. While it is not suitable for dealing with large data sets or complex algorithms, it can aid in the visualization and analysis of smaller batches of data.
- fayrix.com– 10 Best Use Cases of Machine Learning in Finance