Machine learning models are making waves in the financial sector. Major institutions are adopting it for various purposes, from detecting fraud to streamlining application processes. It's becoming a vital tool for many businesses.
To make the most out of machine learning, it's crucial to know what it can do and the risks involved. If you don't, you might miss out on the chance to improve your company with this technology.
In this blog, we’ll explore the use cases of AI and machine learning in finance, discuss the pros and cons, look into some of the latest trends in the field, and provide some quick tips to ensure success.
First, let’s start with some quick definitions.
A robot hand manipulates various financial data
AI, or artificial intelligence, refers to machines capable of performing tasks commonly associated with human intelligence. Examples include learning from experience, recognizing patterns, making decisions, understanding language, and solving problems.
Machine learning is part of artificial intelligence. It involves creating algorithms that can analyze and learn from data to perform tasks like making decisions, identifying anomalies, and predicting future results.
The difference between the two is that AI is a much broader concept encompassing all computer intelligence, while machine learning is a specific subset of AI.
An analyst uses AI to create a financial report
Software developers use AI and machine learning for solutions that help financial institutions leverage their data to make better decisions. Your company can use AI/ML in various business areas, such as customer care and human resources.
Let’s examine how AI and machine learning are reshaping the finance industry.
Machine learning algorithms use previous task outcomes to inform future decisions and automate tasks. This means that instead of following preset instructions, these algorithms learn from previous experiences and adjust their actions accordingly. For example, a machine learning algorithm in an email filtering system can analyze past email interactions and classify incoming emails as spam or legitimate.
Furthermore, Bank of America has used automation to process applications. The algorithm can even execute transactions and use the bank's rules to deal with exceptions. This allowed their staff to focus on the cases that required human interaction.
According to reports, fraud is the largest risk for financial institutions. Software developers have created machine learning algorithms that you can implement in your risk management strategy. These algorithms identify patterns in transactions, helping to detect signs of fraud, insider trading, money laundering, and other illegal activity.
JPMorgan Chase & Co. uses AI and machine learning as part of its anti-fraud strategy. This technology notes patterns in user behavior, transactions, and past information. Then, it can detect abnormal behavior, which honed its fraud detection.
AI/ML can analyze past market data and predict stock market prices. Machine learning applications can also be used for high-frequency trading, quickly locating opportunities, and executing deals. For example, the investment company Two Sigma uses this kind of algorithm to outpace its competition with quick transactions.
AI speech-to-text technology can convert audio recordings from contact center sales calls and other customer interactions into actionable data. ML-powered data analytics applications can use this data to provide insights into how to improve the customer experience and drive revenue.
Financial data is an attractive target for cybercriminals, with attacks costing the global financial sector an estimated $2.5 billion since 2020. Threats are becoming too sophisticated for traditional detection tools to stop, which is why many in the industry are turning to AI-powered cybersecurity tools.
AI and machine learning can learn from past attacks and predict new threat vectors that haven’t been seen before, making them ideal tools to stop zero-day exploits and other novel threats. AI can also be used to automate remediation tasks like incident creation, root-cause analysis (RCA), and device recovery.
Pros | Cons |
Improves over time | Expensive to get started |
Adaptable to different tasks | Takes time to deliver value |
Optimizes labor and productivity | Can hallucinate or make errors |
Artificial intelligence can be a polarizing subject, so you might wonder if AI/ML is right for your business. This pros and cons list can help you judge for yourself how useful AI and machine learning could be for your company.
Improves Over Time The accuracy of an AI’s output increases as you use the technology and its data sets grow. Plus, it can correct its predictions based on previous inaccurate output.
Adaptable to Different Tasks A single AI model can be tweaked or fine-tuned to complete many different tasks.
Optimizes Labor and Productivity As AI/ML takes over menial, time-consuming tasks, your staff can focus on core operations where you can fully utilize their expertise.
Expensive to Get Started Purchasing an AI/ML solution and processing the data it will use takes a lot of time and money up-front, which can be a barrier for some companies. However, you can limit the costs with zero-shot learning.
Takes Time to Deliver Value Machine learning models take time to fine-tune and learn to use effectively, so it can take a while to start to see a positive ROI from an AI investment. Nevertheless, over time, AI/ML can ultimately reduce labor costs and improve revenue.
Can Hallucinate or Make Errors Depending on how well-trained your model is, the quality of your data, and the volatile nature of what you’re trying to analyze, AI/ML could give you poor predictions or inaccurate results. However, you can mitigate this by partnering with a trusted AI developer, increasing the quality of your data, pairing the software with a human adviser, and tweaking the algorithm.
A robot hand manipulates various financial data
The latest trends in machine learning encompass a fusion of cutting-edge methodologies and practical applications across various domains. Let’s delve deeper into these trends.
Low or no-code learning refers to the creation of programs with minimal or no coding. It works by letting you select pre-written pieces of code to arrange them into a functional program.
Companies are developing platforms that allow users to build and deploy machine learning models without extensive programming knowledge. Platforms like Google's AutoML and Microsoft's Azure Machine Learning have user-friendly interfaces, which allow users to create models easily by dragging and dropping components.
With zero-shot or few-shot learning, you require fewer data points than usual. It works by classifying data sets into categories the moment it receives them. For example, you can use zero-shot learning to improve your chatbot.
It can help clients who need assistance by providing sentence options to complete their questions. This can make their inquiries clearer and speed up the process of getting help.
In comparison, machine learning usually requires large data sets. Otherwise, it could give untrustworthy results since they haven't 'learned' enough.
As more people use machine learning technology, there is a worry about the ethics and biases in AI systems. There is a heightened focus on developing and implementing algorithms that are fair, transparent, and accountable. Many organizations are now focusing on researching and addressing ethical challenges in machine learning systems. They are dedicating resources to projects with the aim of promoting fairness and justice.
Neural networks are important in many machine learning models. Research is constantly improving their performance and capabilities. For instance, transformer models have greatly improved how computers understand and process language, performing well on different tests.
Advancements in hardware, like specialized AI chips, are enabling the training and usage of increasingly intricate neural network models. These advancements are making it possible to create and utilize intricate neural networks. Specialized AI chips are a key factor in enabling the training and use of more complex neural network models.
AI and machine learning in finance help predict future trends, handle daily tasks more efficiently, and catch fraudsters and cybercriminals. AI/ML algorithms recognize patterns by analyzing the data you collect on the market, client, and past transactions, so your algorithm is only as good as the data it uses. Therefore, you should verify its validity and accuracy before you enter it into your system. Additionally, this data must be protected with adequate security measures to prevent hackers from blackmailing you or your clients, hurting your reputation, or opening you up to lawsuits.
For businesses seeking to harness the potential of AI and machine learning, partnering with seasoned experts like Divelement ensures optimal implementation and integration into existing operations. Divelement can build custom-tailored solutions for any use cases of AI and machine learning in finance.
Divelement is a nearshore software development firm that delivers innovative AI/ML solutions with accelerated ROI. Schedule a call today to learn more.
