Published by Jessica Weisman-Pitts
Posted on September 1, 2023
5 min readLast updated: February 1, 2026
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By Alister Sneddon, Head of Product, CMC Invest
AI in financial services is a hot topic, with some companies even appointing AI as their CEO. But can AI make better financial decisions than humans? And perhaps more importantly – should it? Personal beliefs and the intimate nature of finances may impact our willingness to fully trust AI. While AI shows promise, the question remains: would you let AI manage your wealth?
While some may fall for the lure of ‘guaranteed returns’ when investing with AI, there are nuances to keep in mind. Designed to simplify life, AI can help users to process complicated information more quickly. However, finances are not always about being rational, it’s about experience and creativity. What works for one investor might not align with your beliefs, and therefore, trusting AI to make choices on your behalf is likely unwise.
Though everyone’s talking about whether AI should choose stocks, its potential role in financial services extends beyond this. Let’s take a closer look at the latest trends and things to be aware of, ahead of potentially using AI for investing goals.
It’s proven that AI can excel in data analysis, especially in processing large, structured data sets. When, for humans, it takes years of education and experience, for machines it’s easier to process and identify patterns or outliers. For example, huge data sets, such as financial statements can be instantly processed, minimising time and expenses for the company. Hence the mass panic that machines will replace human work, but will it really?
Using AI doesn’t mean there will be no need for financial analysts. Instead, it opens up the potential for large-scale data processing across the entire market. Instead of having a limited team of analysts examining a specific number of companies and reports, AI can assist in pinpointing interesting or unexpected patterns, enabling analysts to concentrate their efforts on areas with the greatest potential value.
Here comes an important advantage of having AI as an assistant: AI can provide insights that go beyond surface-level data. It might not have all your answers immediately, but it can guide you to where you should concentrate your efforts. For example, pointing out that the investment in one year outperformed all other years in the market can help you to identify successful investment patterns.
AI is rapidly advancing in the realm of unstructured conversational data, enabling dynamic conversations, and mimicking human interactions. Chatbots, powered by AI, have become more sophisticated and cost-effective, potentially revolutionising live chat experiences. AI-driven systems can now understand tone, context, and intent, creating a seamless first-line support experience.
While some customers may be hesitant about interacting with chatbots, the evolution of AI in communication is ongoing, with continuous improvements in understanding and connecting information without requiring set questions. As AI continues to progress, it will likely become more integrated into our everyday communication, offering helpful suggestions in emails and other written interactions. If a chatbot can mimic tone, it can take jargon and make it digestible for the receiver. For financial services, this skill could even help to solve a long-battled yet common issue: accessibility.
AI can play a crucial role in ensuring security, compliance, and data optimisation by conducting regular check-ins at the right time. It can analyse user behaviour to determine the perfect moment to engage. Current examples, like email campaigns, already use data analysis to time interactions effectively.
However, AI goes beyond simple engagement and delves into understanding the “why” behind users’ actions, testing combinations and patterns on a massive scale. This behavioural analysis can provide valuable insights into how individuals make decisions and consume information. Tailoring services to individual behaviour is no longer science fiction, as AI prompts can have a significant impact on users.
Over time, AI could help customers to understand when is best for them to make a financial decision. Perhaps history shows that you always pick your most successful stocks early in the morning. Or, you think with a savers mind-set at night. Knowing when to make decisions can help you reach your long-term saving and investing goals with more confidence.
Finances are deeply personal, encompassing more than just money in one’s wallet. How we earn, spend, share, and interact with our money reflects our values, and can reveal a lot about a person.
Considering the opportunities AI offers, there is no reason it couldn’t make financial decisions. However, the personal nature of wealth might hinder some from fully trusting AI with their long-term financial decisions. The cost of financial advice can be high, but the value and comfort it provides explain the expense. People are not always rational in finance, as the unpredictable stock market demonstrates. So, the question is not whether AI can be a wealth manager or financial advisor, but rather, would you use it?
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn. In finance, AI is used for data analysis, decision-making, and enhancing customer interactions.
Data analysis is the process of inspecting, cleansing, and modeling data to discover useful information, inform conclusions, and support decision-making. It is crucial in finance for identifying trends and making investment decisions.
Behavioral analysis involves studying the actions and decisions of individuals to understand their preferences and motivations. In finance, it helps tailor services and products to meet customer needs.
Personalized financial advice is tailored guidance provided to individuals based on their unique financial situations, goals, and preferences. It often incorporates technology and data analysis to enhance decision-making.
Unstructured data refers to information that does not have a predefined data model or is not organized in a predefined manner. Examples include emails, social media posts, and customer feedback, which can be analyzed for insights.
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