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The Role of Artificial Intelligence in the Financial Sector
Artificial Intelligence (AI) has gained significant attention, particularly following the release of ChatGPT in November 2022 (McKinsey & Company, 2024). Since then, AI has rapidly evolved, with both companies and the general public integrating it extensively into their daily operations.
Finance, and more specifically Wealth Management, is one of the sectors that can greatly benefit from the implementation of AI. According to the Organization for Economic Co-operation and Development (2021), AI is driving digital transformation and has been rapidly adopted across the finance sector. This adoption is unsurprising, as finance has often been associated with cutting-edge technology. AI's ability to process vast amounts of customer and market data enhances prediction accuracy, generates leads, and automates back-office tasks, making it indispensable for Wealth Management (Rafalski et al., 2025).
A major advantage of AI is its ability to reduce costs and decrease risks (Sha, 2024). Indeed, AI automates repetitive tasks, allowing employees to focus on critical, high-value projects, thereby saving time and reducing costs. Additionally, AI mitigates human error by assisting with monotonous tasks and performing real-time double-checking for errors (Krishnaraj et al., 2003).
In some cases, Artificial Intelligence has allowed individuals to achieve a 30% return on their investment, compared to the traditional expected return of 10% (Fearn, 2024). Wealth Management firms have not missed out on the opportunity. Only two years after the release of ChatGPT, 78% of organizations are already deploying AI-driven technology for both client and advisory purposes, while the remaining 22% are still working on its implementation (Ahramovich, 2025).
Practical Applications of AI in Wealth Management
Artificial Intelligence has become an integral part of Wealth Management, offering a wide range of applications (i.e., in the front and back-office). From enhancing customer service with 24/7 chatbots to ensuring regulatory compliance and reducing costs, AI is revolutionizing the industry in numerous ways (Sikora, 2025). Let's delve deeper into specific use cases that demonstrate the transformative power of AI in Wealth Management.
Concrete Use Cases with Real-Life Examples:
1. Chat bots
AI-powered chatbots have gained significant popularity among financial institutions, due to substantial advancements in recent years. Many financial institutions, such as banks, have implemented this technology to provide a 24/7 personalized customer service experience for clients. This allowed them to increase customer satisfaction, unclog bank agencies, and ultimately cut costs. Chatbots used in banking have been estimated to provide $8 billion in cost savings annually (CFPB, 2023).
Example: Standard Chartered - StanChart provides a 24/7 customer service chatbot along with a chat and collaboration tool called myRM (Fearn, 2024). This tool enhances customer interaction and support by facilitating communication between clients and their relationship managers and enabling secure transfer of documents and files, and more.
2. Regtech & Compliance
With the ever-changing regulations in the financial and technological sectors, companies often dedicate a significant amount of time and resources to compliance each year. In addition to identifying new regulations impacting the firm, Regtech can also help compliance management teams with anti-money laundering, countering financial terrorism, fraud prevention, risk management, stress testing, and micro and macroprudential reporting (Financial Stability Board, 2020). As failing to comply with regulations often result in large fines and reputation loss, this feature has become an industry favorite (Skrobiś, 2024).
Example: EY’s SARGE – Ernest & Young developed a cloud-based AI solution that analyses governing contracts and identifies potential liabilities (Sangma & Becchi, 2021). SARGE is not completely independent, but according to estimates it allows compliance management teams to save up to 75% of time (Sangma & Becchi, 2021).
3. Capital Market Applications / Sentiment Analysis / Trading Applications
The presence of technology in the capital markets’ decisions processes is not new, quantitative finance has long been relying on advanced statistical analysis to shape complex trading strategies. Artificial Intelligence and Machine Learning can be seen as the natural extension of quantitative trading solutions as it allows for bulk data analysis and creating better learning models resulting in more precise strategies (Congressional Research Service, 2024).
Another aspect of quantitative finance that has been greatly improved by AI is sentiment analysis. AI’s ability to process vast amounts of data allows wealth management firms to gain better insights into the market's current opinion about securities.
Example: BlackRock’s Aladdin – BlackRock promotes Aladdin as comprehensive portfolio management software that includes trading functions among its many capabilities. The platform integrates advanced risk analytics, portfolio management tools, and trading features, empowering financial professionals, institutions, and asset managers to make well-informed decisions (BlackRock, 2025).
4. Robo-advisors
Robo-advisors also had their fair share of improvements over the last few years, becoming increasingly proficient at providing the best portfolios tailored to clients' preferences. But again, AI has been able to bring the portfolio allocation process to the next level by utilizing, among other things, automated investing. It allows for better predictions of what next steps should be undertaken as well as better personalized communication and reports for the client, also allowing for better onboarding (Sangma & Becchi, 2021). Additionally, AI has facilitated the implementation of improved tax harvesting features for clients' investments. All of these advancements contribute to increased customer satisfaction and loyalty, allowing wealth managers to retain clients long-term (Kiran, 2024).
Example: Wealthfront - Wealthfront’s robo-advisory platform is among the few that provide digital-only financial planning and investment management services. Wealthfront’s AI algorithm examines a client’s saving and spending habits and automatically establishes the optimal steps for reaching their financial goals (Friedberg, 2025).
5. Due Diligence / Bulk Research / Manual Time-consuming Tasks
As a significant portion of a wealth advisor's daily tasks is dedicated to repetitive, non-value-adding, and time-consuming activities, it comes at the expense of spending time with clients and fostering the advisor-client relationship (McKinsey analysis, 2022). Indeed, filling out paperwork, conducting due diligence, and spending time on research can be inefficient.
According to recent McKinsey research (2022), relationship managers spend up to 70% of their time on advisory-irrelevant activities, as illustrated in figure 1
Figure 1: Timing allocated on (non)-advisory activities by relationship managers
Source: Analytics transformation in wealth management by McKinsey analysis (2022), from: ries/financial-services/our-insights/analytics-transformation-in-wealth-management
Automating non-advisory tasks or at least reducing the time spent on them would be immensely valuable for wealth managers, allowing them to focus more on value-adding activities and become more productive. Indeed, as AI can compile, capture, and synthesize data into the advisor's customer relationship management system, it will enable advisors to spend more time with each client and deepen their relationships (Sha, 2024).
Example: Morgan Stanley - Morgan Stanley has presented an AI assistant designed to streamline the day-to-day tasks of its global wealth managers. The AI @ Morgan Stanley Assistant allows the bank’s financial advisors to find relevant information from an internal database of more than 100,000 documents (Son, 2023).
6. Know Your Customer / Onboarding
One of the main pain points for relationship managers is the Know Your Customer (KYC) process required to provide investment advice to clients, in which AI can also provide support. Due to its ability to efficiently collect information, as mentioned in the data extraction use case, and to cross-verify it across various sources, AI can significantly impact the onboarding process during an advisory session. For instance, it saves time for the advisor and provides a more pleasant experience for the client (Sha, 2024).
Example: Magic DeepSight – Magic DeepSight is an AI-powered tool aimed at extracting data from documents and identifying KYC relevant information, ultimately automating the KYC process. Implementing this solution allows for up to a 70% decrease in KYC data analysis expenditures (Magic FinServ, 2023).
7. Lead Generation / Client Acquisition
Artificial intelligence, with its ability to analyze a vast amount of data, can identify new high-quality clients and more precisely segment existing prospects (Sha, 2024). One of AI's many features for the lead generation is its capability to match advisors and clients based on common interests, age group, political opinions, shared languages, and more. AI can also personalize sales pitch, adapt its tone, find responses to objections, and much more (Rigg, 2025).
Example: InvestCloud – InvestCloud provides AI-powered technology specialized in mining data on social media such as LinkedIn (Rankov, 2023). The AI investigates the prospect’s profile and attempts to pair it with a relationship manager that has a high matching score, resulting in higher conversion rates and better customer loyalty (Sha, 2024).
8. Recording and Utilizing Client Data to a Better Extent / Higher Personalization in Advice / Fostering Customer Relationships
The best relationship managers are often those with the best memories (Sha, 2024). Being able to recall all the little details about a client’s personal life, professional setting, risk appetite, as well as their dining and wine preferences, favorite hobbies, sense of humor, political views, and more, greatly contributes to the success of the client relationship.
Artificial intelligence becomes super handy by recording, sorting, and analyzing data (Ceiba Software, 2025). An AI assistant can provide relationship managers with details, topic recommendations, and relevant questions before or during client meetings, significantly enhancing the client relationship.
Example: Gambit Financial Solutions – Gambit currently works on a proof of concept (POC) using Generative AI to assist relationship managers during the advisory process. Stay connected to learn more in the future!
The Current State of AI in Wealth Management
Quantifying the exact adoption rate of AI and the extent to which it is being used by companies in general is quite complex. While artificial intelligence is a powerful tool, it comes with risks and vulnerabilities, especially in sectors like wealth management, where large amounts of money and data are involved (Leitner & al., 2024). A single mistake can have devastating consequences.
Wealth management firms remain cautious about AI due to the numerous potential threats and existing disadvantages that need to be addressed. Currently, AI is primarily used in roles like an intern, with every output requiring double-checking. Now, AI functions mostly as an assistant, but the industry is still far from the day when AI will fully take over roles, especially in wealth management.
Furthermore, as AI tools continue to spread across the financial system and a few best-performing players stand out, it is expected that externalities such as market correlation and herd behavior will drastically increase. If you want to know more about the advantages and disadvantages of AI for wealth management, you can read more about it in this article about integrating AI in investment advisory tools.
Nevertheless, when implemented correctly, the advantages of Artificial Intelligence clearly outweigh the risks and costs. This is directly reflected in the rate of adoption of AI and by the observable benefits companies have observed. According to PwC's (2024) Global Investor Survey, 73% of investors expect companies to scale AI solutions. More specifically, 66% foresee productivity gains, 63% anticipate revenue growth, and 62% expect increased profitability from AI within the next year (PwC, 2024).
Teasing AI Advancements for Gambit
Being a frontrunner in the promising financial technology sector, Gambit has explored numerous use cases that could significantly benefit to financial institution which are its customer base.
Gambit employs the Design Sprint process, originally created by Google Ventures, to test, prototype, and solve complex business questions. We have adapted this process to a 5-days format, which is more closely aligned with our business, participants, and remote environments. The main advantage of the Gambit Design Sprint is that it produces quick, focused, user-centric projects or ideas that are stress-tested and market-fit. This process heavily mitigates risk and identifies critical problems before investing in the project.
When the project or idea is approved, a proof of concept (POC) is created to further investigate both its value and technical feasibility. This phase extends beyond the design sprint, aiming to delve deeper into all aspects and provide a final validation for the different use cases.
We apply these processes to explore possibilities and deliver tailored solutions for our clients. Consequently, Gambit has applied these concepts to the exploration of AI, and we expect to implement our first Artificial Intelligence feature in our product during the upcoming semester.
If you want to know more about the available wealth management options for your company, do not hesitate to reach out to our team.
What Comes Next for AI in Wealth Management?
Historically, finance has been slow to adopt innovative technologies due to the trust-based nature of the industry. This is mainly because finance and banking cannot afford scandals involving clients' money. As mistakes involving money are not tolerated, financial institutions face substantial regulatory and security constraints, which naturally limit their innovation potential compared to other industries.
However, decreasing margins for banks and asset managers have stimulated them to explore new sources of efficiency and this is where AI comes into play. With its unique abilities, artificial intelligence has the potential to revolutionize the finance industry. This has forced finance professionals to slowly start implementing AI in their daily operations but still with great care.
In advisory business, for example, AI is starting to get some attention. However, we are far from the days when a robot will replace your favorite relationship manager. The risks associated with artificial intelligence hallucinations are real, and wealth management firms are still not ready to abandon the 'human in the loop' decision-making model (Leitner & al., 2024). Additional time and development are necessary before fully autonomous AI use cases can be implemented in the financial sector.
Nonetheless, it is hard to argue against the fact that artificial intelligence and machine learning will have a major impact on finance. Policymakers, financial institutions, investors, customers have all understood this and are all trying to promote, hold back, protect or destroy the fast implementation of AI in companies. Artificial intelligence and machine learning bring their own set of risks and advantages which, with the current projections, will be crucial in defining the future of the wealth management sector.
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