The financial sector is facing a pivotal moment. AI is developing very rapidly, and it seems that its integration is already inevitable. The systems indeed are remarkable at processing large volumes of data and spotting trends. Nevertheless, at this point, they sometimes can also fall short of intuition and nuanced market insight that professional analysts provide.
Keeping this in mind, a crucial question faces industry: can AI completely replace human analysts or is it just a temporary trend?
Where can AI replace humans?
Ever since the arrival of artificial intelligence, businesses have anticipated its advent as a revolutionary tool that alleviates the need for human interface and increases productivity in order of magnitude. AI was designed to become capable of lifting the burden from repetitive work streams such as raw data inputs, so the idea was that AI would enable faster turnaround time and higher accuracy rates.
So far, AI in financial management has not only made processes run seamlessly, the analysis of massive databases provided businesses with useful, truly innovative deduction strategies. Sometimes, through its methods, AI can also find correlations inaccessible to traditional methods. It also makes it much cheaper to provide services in finance, for example, in areas such as robo-advising.
Businesses aimed to identify new patterns, improve employee staffing, and strategically plan for the future, aided by data-driven decision-making. Moreover, it was estimated that employee satisfaction would rise due to AI tools personally tailoring the eLearning courses, career progression plans, and perks to employees’ needs. In the long run, automation of mundane tasks and constant optimization of processes were anticipated to lead to superior savings in relation to AI.
Although the technology is quite young, we can still see that leaders from the financial management industry actively integrate it. Take, for example, BlackRock, which developed its own AI system — Aladdin AI. This development is aimed at helping analysts neutralize risks. The company claims that with the use of Aladdin, investment professionals manage risk by offering a unified framework for public and private markets, operations, and data.
Another famous example is Goldman Sachs. The investment bank is even testing the hypothesis about removing all junior staff and replacing them with AI technology. For now, it concerns routine tasks such as document gathering, summarizing reports, and running numbers for financial advisors and senior managers. In their opinion, it has already proved to be more effective and cheaper.
Where AI cannot replace humans?
Despite the numerous advantages it brings to the table, AI cannot substitute qualified, ad hoc trained personnel to minimize the risk of non-quantifiable damages. It can also not replace managers in strategic decision-making, taking into account fair practice, including analyzing the psychology of people making decisions in central banks and large organizations. That is why artificial intelligence, no matter how perfect it may be, is not able to replace humans in areas where relationships and negotiations play a key role. For example, in private banking, a personal approach and the ability to find compromises remain indispensable.
Evaluating the prospective success of venture investments, where investors often assess the potential of the team, funders, and, to a lesser extent, financial reports (which are often unprofitable), is also all but impossible to entrust to AI automation. In the financial world, which is extremely sensitive and stringent to any kind of preventable loss, if it’s the AI tool that makes the transactional decision, no concrete party would assume responsibility for fatal errors and material damages.
Regulatory can also partially hurdle ubiquitous AI implementation. A so-called Bank Secrecy Act (BSA) and Customer Due Diligence (CDD) rule that prevention of money laundering and terrorist financing requires financial institutions to identify and verify customer identities. The law often requires face-to-face contact and/or facility visits, and this is the thing that AI is not capable of coping with.
Agent-Based AI as the Next Generation Aid of Financial Analysis
Traditional AI models in finance mainly work as analytical tools, providing predictions and recommendations but not making decisions independently. Agentic AI tackles changing this paradigm from passive analysis to active process management. It can not only interpret data but also initiate actions, such as optimizing investment strategies, managing customer subscriptions, and automating operational tasks.
For example, Amazon’s announcement of an agent-based AI division confirms the growing interest in this technology. However, the impact of such solutions goes far beyond private companies and is becoming the focus of financial regulators; for example, the European Central Bank (ECB) and Reserve Bank of India (RBI) emphasize in their reports that the development of generative AI represents a significant technological leap and requires thorough legal expertise.
But here is the same caveat: the main question is who exactly will assume the decision-making responsibility, where possible errors can cost companies and lenders tens or even hundreds of millions of dollars. Agent-based AI is a good practice tool and a stress-test model, but it requires training too.
What to expect in the future?
Given the prospects that AI offers, I believe that artificial intelligence is capable of becoming an integral part of the processes of analysis and money management soon, gradually replacing almost all participants except strategists, managers, and highly qualified professionals. They will be responsible for making strategic decisions, ensuring they comply with ethical standards, and managing relations with investors and government agencies.
In addition, highly qualified humans will determine what, where, and how to train artificial intelligence and defend its rights in the legal field. The development of autonomous technologies is hampered not only by technical limitations but also by legal barriers.
Ultimately, it will be an efficient symbiosis between highly skilled human analysts. Such systems combining AI and expert control (human-in-the-loop) have already achieved 95.3% fraud detection accuracy, and the number of false positives decreased by 68.9%.
More likely, financial services for retail customers will also be fully automated: AI will analyze, make decisions, and perform operations. It is going to create content for clients, and the investment process itself will become automatic — for example, with the help of an AI consultant available by subscription. Such a virtual assistant will be able to manage part of the client’s capital, as a personal financial advisor does.
As a result, AI will start competing with AI, and the winner will be the one who turns out to be smarter, faster and stronger. Imagine the Olympics of the future: AI athletes are in the arena, and people are in the role of coaches and sponsors, creating the infrastructure for training for them.