The AI Paradox in Finance: Unlocking Gains, Multiplying Risks

Introduction:

Over the years, advancements in artificial intelligence (AI) have occurred at a rapid pace. This has changed how machines learn, reason, and solve problems. Its impact reaches into everyday life, and the financial world is no exception. It is transforming how financial institutions work with greater efficiency, speed, and accuracy.

From banks to trading firms and individual investors, everyone is using AI. According to a report of the World Economic Forum, financial services firms spent a significant $35 billion on AI in 2023, with projected investments across banking, insurance, capital markets, and payments expected to reach $97 billion by 2027. The main reason behind such huge investments is that AI can process massive datasets to predict market trends and run algorithmic programmes that execute thousands of trades in mere milliseconds.

AI brings both advantages and disadvantages to the financial system. It can bring new opportunities and benefits such as productivity enhancements, cost savings, and improved regulatory compliance. However, if AI tools are used widely in the financial system and AI suppliers are concentrated, operational risk (including cyber risk), market concentration, and too-big-to-fail problems may grow. These issues must be solved through a regulatory framework otherwise, they could result in issues.

AI Predicting Markets:

1. Natural Language Processing (NLP): It is a branch of AI that enables computers to understand, interpret, and generate human language. With its ability to process large volumes of unstructured text, NLP can detect emerging trends, shifts in sentiment, and breaking news that may influence market movements. This helps investors and financial institutions focus on critical data points from vast amounts of data  for more accurate market predictions. For example, analysing social media posts related to a particular product or service can reveal how it is perceived and used by customers. Such information can then inform marketing strategies, product development, and other business decisions.

2. Sentiment Analysis: Sentiment refers to the general mood, attitude, or feeling of investors towards a specific stock or industry. AI-powered systems can analyse news articles, company financial reports, and social media. Thus, helping investors and financial institutions to gauge market sentiment and make accurate predictions.

3. High-Frequency Trading: It is a type of algorithmic trading that uses powerful computer systems and complex algorithms to execute a massive number of orders at extremely high speeds. The AI algorithms trade within milliseconds, allowing investors and financial institutions to exploit minuscule differences in price.

The benefits of these advancements are:                                                                         

Artificial Intelligence offers data-driven insights by using real facts and figures. This results in greater accuracy of predictions and gives investors more confidence as their decisions are based on factual and analytic data. This results in better investment decisions. AI models are effective at assessing and mitigating risks. By carefully analysing various risk factors and market conditions, investors and financial institutions can proactively implement strategies to reduce danger, which leads to better returns.                                                     

AI doesn’t include any kind of cognitive biases, human emotions, and other psychological factors, providing an objective perspective on the market, ensuring that trading and prediction decisions are based purely on data and not on any kind of bias. Artificial Intelligence considers our individual preferences, financial goals, and risk tolerance to provide us with customised investment advice. This improves the customer’s experience, helps them build trust with the platform, and directly leads to more informed and successful investment decisions.

The Dark Side:

In addition to all these benefits, AI in finance possesses various risks too. Cybersecurity, data privacy and protection, fraud, market manipulation, and deepfakes are among the most frequently cited risks.

While AI is known for its accuracy and objectivity, these strengths depend on how responsibly it is used and trained. It may spread misinformation when exposed to biased or manipulated inputs, leading to potential market manipulation and fraudulent transactions. For instance, if an AI model monitoring social media detects a sudden increase in negative posts about a tech company, it might wrongly predict a stock drop, even if the posts originate from bots spreading misinformation.

The ability of GenAI to create and spread deepfake content is a huge threat to the industry. This risk has grown as both the ability and accessibility of GenAI have increased. Believable images, videos, and voices can be created with remarkable ease to impersonate familiar figures and trick others into doing things or sending money. Bad actors may also exploit AI to spread misinformation that influences stock prices, which can harm investors and market integrity.

While AI is recognised for enhancing cybersecurity and mitigating risks, it also presents new cybersecurity threats depending on its use and deployment. Cybercriminals now use AI tools to plan cyberattacks, giving them more ideas. They could use AI to generate convincing fake messages, deepfake videos, or voices, and create fake identification documents. All these could help hackers execute multimillion-pound scams worldwide.

At the same time, AI systems themselves are not immune to such threats, as they can become targets of cyberattacks. Hackers can tamper with AI models, manipulate their training data, or alter outputs to mislead financial systems. Such attacks can lead to the theft of sensitive company data. Poorly designed AI systems can create new vulnerabilities for financial firms. This can result in client or internal data being fed into insecure AI tools, which could expose confidential or personally identifiable information.

Another issue is market volatility. Financial markets are inherently volatile, and even the smartest AI systems often find it hard to keep up with those sudden and unpredictable changes. For instance, as more financial institutions use similar AI models and datasets, a new kind of risk emerges, which is herding behaviour. As AI systems are trained on the same data and are programmed to react in similar ways, they tend to make similar trading decisions. When many AI systems react in the same way, it can cause markets to move sharply in one direction. This could lead to sudden spikes in volatility or even liquidity drying up. Its effect can worsen during unpredictable events, like political shocks or rapid market swings. The AI systems might suggest all to start buying or selling at once, making the situation even more unstable. Hence, this could lead to worsening financial instability in the world.

Conclusion:

AI is undoubtedly the future of finance. The use of AI systems can create potential benefits for firms and investors and increase their efficiency. However, the increased use of AI systems and recent advancements in AI can potentially give rise to new and increasing issues, risks, and challenges, as discussed above. To control this, regulators must be implemented. Already, a few regulations are being used to detect and prevent threats. For instance:

Authentication technology: Uses digital watermarks that are embedded in new content at the time of creation to help prove its authenticity.

Detection technology: This technology continuously monitors vast networks of data to spot anything unusual in real time. What is remarkable is that they could spot content such as forged documents or deepfake videos without even needing the original for comparison. This helps in mitigating cyberattacks.

However, these are not enough. There is a need for the development of additional tools, regulators, and recommendations to address the issues, risks, and challenges posed by the use of AI in financial services. Hence, as finance continues to evolve, the key question will not only be how accurately AI predicts markets, but how responsibly we manage the risks it creates.

By- Anikeit Gupta