Sep 14, 2024
Written by:
John McDowell
Even though AI technology is still in its developmental stages, it has blown everyone away with its tremendous capabilities. Generative AI tools like ChatGPT and Google Gemini are being widely used for a range of tasks, such as text generation, information gathering, and analysis tasks. If you haven't used one of these tools to simplify your emails, speeches, or lesson plans, you might be behind the curve. Organizations are even using AI to automate various internal processes and improve their online offerings, such as customer support services, recommending products, and advertisement targeting.
In this post, we will focus on the growing role of AI in finance and discuss how it is revolutionizing investing strategies, risk management, and customer services in the financial services industry.
Artificial intelligence has made its way into the finance industry, pushing the boundaries of finance technology and opening doors to many more possibilities. The importance of technology in the finance industry can’t be overstressed as organizations operating in the finance space require robust, secure, and reliable systems to allow customers to manage their accounts and make transactions remotely. Further, regulatory requirements also compel financial institutions such as banks to invest in cutting-edge technology to keep their IT systems secure from external attacks. With the sheer amount of data that banks generate daily, it makes sense for banks and financial institutions to deploy AI tools that can quickly process and generate insightful reports from the data.
With the speed of adoption of AI applications in finance, the depth of services offered by AI can often be overlooked. These range from robo-advisory services to call-center services. Let's take a look at some of the biggest advancements in finance tech combined with AI applications:
AI is being used to give automated financial advisory services to clients at comparatively lower fees than those charged by human financial advisors for their services. Based on the client’s financial objectives, risk tolerance, age, capital, and investment horizon, a robo advisor can create a customized portfolio of assets based on statistical investing averages. The process is usually quite interactive and is completed on a mobile or web app. Another advantage of robo-advisors is that they are accessible 24/7 and can be used to make changes to your investment account without contacting a financial advisor.
Financial advisors also use AI tools to manage their clients’ portfolios and mutual funds. With automated tools, several tasks like completion of redemption requests, purchase of new units, NAV calculation, funds’ cash management, asset allocation mix, and rebalancing, are all done using some sort of AI tools and technology. The use of AI in these processes significantly improves the productivity of fund managers as well as the whole organization.
Many financial services firms provide their customers with algorithmic or automated trading abilities on their trading software. This allows clients to execute their trading strategies automatically using the software simply by encoding their trading strategies into an algorithmic software program. In today’s markets, the bulk of trading volume comes from algorithmic or automated trading, which shows the pace at which technology is being integrated into the trading world.
For more information on how this is automated trading is reshaping the investing landscape, read our guide on this here at Tradingsim.
Generative AI and large language models (LLMs) are extremely capable of generating human-like responses to queries. They are being trained by companies on their product-related knowledge base to provide automated text-based customer support services. LLMs are trained on extremely large amounts of data, mostly from the internet, to learn relationships between statistics, characters, words, and sentences and how they all work together. These processes are getting more and more accurate and revolutionizing the interactions between customers and companies, effectively reducing customer service overhead.
Banks’ customers, in addition to getting their transaction history right on their mobile phones, also get budgeting capabilities on their banking app. The built-in AI technology in the app sorts each transaction into different spending categories and generates a spending behavior report on a real-time basis. This capability allows users to track their spending patterns and stay within their budgets. Some apps also allow users to set their overall spending limits for individual categories so that when the limits are breached, users get notified about it.
Fund managers in big firms rely on technology for executing investment strategies and managing the portfolios of their clients. But with AI tools, fund managers can now do much more in a shorter amount of time. We can break down the portfolio management process into three distinct pieces: a firm’s internal processes, customer experiences, and the portfolio manager’s decision-making. AI tools can help the firm in all three components.
Using different AI tools, a financial services firm can improve their internal controls, develop and improve internal standard operating procedures for various fund management processes, and a range of other firm-specific workflow processes. Some firms also let their customers use built-in AI tools in the apps to create a portfolio of stocks based on their objectives, risk tolerance, investment horizon, and planned investment capital.
Generative AI tools are particularly useful for summarizing lengthy and complex texts, making it ideal for wealth managers who don’t have time to read full reports. Additionally, portfolio managers can benefit from AI tools to analyze enormous amounts of data to short list assets or securities for creating different types of portfolios. For instance, wealth managers who want to create a portfolio for an investor with high risk tolerance can use AI tools to find high growth stocks to build an aggressive portfolio.
Modern-day investment managers routinely use AI tools for conducting fundamental analysis, technical analysis, and portfolio optimization, which not only expedites the analysis process but also improves the quality of decision making because of the data-driven analytics generated by the AI tools.
Once a portfolio is created, it needs to be periodically reviewed and rebalanced to ensure that the returns match with the long term objectives of the investor. Thankfully, portfolio rebalancing can also be automated using AI tools.
Risk management is a pivotal element of every investment strategy, and AI can help investment managers implement risk management strategies by effectively identifying and mitigating risks. Banks have a separate risk management department that is responsible for keeping the bank’s financial risk at statutory tolerable levels. Capital Adequacy Ratio is one of the most important metrics of banks that is monitored by regulators to ensure that the bank has enough capital as a percentage of its risk-weighted assets. Further, risk managers divide risks into four major classes: market risk, credit risk, liquidity risk, and operational risk.
Most banks have developed their in-house AI-integrated software systems to help them build complex risk management models and scenario-testing to keep each risk within the allowable limits. Every investment opportunity, no matter how attractive it might seem, is filtered through risk models and is undertaken only if it doesn’t disturb the organization’s overall risk outlook.
At an individual level, investment managers also use different AI tools to mitigate the individual portfolio risk and the fund’s overall risk. Suppose a fund manager is in charge of managing portfolios for several clients while also managing an open-end mutual fund. The fund manager can use AI to identify assets with low correlation and good historical returns, among other selection criteria, to diversify the portfolio’s risk in accordance with the investor’s risk profile. Similarly, to mitigate the fund’s risk, the fund manager can make use of the firm’s in-house software to comply with the risk management standards set out for investments.
AI has had a profound impact on trading systems in the past decade. Nowadays, many unlicensed traders execute their trading strategies using automated trading systems, which can significantly improve their performance. While the responsibility for crafting a trading strategy still rests with humans, AI systems can aid in refining the trading strategy by helping with analysis and execution of the strategy. Once a trading strategy is created, the AI-driven automated system monitors the market and automatically enters and exits trades based on the programmed strategy. AI can process enormous amounts of data in minutes, making analytical tasks much simpler and less time consuming.
Long-term investors also use AI to analyze a company's fundamentals to determine a stock’s intrinsic or real value. This information helps them find undervalued and overvalued stocks and set a target price for a stock.
In today’s financial world, the need for secure, scam-free systems is continuously growing. Challenges like money laundering, phishing, and scams are becoming increasingly common with the growth in digital technology where people can transfer and receive funds almost instantly anywhere in the world. Thankfully, banks and financial institutions, due to strict regulatory oversight, have been able to make their systems relatively secure. However, internal fraud and misappropriations can still happen. External attacks from hackers into an organization’s systems can also happen.
AI technology, in various forms, has been deployed by organizations to flag instances of unusual activity in customers’ accounts. Daily and monthly transaction limits are set for each account to minimize chances of loss for customer as well as bank in case of any fraudulent activity. A customer’s location can be traced using IP address or GPS to ensure that unauthorized access from different geographical locations is also avoided.
Using machine learning, AI models can be created and trained on vast amounts of data, enabling the system to segment customers based on their transactions history, their monthly income, and average running balance. The system then assigns each account a risk score, which helps identify the relative riskiness of the account.
AI models are being trained to profile each customer in a company’s database and provide customized services based on the customer’s sex, age, spending behavior, marital status, employment status, etc. For example, a bank offering online account opening services can ask the prospective customer a set of actions to determine their age and employment status and offer them the right account type based on their answers. Similarly, insurance companies ask customers to fill out a short questionnaire and recommend to them the appropriate insurance policy according to their responses. Some telecom companies use AI to provide personalized plans to customers after analyzing their past usage patterns. AI financial services may be influenced by personal web tracking, as companies like Meta and Google are doing for their apps and services.
Banks require a dedicated customer service department that works round the clock to resolve the queries of their large customer base. With the growing competition among banks, the quality of their customer service often becomes a differentiating factor in customers’ minds.
Most banks use customer relationship management software. AI can be deployed with this software to analyze the customer database and gain meaningful insights. AI can be used to segment customers by analyzing their behaviors, which can then be used for initiating targeted marketing campaigns, upselling, or providing customized services.
AI can also aid customer support teams in plenty of tasks, allowing them to focus on the issues that require more thorough human intervention. For example, customers’ frequently asked questions can either be resolved by referring customers to the relevant section of the website’s help page, while the intermediate queries can be handled by AI-driven chatbots. However, complex queries that are not covered by either the FAQs and AI chatbots can be diverted to human agents for offering personalized solutions.
Using the historical data of customer interactions from customer relationship management software, AI can generate product troubleshooting guides to lower the ratio of customers contacting customer support for basic issues. Additionally, the AI system can help identify the areas of improvements by categorizing the type of queries received in a particular period and the average time taken by the customer support to close a query.
AI can gauge the overall customer sentiment by analyzing customer communications with the customer service department as well as the social media mentions of the organization. Integration of AI into the customer support software can drastically improve the department’s performance, while also helping improve customer satisfaction levels.
AI is still in its early growth stage, and with the passage of time, we can be certain that AI technology will get much more advanced than it is now. Before AI and technology, trading stocks and investing was something that only the rich could afford. But with modern technological advancements, individual traders can start investing with only $100 and trade stocks, ETFs, Futures, cryptocurrencies right from their smartphones.
AI technology is behind the rise of innovative products on different exchanges all over the world. Cryptocurrency trading, Fintech companies, NFTs trading, and exchanges like Binance are a product of amalgamation of AI and technology, and it is just a matter of time before a new innovative product in the finance industry brings about a revolutionary change.
The process of investing through portfolio creation and periodic rebalancing can be automated using AI technology. To automate investing and achieve the desired financial growth, an investing strategy has to be crafted and programmed to run on software.
AI, due to its excellent data analytics ability, can carry out investment-related tasks quite well as investment decisions are taken after analyzing historical data. The applications of AI in risk management and anti-fraud or phishing contribute to keeping investments secure and safe from external attacks and internal embezzlement attempts.
AI has wide-ranging applications across various industries, from automotive to education, from healthcare to finance. In the financial services industry, AI has been implemented to carry out risk management, customer services, portfolio management, algorithmic trading, automated trading systems, fraud detection and prevention, security, and customized financial services.
However, with growing use of AI, there have been concerns about data security and breach that could occur by using tools like ChatGPT or other cloud-based AI platforms. While organizations have sufficient funds to develop, train, and deploy their inhouse AI models, individuals or smaller organizations might have to face capital constraints and bear with the limitations that freemium AI providers offer.
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