AI In Fintech

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21 Aug, 2024

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AI in Fintech refers to the extreme or massive use of artificial intelligence (AI) in the operations of financial institutions and similar companies. The primary purpose of this technology is to incorporate AI and in-built bots to ease the platform performance for the customers.

AI has been prevalent in this sector since the late 20th century. It paved the way for the use of artificial intelligence in finance-based solutions. Here, it aims to uplift the existing efficiency to another level for the customers. For instance, a fintech company may deploy algorithms to improve the trading facility. As a result, customers may use them to detect the chart patterns and develop a strategy. Also, it provides ease while trading.

What Is AI In Fintech?

AI in fintech refers to the application of artificial intelligence in the finance sector. It allows firms to use AI in the financial products and services.
The prevalence of the AI has been dated to 1982. At that time, mathematician John Simons used this intelligence to track stocks and predict market trends.
AI is already being used in trading platforms, the banking sector, risk management, personal finance, fraud detection, and loan processing.
According to statistics, the AI market is supposed to reach a market size of $49.83 by 2028.

AI In Fintech Explained

AI in fintech refers to the widespread of artificial intelligence in finance related companies and other institutions. The origin of AI can be traced back to the 1900s. However, the first use of generative AI in fintech occurred in 1982. At that time, mathematician James Simons utilized AI to gather data, analyze statistical trends, and predict the stock prices in the market. Later, the prevalence of AI in fintech rose tremendously.

In the 1980s, when Expert systems became popular, users could easily predict market trends. Similarly, even banking companies joined the trend of AI in fintech. Later, in mid-2019, almost 90% of the fintech companies had already started using AI. However, AI fintech companies may incorporate artificial intelligence for different motives.

At the core of fintech companies, AI has different mechanisms and components. It includes generative adversarial networks (GANs) and variational autoencoders (VAEs). They intend to collect the existing data (input) and create new samples relating to the underlying distribution. For instance, the trading platform's algorithm may input the historical prices and forecast future trends. As a result, it leads to higher efficiency and time optimization. However, training the AI model is necessary to implement the model on the platform.

During this training period, the model may also include different factors. It mainly consists of variables from the data set itself. Such inclusion helps in understanding the behavior and attitude of the model in the later stages. Once selected, the model is developed. It may be a regression model, classification model, or time series analysis model. However, neural networks and deep learning models are fruitful in complex structures. After training ends, the AI model is back-tested and optimized for better efficiency.

Examples

Let us look at some real and hypothetical examples of AI in the fintech market to comprehend the concept better:

Example #1

Suppose John is an intraday trader who invests in financial instruments. He has traded for more than six years. During this time, he used to collect the historical prices of stocks, analyze, and invest likewise. But this technique consumed a lot of time. Also, John always wanted news to be included on the platform. As a result, the trading platform released an AI model, EasyTicker, for trading sessions.

As per this model, this trading platform would collect the historical data of the stocks and predict the future trend for them. So, if a stock trades under the resistance level (r low) today, the AI model will estimate future prices, which is possible by using news, chart patterns, and famous strategies. Also, it has a chatbot that would reply to the users on the executive's behalf. Thus, the queries of the traders, especially John, end at the first stage itself. Therefore, he was able to trade in the market effectively.

Example #2

According to a recent statistics report as of November 2023, 73% of employees (mostly financial service executives) feel AI will soon take their jobs. Of the whole fintech industry, 40% consider AI a boon, and the rest see it as a foe. Yet, a certain percentage of employees (66%) feel generative AI in the fintech industry will increase their revenues by 20% in the next three years. However, 57% still expect to lose their jobs with this inclusion.

At the same time, another report suggests that the market size of such AI fintech companies will be $42.83 billion in 2023 and $49.42 billion in 2028.

Use Cases

As AI is a progressive factor of technology, the application and role of AI in fintech have increased at a higher rate. Let us look at some use cases in this industry:

#1 - Stock Market Trading

The foremost application of artificial intelligence is visible in trading platforms. Instead of humans, they use virtual chatbots and advisors. AI helps solve customers' issues and queries related to trading, resulting in a hurdle-free trading process. For example, the AI chatbots utilized by QuantConnect and Robinhood help provide trading advice.

#2 - Banking

Another application of generative AI is in the banking sector. Many financial institutions use virtual bots to serve customers. AI helps provide seamless service to customers by answering their queries. So, if a person needs details about loan applications or account details, the AI bot can retrieve them quickly. For instance, the AI chatbots of Bank of America (Erica) and Ally Bank help customers manage their finances.

#3 - Risk Management

Managing finances and optimizing risk is a crucial part of financial management. However, it may not be easy to do so without a wealth of knowledge. Therefore, some financial institutions provide virtual chatbots and software that can help mitigate risk. These AI models evaluate credit and financial risk associated with investments. For example, AI fintech companies like Blackrock and Kabbage use AI for risk management.

Also, some AI models like Wealthfront and Betterment can help in providing better financial advice.

#4 - Fraud Detection

AI is also commonly used in fraud detection when dealing with large volumes of financial data. Financial authorities heavily utilize AI to detect any high-volume trading by any trader. For instance, Feedzai and Kount provide solutions that help to mitigate fraud in real-time scenarios.

Benefits

There are several benefits of using AI in fintech companies. Let us look at them:

AI helps financial sectors to incorporate technology with advanced benefits. It serves as a tool to increase efficiency and customer experience.
It reduces the average time consumed by using artificial intelligence to complete complex jobs. As a result, the customer queries are resolved in no time. Also, it minimizes the risk of human error.
This inclusion allows users to estimate future trends and detect any fraud. It can also monitor and track the real-time market data.
The role of AI in fintech increases the company's performance and growth potential and reduces the firm's overall cost.
It processes data quickly for better decision-making. It also provides various possibilities for a question, which also enables effective decisions.
AI also tries to measure an individual's creditworthiness in loan facilities. It compares a wide range of parameters to evaluate credit risk and credit score. Besides, firms can use AI to analyze users' behavior.

Future

Many industry experts and financial enthusiasts have listed various benefits of AI. Also, they have predicted a better future for AI in the fintech market for the next decade. According to this survey report, the global market size of the AI industry will increase by a compounded annual growth rate (CAGR) of 2.91% by 2028. This rise will majorly occur in the blockchain and other sectors.

Among all regions, North America and Europe have the highest adoption rates. A report suggests that the European region has surpassed 841.5 million fintech users, and this number will exceed 900 million by 2027. In contrast, Asia-Pacific has witnessed the fastest-growing market.

In addition, the Financial Conduct Authority's (FCA) sandbox allows fintech companies to test their AI products in controlled environments and then release them to the public. The Markets in Financial Instruments (MiFID II) has also released a revision that allows fintech companies to access such markets.

As financial institutions collaborate with other firms, the use of AI will also increase. For example, an insurance company can partner with healthcare firms to reduce patients' medical costs. Likewise, the application of artificial intelligence is also prevalent in the blockchain ecosystem. It brings transparency and security to cryptocurrency transactions.

Frequently Asked Questions (FAQs)

1. What are some popular fintech AI startups?

Many fintech AI startups are popular in the finance space. They are as follows:
● Numerai (A fintech firm that works as a hedge fund)
● Kabbage and Upstart (They provide a lending facility by using AI as a credit score tracker)
● Cleo (It has an AI-based assistant that provides financial advice).

2. What are the challenges of using AI in fintech?

Following are the limitations or issues associated with the use of AI in fintech. Let us look at them:
● Wrong predictions or inaccurate financial advice.
● Issues regarding data breach and security.
● Loss of employment of existing employees in this sector.
● Certain regulatory challenges also exist with this boon.

3. Will AI take over finance jobs?

AI may snatch a few jobs from people to a certain extent, but it still cannot dominate. Sometimes, the AI bot experiences issues and may give wrong results, which may lead to huge losses. At such a stage, employees are needed to give financial advice. However, AI can certainly transform the way businesses operate.