Artificial intelligence is no longer just a thing of science fiction. Nowadays, machines can learn and make decisions on their own, thanks to the power of machine learning algorithms. We explain how AI is being applied to finance here, detailing eight prominent use cases.
Why is AI so important for the finance sector?
In the last five years, the use cases for artificial intelligence (AI) have snowballed in the finance industry. We expect even more progress in the coming decade. According to projections from AI in the fintech industry, the global market is expected to reach $62.65 billion by 2028, growing at a CAGR of 19.5%.
AI helps companies make smarter, faster, and more accurate decisions in the financial sector. Artificial intelligence can assist with fraud detection, customer service, risk management, and compliance.
Regulatory reporting requirements and financial planning are valuable assets in any financial organization.
Financial institutions use AI to improve their business processes, gain better insight into customer behavior, and develop innovative products and services to remain competitive.
Benefits of using AI in finance
Artificial intelligence can improve productivity, efficiency, and accuracy with minimal human effort, which makes it extremely powerful. Here are a few reasons you should be excited about AI in finance:
- Better insights into customer behavior and preferences can be used to design better products, services, and offers.
- Analysts may use artificial intelligence to help improve decision-making processes by making faster decisions based on smarter data analysis.
- Artificial intelligence can help businesses improve their financials by increasing revenue. For example, artificial intelligence can automate tasks to save analysts’ time for more valuable work, like helping clients or generating new business leads.
- AI can help reduce costs in various ways. For example, it can improve the efficiency of investment management processes by automating tasks and reducing human errors. As a result, there will be a lower trading fee for customers.
- Auditing plays a crucial role in finance. A company can reduce internal fraud significantly through AI-powered fraud detection systems that monitor account activity 24/7 and increase profitability at the same time.
- AI offers significant benefits for workflow automation.
Challenges of using AI in finance
- Artificial intelligence technology presents new challenges for data privacy and security. For example, in early 2019, Google admitted to inadvertently collecting personal information from its users’ accounts when using their location history information.
- When developing artificial intelligence systems, biased algorithms are risky due to accidental or deliberate errors in the training process. These errors can result from biases built into existing datasets used for training purposes or even from human bias during development stages.
- Accessing large amounts of high-quality data can be challenging for many companies. This is because it requires significant investment, time, and effort.
- Companies need access to high-quality information before they can train an artificial intelligence system with accurate predictions. This could be about future outcomes related to profit margins or costs associated with running operations such as marketing campaigns.
Top 8 use cases of AI in finance
Artificial intelligence (AI) is already being used in many applications, including credit scoring and fraud detection. Here are a few more potential use cases for AI in finance:
1. Risk assessment
AI helps organizations with risk assessment using big data, fraud detection, and prevention. Risk assessment is critical for any financial institution that deals with lending money to customers or investing in securities.
The use of AI will enhance the ability of banks and other financial institutions to make better decisions about the creditworthiness of potential borrowers. This will reduce the likelihood that they will grant loans for inappropriate purposes, such as financing terrorism.
Using machine learning algorithms (MLAs) and deep learning models (DLMs) AI can detect whether you are eligible for a loan or not by analyzing various indicators like your credit score and income level. It plays an essential role in determining whether you qualify for a loan.
2. Fraud detection and management
More firms are turning to AI-based solutions. This means that fraud detection is becoming an important element of the financial infrastructure of every industry. According to IBM, global fraud losses will amount to $44 billion by 2025, with 72% of company leaders ranking it as a significant issue.
AI can detect fraudulent activities by analyzing the user’s behavior and comparing it with regular buying, selling, and trading patterns. If something is not right, then an alert will be generated.
AI is also good at helping banks with compliance management. This is a process by which they ensure that they follow all laws and regulations.
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3. Financial advisory services
Advising customers on their financial position is one of the most critical aspects of finance. A financial advisor can help you to save money, invest in the right stocks and manage your finances better. However, they cannot deliver accurate advice because they lack the necessary data analysis skills. This is where AI comes into play. It aims to solve data analysis problems and provide accurate financial advice for people who do not have enough time or knowledge to deal with it themselves. By 2025, Statista predicts that there will be 478.89 million users using AI for asset management.
The system could also recommend new investments based on historical trends or show how they fit into an overall investment strategy. AI does the process without having to input any data into an Excel spreadsheet.
For example, let’s say you have $500,000 invested in mutual funds and stocks. You want to know which funds have performed well recently and which haven’t been doing so well over the past six months or years (or longer). AI will allow banks to analyze your investments and provide a customized report on how they’re performing over time. As a result, you’ll be able to make more informed decisions about your assets.
4. Trading
Stock markets have been around for centuries, but their behavior continues to baffle even the most seasoned traders today. This is because there are many influencing factors in determining how stocks move up or down.
AI systems are now capable of learning from past experiences and drawing conclusions based on them (i.e., prediction). We may see more accurate predictions about future market behavior than ever before. This could result in better investment decisions at both institutional and personal levels.
As a result of studying historical data and discovering patterns, AI can predict future prices of stocks, bonds, and other financial instruments. It may also be used to analyze news that has the potential to affect the market.
In addition to analyzing massive amounts of data faster than humans. AI is better at making predictions due to its ability to learn from mistakes. For example, if an AI system predicts a stock will go up, but it goes down instead (a common scenario). AI will incorporate this mistake into its models so that next time it has access to more accurate information when predicting stock prices.
5. Credit decisions
In the past, credit decisions were made almost entirely by human beings. But AI is quickly becoming more accurate than humans in making predictions. Companies like Kabbage use AI to analyze thousands of data points about a business. They pull out patterns that predict whether users will default on loan payments in the future. This data-driven decision-making helps financial institutions save time by making faster and more accurate credit decisions.
AI is already being used to analyze data related to a borrower’s credit history — such as payment history and other account activity. This enables companies to make risk assessments to approve or deny loans.
As of 2023, a survey conducted by the World Economic Forum and the Cambridge Centre for Alternative Finance reports that 85% of financial services organizations are currently using AI in some form.
The technology could provide the user with financial advice or even offer them a new loan at a lower interest rate. AI also has the potential to help banks reduce fraud by preventing hackers from stealing personal information and identity theft.
The technology may be able to make decisions about whether to lend money based on factors such as income, age, and the type of property you want to buy. For example, the lender will refuse your application if you’re interested in buying an expensive home but have a bad credit history. AI could be used to analyze various data points associated with an individual applicant before deciding whether they qualify for a loan or not.
6. Personalized banking experience
A personalized banking experience is the most desirable type of banking service. It allows customers to enjoy a customized experience based on their unique needs and preferences. A bank’s AI-powered chatbot can provide personalized recommendations for each customer. This could be through leveraging data from many sources, including social media, mobile devices, and credit history.
As a result, banks and their customers can communicate effectively, which engages both parties in the process. Moreover, AI can now be used to provide individualized financial advice based on user activity and data collected from other non-banking apps. DBS and RBC are just a few institutions that have already adopted AI-based technologies.
In today’s tech-savvy world, people have become accustomed to using chatbots for everything. For instance, ordering food and booking flights. Therefore, it makes sense for banks to offer customers the option of getting help through a chatbot instead of waiting in line at the branch or making a phone call.
Customers can easily use Chatbots to review the activities on their bank accounts, their account balance, and their monthly payments.
7. Process automation
Process automation is another big use case for artificial intelligence in finance. This involves automating repetitive or complex tasks like market data analysis and investment management.
Tasks can be automated by humans or machines depending on whether they require human decision-making skills or not.
For example:
- A robot could perform simple math to calculate the value of an asset based on its current price. At the same time, humans need to make more complicated calculations based on factors like inflation and interest rates.
- A computer could also perform more complicated tasks. For instance, financial modeling (modeling how certain variables affect an organization’s financial performance). It would involve using lots of data points from previous years’ earnings reports or market trends. A human may have difficulty understanding all this information without prior knowledge about these topics. Hence, the software may be better equipped for this task than humans.
In addition, intelligent software can automate many of these tasks and free up financial professionals’ time to focus on more valuable work. This is especially true for tasks that require human intuition, creative thinking, or emotional intelligence.
8. Regulations and rules
Compliance is big business. And yet, regulations and rules are still complicated to understand. For example, if a company issues a bond in the U.S. purchased by an investor in China who wants to sell that bond in Hong Kong, how do you know if it’s legal? What if you want to invest as an individual rather than an institution? Are there different rules for each country?
This is just one example of how complex these issues can be when it comes down to compliance. It’s also why AI can be helpful: it can help make sense of these complex situations by analyzing vast amounts of information fast and accurately. This is something humans aren’t designed for.
So, how can AI help with compliance? There are several ways it can make things easier for you. For example, an AI system could analyze a company’s financial statements to see if they match up with the rules and regulations in place. This would allow you to identify any issues before they become too big. Similarly, AI algorithms can analyze past data patterns. As a result, if other companies have had similar problems in the past AI could help prevent future violations.
Finance AI applications will only increase
Artificial intelligence is becoming more prevalent in the financial world, but it is still in its infancy. The use cases for AI in finance are numerous, and they will expand as the technology becomes more sophisticated.
This is just the beginning of AI in finance and will continue to grow in importance over time. As more companies adopt AI solutions, we expect that competition will increase and prices will fall even further. This will lead to higher adoption rates across all industries, including finance.
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