As artificial intelligence (AI) starts to enter the financial technology (fintech) sector, there is a lot of buzz and excitement around what it could mean for the future.
Artificial intelligence is the next frontier in fintech, and it is changing the way we do banking. Imagine a financial world where you can open an account with your phone. You don’t have to visit a branch or speak to another human being, which means banks can serve larger segments of people and communities with personalized products and services leveraging technology without adding additional resources. And this is just one of many ways AI will make banking easier for everyone.
By automating complex processes and making data-driven decisions, AI streamlines financial operations for businesses and consumers alike.
This article will explore the technologies AI is based on and discuss its key benefits.
What Type of Technologies Are Based on Artificial Intelligence?
AI is composed of many different technologies, all advancing at an increasingly fast pace. Following are the AI technologies most relevant to fintech.
Machine learning enables software robots to accomplish predefined tasks without being programmed for each specific scenario. For example, machine learning algorithms are used in banking to detect fraud, predict how many loan applications a bank will receive in the next ten years, and determine creditworthiness.
Big Data Analytics
AI needs big data to operate, so fintech companies must gather and store substantial amounts of financial data. This need for capacity is a challenge since many banks and financial institutions have outdated IT infrastructures that cannot accommodate the huge amounts of digital information they must gather and analyze.
Unlike machine learning, deep learning uses a hierarchical approach to processing data. Hierarchical means that it can process multiple layers of information at once. AI systems can process raw data to determine the initial conditions of these data sets necessary for a result.
How is AI Being Used in Fintech
Application of AI technologies covers wide segments of financial services and Fintech solutions, from customer experience and interaction to back-end processing.
Front End Approach
In this approach, chatbots are providing customers with 24/7 access to information. Rather than call or email for support, clients can use messaging channels like Facebook Messenger and WeChat to interact directly with virtual assistants.
This approach has a more practical and functional purpose, as AI is used to help banks with machine learning and deep learning algorithms. In this approach, AI is used to perform tasks for financial decision-making that would typically require a lot of time and resources.
The Benefits of Using AI in Fintech
Safe, Secure, and Accurate Banking
AI is a powerful technology that can streamline banking across the board. Compared to using purely manual processes, AI provides greater speed and accuracy. In addition, it saves money for banks and financial institutions since it makes many tasks cheaper and easier to perform.
Better Customer Experiences
As more financial institutions adopt AI and new technologies continue to evolve, we’ll see greater improvements in customer satisfaction. For example, it’s likely that as AI becomes more advanced, we’ll see better customer experiences with financial applications, chatbots, and virtual assistants.
Less Employee and Greater Customer Handling Capacity
According to the statistic from Statista, by 2020, virtually all financial services businesses had adopted artificial intelligence (AI) technology to cut operating expenses. However, among those interviewed in North America, 37% believe that the major benefit AI will provide to their organization is increased staff capacity to deal with the volume.
In addition to streamlining operations, AI is also being used for research, making discoveries in machine learning and deep learning algorithms. As this type of technology improves, fintech firms will serve more people without adding more staff, making banking easier for everyone.
Use Cases of AI in the Finance Sector and Fintech
Having looked at the benefits of using AI in the financial industry and banking, let’s examine some use cases.
AI can process huge amounts of data to detect unusual activity, flagging unusual spending patterns and other irregularities which can help banks stay ahead of fraudulent transactions while serving their customers well.
Using AI, companies can divide their clients into segments based on their similarities and differences in behavior, interest, and demographics which helps companies provide a better customer experience.
Credit Scoring and Loan prediction
AI can predict how likely an individual will default on a loan or miss payments using smart algorithms. Using this information, fintech lenders can make better decisions around giving out loans.
Financial Investing Advice
Using AI to process large amounts of financial data, companies can provide their users with suggestions on adjusting their personalized portfolio management. Since AI can gather information from various sources simultaneously, it provides a complete picture that may not be possible for a human analyst to be able to achieve.
Customer Relationship Management
Perhaps one of the most popular applications of AI, using virtual assistants and chatbots to interact with customers on a 24/7 basis helps financial service providers save money while keeping their clients happy.
Similarly, AI can create trading algorithms that use market data to make projections and provide advice. Other algorithms can identify patterns in the market to determine when is the best time to buy or sell.
As we can see, there are a variety of applications for AI in the finance industry and fintech. In the coming years, as financial firms continue to adopt AI and new technologies, we’re going to see many improvements in how financial services are delivered.
In addition, smart algorithms can help people make better decisions regarding their money, whether they’re inexperienced investors or seasoned professionals.