We all know that today's modern finance is different from the financial world of the last few decades. The scale of transactions has increased, especially for startups. We have been using credit cards since the 1950s and today there are approximately 40 billion annual credit card transactions in the United States alone.
Financial access has increased with the developing technology and this has caused a significant increase in transaction volume. In 1990, about 14% of consumer transactions were electronic payments. Today, about one-quarter of consumer payments are made in cash.
Modern finance has become complicated today. Today's ATMs have become complex machines, even for their original counterparts. In the 1960s, you could withdraw money from an ATM. But today you can use it for loans, deposits, bills, cashing checks, loan payments and many more.
All of this increased financial complexity requires the improvement of existing algorithms. Artificial intelligence solutions are required to authenticate financial transactions, detect fraud, and review loan applications, among other things. Financial experts agree, with two-thirds of financial services leaders expecting to use AI on a large scale within the next few years.
At the same time, the complexity of artificial intelligence technology has also increased. Traditional AI is difficult, expensive, and often slow to deploy for many financial institutions, as it requires data scientists and software development. For this reason, codeless artificial intelligence platforms are needed and machine learning models are created for financial institutions with an easy-to-use, user-friendly interface.
At the same time, companies face a lack of knowledge and high costs in implementing artificial intelligence solutions. In a 2017 Deloitte survey, 47% of companies have difficulties integrating AI solutions into their systems. Also, 40% stated that they found the cost high. And 37% said managers don't have enough expertise in integrating AI into their systems. AI developers, on the other hand, are trying to find a solution without the need for coding knowledge, high costs, and experts. That's what we call No-Code AI.
What is no-code AI?
No-Code AI is a system that enables businesses to perform many operations such as data classification and analysis through a user-friendly interface without the need for coding and technical knowledge. No-Code AI is presented with a specially developed model such as drag and drop, which the enterprise can easily integrate into its existing system and use quickly. The whole purpose here is to be designed in such a way that the user can easily use it.
Let's look at a few practical examples of No-Code AI applications in finance.
Detecting Financial Fraud with No-Code AI
As we said before, the annual number of credit card transactions in the USA alone is about $ 40 billion. It is very difficult to detect frauds in all these transactions. But with payment card fraud of approximately $30 billion per year, over a third of these losses are in the United States, this is a very important task to handle.
It is clear that trying to detect fraud in billions of transactions is a very difficult task. But with the intervention of machine learning, huge amounts of all this data are scanned very quickly and patterns in fraud can be uncovered. Models can be parsed into text fields with the help of natural language processing and categorize all operations.
Approving Loans with No-Code AI
Automatic loan segmentation and approval is one of the most important no-code AI applications. In particular, the 5.9%loan delinquency rate means billions of dollars of loss.
One of the most important criteria in lending is to lend to people who are likely to repay. But it's getting harder to predict whether people will be able to repay these debts. While banks are constantly looking for solutions to solve this problem, artificial intelligence can help at this point.
Actually, lending is a big data problem so it can be easily solved with machine learning. The more data you have about the borrower, the better you can evaluate the amount of loan that can be given. For example, many criteria such as collateral (home, job, car, etc.), inflation, economic growth, credit scoring can be examined to determine the value of a loan. With No-Code AI, all this data can be analyzed consistently.
Predicting Financial Distress with No-Code AI
Another application example for No-Code AI is that it can predict financial distress and bankruptcy. Bankruptcy carries a huge risk for investors who do not have portfolio diversification. Big automaker Hertz is a great example of this. Hertz's bankruptcy caused billions of dollars in losses. We can use no-code AI platforms to predict financial distress and thereby reduce risk.
Trading on Financial Sentiment with No-Code AI
We talked about minimizing risk with artificial intelligence. But it can also be used to find upside financial opportunities. For example, we can measure the sentiment of upstream social media posts like Elon Musk's tweets.
As you know, Elon Musk's tweets can move the markets dramatically. By performing sentiment analysis, very profitable transactions can be made. For example, Elon Musk tweeted “Gamestonk!!”, Gamestock shares rose 157%. Or we experienced the same events with Dogecoin and ETSY.
Conclusion
In this article, we examined how ai is changing business financially and how ai models provide benefits for businesses. Of course, integrating Code-Free AI applications into your business is no simple matter. It is important to analyze how all AI solutions should fit your business. In some cases, you may prefer a system specific to your business instead of codeless systems. Of course, this can be revealed by an in-depth analysis of your business needs.
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