Just think about it. Nowadays, there’s everything from cryptocurrencies to a multitude of fintech startups offering anything from automated loan applications to insurance, and robo-advisors that allow investors to invest far cheaper than ever before.
Considering this, it’s easy to see that the finance industry today is a completely different animal compared to the finance industry of only a few years ago.
For one, the number of transactions has increased exponentially over time. For example, while credit cards have only been used since the 1950s, now every year there are now about 40 billion credit card transactions in the United States alone.
There’s no doubt that this figure has been helped along by the availability of technology and the popularity of the internet. So, for instance, in 1990, electronic payments only constituted a minimal portion, about 14%, of all consumer transactions. Now, in 2021, the converse is true. In fact, most transactions nowadays are electronic, and only one-quarter of payments are made using cash.
And with more electronic payments and more ways to pay, comes more complexity. Just look at the humble ATM. In the 1960s, it was simply a cash dispenser in its simplest form. A customer would insert their card, provide their PIN, and get cash.
Today, they are used for everything from deposits to credit, bill and loan payments, cashing checks, and even allows customers to replace their debit cards, to name but a few.
Unfortunately, the sheer scale of the finance industry today means that humans are simply incapable of identifying financial fraud, validating financial transactions, reviewing loan applications, automating workflows, and more. Likewise, algorithms of old are simply not good enough anymore. And financial experts recognize this.
In fact, it’s expected that AI will have a significant impact on the financial industry within the next two years.
And therein lies the problem. Over time, AI technology has become more complex as well. Traditionally, to implement it required a team of data scientists and software developers. As a result, it was slow, expensive, and challenging to implement for many financial institutions.
Luckily, emerging AI platforms provide the best solution to democratize AI and enable businesses to develop machine learning models via an intuitive visual interface.
With that in mind, let’s look at some examples of how AI can be used in the finance industry.
The United State’s overall loan delinquency rate is about 5.9%. Let that number sink in, almost 6% of consumers fail to pay back their loans. Obviously, this results in billions of dollars in losses to the industry.
Sure, lenders do their best to only lend capital to those customers who are likely to pay it back. But the problem is, with tens of millions of new personal loans every year, it becomes challenging to predict whether a customer will pay back their loan or not.
So, imagine it was possible to predict which customers are more likely to pay back their loans and which are not. Actually, it is, with AI. This makes it a perfect use case for AI.
In fact, AI again offers lenders an effective solution to this challenging problem. With it, lenders can analyze data of previous loans and develop a loan default prediction model based on their historical data. This model could then be used to approve applicants who are more likely to pay back their loans.
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Another place where AI can be used in the financial industry is to predict financial distress or corporate bankruptcy. Financial distress or bankruptcy poses a significant risk to investors. This is even more so for those without enough portfolio diversification to hedge against it.
Investors and portfolio managers can minimize this risk by predicting financial distress with AI platforms. With the right model, they’ll know beforehand if and when financial distress becomes a possibility.
Investors could then get out of the market to limit risk, or portfolio managers could let their investors know that their portfolio is at risk due to an asset that is posed to drop in value substantially.
As we’ve mentioned earlier, there are around 40 billion annual credit card transactions in the US. Detecting fraud in these transactions is like finding a needle in the haystack.
Yet, despite this challenge, it’s crucial, as payment card transaction fraud leads to losses of almost $30 billion a year. The US alone accounts for over a third of these losses.
And the COVID-19 pandemic brought no reprieve as fraud is only increasing simply because more people are buying online than ever before. Also, new types of fraud and more sophisticated methods of perpetrating it are constantly emerging.
It’s easy to see why trying to detect fraud manually under these circumstances would be a fruitless exercise. This is where machine learning comes in. With it, it’s possible to quickly scan huge amounts of transactional data to uncover patterns in fraud.
These patterns can then be used to uncover fraud in new financial transactions in real-time. Models can even be built to parse text fields via natural language processing, which makes it easy to categorize transaction types.
Apart from almost eliminating risk, investors can use AI to find financial opportunities. Here, ML models can measure the sentiment of social media posts. This then enables investors to find the right investment opportunities.
Make no mistake, real-time sentiment analysis can be very rewarding. Just think back to what Elon Musk’s views on Twitter did to GameStop shares. Also, his recent tweets had a significant impact on the cryptocurrency markets. So, trading on sentiment can result in some healthy returns.
Keep in mind, though, that these are just a few examples of the possible use cases for AI in finance. The steps to develop and deploy any of these models can be replicated for basically any financial prediction.
The only thing needed is a historical dataset relating to the specific metric, and an ML model can be built based on the data. Once built, it can be deployed almost anywhere.
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It’s simple, AI is the future of machine learning tools for the financial industry. It allows businesses to solve increasingly difficult problems across numerous use cases.
And adopting AI is easier than ever. What once took teams of data scientists and software engineers a lot of time, effort, and money, can now be done by anyone with access to data in mere minutes by leveraging emerging no-code AI tools, no software or data science skills required.
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