Modern finance is an entirely different world from the financial industry of even a few decades ago.
For starters, the scale of transactions has massively increased. Credit cards have only been around since the 1950s, and there are now approximately 40 billion annual credit card transactions in the United States alone.
The increase in transaction volume is primarily due to increased financial access through the use of technology. In 1990, electronic payments made up around 14% of consumer transactions. Today, the situation has flipped, as most transactions are electronic, and just one-quarter of consumer payments are made with cash.
Modern finance has also become highly complex. Even ATMs are complicated machines today compared to their original counterparts. In the 1960s, an ATM was a cash dispensing machine. Today, they can often also be used for deposits (enabled by computer vision), credit, payment of bills and loans, cashing checks, replacing debit cards, and more.
The increased scale and complexity of finance means that traditional algorithms no longer suffice. AI solutions are needed to uncover financial fraud, validate financial transactions, review loan applications, automate workflows, and more. Financial experts recognize this, as nearly two-thirds of financial services leaders expect to be mass AI adopters in the next two years.
That said, AI technology has increased in complexity as well. Traditional AI requires data scientists and software development and therefore is slow, expensive, and difficult to use for many financial institutions. Fortunately, emerging no-code AI platforms break down these barriers and enable business users to create ML models via an easy-to-use visual interface.
Let’s dive into a few practical examples of no-code AI applications in finance.
As we’ve highlighted, there are around 40 billion annual credit card transactions in the US.
Detecting fraud in these transactions is like finding the veritable needle in the haystack. Still, it’s a vital task, as payment card transaction fraud costs nearly $30 billion a year, with more than a third of those losses attributable to the United States.
Fraud is only on the rise during the pandemic. New types of fraud are constantly emerging, such as synthetic account fraud, in which false credit accounts are made and abandoned after withdrawing a large amount of credit.
Detecting fraud manually, across billions of transactions, would be an essentially pointless task. This is where machine learning comes in, which can quickly scan massive amounts of historical data to uncover patterns in fraud and use that pattern recognition to uncover fraud in new financial transactions in real-time. Models can even parse text fields via natural language processing - making it easy to categorize transaction types.
With Akkio’s easy automl training process, you can build and deploy an AI model to predict fraud in minutes. To get started, you can sign up for Akkio for free. Every machine learning task, including fraud detection, requires a historical dataset to teach the model how to recognize patterns for that task.
In this case, we’ll use a Kaggle dataset of credit card transactions, with around 285,000 rows - relatively few of which are cases of fraud. The actual financial information is anonymized to protect the privacy of the users who generated this real-world data. However, we can still use the data to create a machine learning model.
We’ve already included this specific dataset on Akkio as a sample for demonstration purposes, which you’ll find on the homepage, titled “Credit Card Fraud Demo.”
Selecting the Credit Card Fraud demo, we can see three steps: Data input, prediction, and output (or model deployment via Zapier, in this case). The data input step shows us a scrollable overview of our dataset and allows us to modify each column’s data type or simply replace the table.
The next step, prediction, is where we select the column from the table that we’d like to predict. In our case, it’s a column called “Fraud?” With Akkio, we simply check the box next to “Fraud?” and we’ll have an accurate fraud detection model in seconds.
Finally, we can deploy the model in any setting, via API, or without any code using an automation tool like Zapier.
You can find an example of deploying this model via Zapier to set up fraud alerts and notifications on our full use-case page.
In short, we can send new financial transactions to our Akkio model, which then predicts whether or not it’s fraudulent. If Akkio predicts that the transaction is fraudulent, we can send an SMS warning to the customer, all without a line of code!
Another powerful no-code AI use-case for finance is automated loan segmentation and approval. The nation’s overall loan delinquency rate hovers around 5.9%, resulting in billions of dollars in losses.
Of course, lenders try to only lend to those who are likely to pay back their loans. Still, with tens of millions of Americans taking out personal loans, it becomes difficult to effectively and efficiently predict whether or not any individual loan will be repaid.
No-code AI again presents an effortless solution. For example, with this dataset of vehicle loans, we could build a loan default prediction model in much the same way as we built the fraud prediction model. Simply upload the CSV to Akkio, and in this case, select the column “loan_default,” and then deploy the predictive model in any setting.
Such a model flow could be used to auto-approve applicants with a low probability of default.
Another important use-case is to predict financial distress, or in the most severe form, corporate bankruptcy.
Bankruptcy is a considerable risk to investors, especially those without significant portfolio diversification. For instance, the automaker giant Hertz went bankrupt, leading to billions of dollars in losses.
Investors and portfolio managers can mitigate risk by using no-code AI platforms to predict financial distress. We can use this Kaggle dataset to predict financial distress, simply by uploading it to Akkio and selecting the “Financial Distress” column.
This model could then be used to provide early warning, such as by emailing investors when an asset in their portfolio is at risk of dramatically dropping in value.
Beyond minimizing risk, no-code AI can be used to find upside financial opportunities. We’ve written a guide on using Akkio to measure the sentiment of any social media post or tweet, such as financial tweets by Elon Musk, to find investment opportunities.
Real-time sentiment analysis can be incredibly lucrative, as Elon Musk’s tweets have dramatically moved markets time and time again. For instance, when Elon tweeted “Gamestonk!!,” the shares of Gamestock “rocketed as much as 157%.” Similar events occurred with assets like ETSY and Dogecoin.
The steps we used earlier to build and deploy a fraud prediction model with no-code AI can be replicated for practically any financial metric.
As you’ve seen, as long as there’s a historical tabular dataset available, you can simply select the column you’d like to predict, and a machine learning model will be automatically generated in the background.
From there, you can deploy your model in virtually any setting via API and even deploy without code by using an automation tool like Zapier.
No-code AI is the future of machine learning tools for finance, as it allows business users to solve increasingly large, complex problems effortlessly across a wide range of use cases.
What once took large technical teams many months and millions of dollars—like building and deploying accurate fraud detection models—can now be done in minutes with no-code AI tools from Akkio. No writing code or data science experience needed.