In the financial services industry, machine learning is used to validate transactions, detect fraud, make recommendations, and more.
Artificial intelligence (AI) is the new frontier in financial services. The technology has the potential to improve customer engagement, streamline operations, and reduce costs.
The financial services industry is already using AI to improve customer engagement and operational efficiency, and more. In fact, a recent study by the Digital Banking Report showed that 54% of respondents (banks and credit unions) ranked the use of AI more important than even improving their customer experience.
AI can help you automate routine tasks, make better decisions, and increase revenue.
Top AI Use-Cases in Finance
Here are some ways AI can help your financial services business:
- Automate routine tasks: AI can help you automate tasks that are repetitive or require human input. For example, it can help you automate tasks that require human judgment, such as reviewing loan applications and flagging those that may be fraudulent.
- Improve decision making: AI can help you make faster decisions by analyzing large volumes of data in real time. It can also help you make better decisions by providing insights into patterns and trends in your data.
- Increase revenue: AI can help you increase revenue by identifying opportunities to sell more products or services to your existing customer base. For example, it can identify patterns of high spending that indicate customers may be interested in additional products or services.
Building a Fraud Detection Model
Let’s explore a practical example of using no-code AI for financial services, by building and deploying a model to predict financial fraud.
We’ll use this credit card fraud dataset from Kaggle to train an AI model. The dataset contains credit card transactions made by European cardholders in just two days in September 2013, creating a whopping 284,807 rows of data, with just 492 cases of fraud.
Because of the sensitive nature of financial data, the actual meaning of the data is obscured through statistical transformations.
Manually detecting cases of fraud would be like playing the world’s most difficult game of Where’s Waldo? Instead, machine learning can be used to find patterns in fraudulent transactions and automatically surface fraud.
Let’s upload that Kaggle dataset to Obviously AI. At the top left, we'll click “Add Dataset” to add the Kaggle dataset.
Due to the size of the dataset, we’ll upload it to Dropbox, and then use Obviously AI’s Dropbox integration to connect it.
After the dataset is uploaded to Obviously AI, make sure that the column “Class” is selected—this is the metric that describes whether a transaction was fraudulent or not. Then, hit “Start Predicting,” and a series of machine learning models will automatically be made in the background to predict fraud.
After a few moments, you’ll see your AI model report, with a number of tabs to explore the model and make predictions.
Deploying the Fraud Detection Model
Now that you know how to build an AI model, it’s time to deploy it.
In the “Export Predictions” tab, you can upload new data to make predictions, export the model as a web app in one click, or make predictions via API.
The “Export as Web App” is the simplest option, as getting a link to make predictions with your AI model is just one click away.
You can use the API even if you’re non-technical, with no-code automation tools like Integromat and Zapier.
Building Any Financial AI Model
The steps we’ve used to build and deploy a fraud detection model can be easily replicated for any financial metric.
For instance, you could use no-code AI to predict default rate, predict cash inflows and outflows, or predict client price sensitivity.
As long as you have the historical data, you can build and deploy AI models. What used to take months and cost hundreds of thousands of dollars can now be done effortlessly, in minutes.
We believe AI is the future. It’s not hard to see why: with AI, you can optimize any KPI, and the promise of AI-based fraud detection, lending, risk management, and more can help improve financial businesses.
Want to learn more? Take a look at our Ultimate Guide to Machine Learning!
Ready to see it in real life? Book a demo with our team today to see it person.