Illustration of magnifying glass over analytics charts to demonstrate how financial institutions can use machine learning technology for specific business value

Machine learning is a hot topic in the banking industry today. But machine learning is much more than a buzzword. In fact, machine learning is a game-changing technology that provides real-life business value for fraud detection and prevention. That’s why banks and financial institutions looking to invest in machine learning systems need to distinguish between the buzz and real-world business value.

Understanding Machine Learning’s Business Value

Machine learning is an application of artificial intelligence (AI) that is capable of learning new information and insights without specific programming. Machine learning algorithms are trained on data and use these data patterns to make informed decisions or predictions. 

Banks and financial institutions can tap into machine learning capabilities for a wide range of business use cases, including fraud detection and prevention. Once the organization has decided to make the investment, the question is how best to implement a machine learning model for your specific business. 

There are a number of considerations to take when investing in machine learning. Some organizations decide to build their own machine learning models. This is certainly a fair option. However, this approach is fraught with risk. In fact, in some cases, banks that pursued building their own models had turned to outside vendors for assistance when the management of their ML framework became too cumbersome,  didn’t behave as expected, or the development cycles became too slow. That’s why partnering with a vendor is safer for building machine learning models for their specific business use cases. Especially for mission-critical uses like real-time fraud detection. 

3 Steps to Maximize Machine Learning’s Business Value for Fraud Detection

Banks and financial institutions can’t afford to view the selection of a machine learning system as simply a “check the box” task. A bank’s machine learning model may produce inaccurate results if the planning is insufficient or implementation isn’t handled correctly. 

It’s important to consider the investment of time and resources required and to ensure the machine learning algorithm addresses the bank’s specific needs. Determining the key performance indicators (KPIs) to prioritize is the first step banks and financial institutions must take when making a machine learning investment.

Banks should emphasize the following KPIs in order to maximize machine learning’s business value for fraud detection. 

  • The System Needs to be Fast

Any machine learning algorithm must be capable of rapidly delivering answers. Whether a customer is shopping at a supermarket or a shoe store, they want their transaction approved without delay. If the system can’t make fast and accurate predictions, it could block thousands of legitimate transactions or lose millions of dollars to organized fraudsters.

  • The System Needs to be Designed for an Adversarial Fraud Market

While the system must be fast enough to approve legitimate transactions, it must also be flexible enough to detect different types of fraud. The fraud market constantly evolves, forcing banks to play a game of cat-and-mouse in which they must continuously update their fraud detection capabilities. In other words, a bank’s machine learning models must be able to evolve to meet new fraud tactics and challenges.

  • The System Needs to be Future-Proof

Shifting fraud patterns aren’t the only factors to consider. Banks must also prepare for shifting financial regulations. In the European Union, for example, PSD2 required more advanced authentication methods. Today, liability for scam losses is shifting to banks in some major markets. Banks should look at these regulatory shifts and ensure their machine learning models are flexible enough to adapt to updated regulations.

Implementing the Right Machine Learning Business Use Cases for Fraud Detection

By leveraging advanced algorithms, organizations can analyze vast amounts and take proactive measures to stop financial crime. This includes preventing financial losses and protecting the financial institution from a reputational hit.

But it’s important to note that not all machine learning models are the same. Banks need to consider which specific use cases their organization wants to prioritize before making an expensive investment decision. In order to make the most of their machine learning investments, banks should focus on top business priorities. But that’s not all. Banks must also invest in building a data culture to ensure the model’s success.

Machine Learning Business Use Case 1. Reduce Fraud Losses

Machine learning enables organizations to detect and respond to anomalies and suspicious activities rapidly. By leveraging historical data and real-time analysis, machine learning algorithms can identify emerging fraud patterns and stay ahead of evolving fraud techniques. This proactive approach helps organizations prevent fraud losses and safeguard their financial assets.

Machine Learning Business Use Case 2. Minimize Customer Friction

False positives are one of the biggest pain points in detecting fraud. The last thing a financial institution wants is to inconvenience legitimate customers while blocking potential fraud. Machine learning algorithms analyze multiple data points, enabling more accurate fraud detection and reducing the number of false positives. By enabling a seamless and secure customer experience, financial institutions can improve customer satisfaction and loyalty – and stand apart in a competitive market.

Machine Learning Business Use Case 3. Maximize Operational Efficiencies 

Machine learning for fraud detection streamlines operational processes. Automated algorithms can free up valuable resources, allowing bank personnel to intervene or further investigate more complex fraud cases. As the algorithms regularly learn and improve, there is a reduced need for manual rule updates and improved operational efficiency.

Remember to Build a Data Culture for Reporting and Observability 

Organizations need to foster a data culture in order for a machine learning investment to be effective. Financial institutions should establish robust reporting mechanisms and AI observability to monitor and refine machine learning models. This gives personnel the ability to monitor models that are already in production and adjust them as needed. As they consider their machine learning investments, organizations must also consider how they will build a culture in which data scientists, fraud analysts, and other stakeholders collaborate and innovate effectively to continuously improve fraud detection strategies.

Machine learning is more than a technological buzzword. Implementing machine learning use cases for fraud detection offers significant business value. But machine learning is an expensive and careful investment for any organization. Banks need to look beyond the buzzword and build a strong data culture to ensure the ongoing effectiveness and accuracy of machine learning-based fraud detection systems.