Machine learning has been an essential topic of discussion in different industries over the past decade. Machine learning is not a new technology; the concept has existed for multiple decades, and the technology itself is more than two decades old at this point. However, the technology was not easy to use and not financially viable for commercial usage until this decade. Like all other businesses, financial organizations are excited at the prospects of machine learning in many different domains.

Machine learning is already being used across the industry for many tasks, including risk & compliance monitoring and reporting.

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What is Machine Learning?

Machine learning is often conflated with Artificial Intelligence, so it is essential to understand the differences between the two. In the realm of computer science, Artificial Intelligence is an umbrella term used for technologies that help computers act more intelligently. When most people hear “Artificial Intelligence,” they assume it means computer software that can learn anything because that is how A.I. has been depicted across books, movies, television shows, and video games. In reality, A.I. refers to multiple tools and technologies which are automated to various levels.

Machine learning is the discipline of Artificial Intelligence that focuses on enabling software solutions to learn from experience and data. Machine learning works through algorithms that adapt to feedback provided by the user. YouTube, Spotify, and Netflix are all excellent examples of machine learning algorithms. The recommendations for all these services are based on the feedback provided by the viewed. YouTube tracks user likes and how much of a video a user watched to determine what to recommend to them.

Machine learning is not a new technology; the concept has existed for multiple decades, and the technology itself is more than two decades old at this point. Click To Tweet

Difference Between Machine Learning and Neural Networks

There is a difference between machine learning and neural networks, which is essential for risk and compliance professionals. Neural networks are more in-line with people’s perceptions of artificial intelligence and can be considered superior to machine learning. However, it is still an experimental technology used in a minimal capacity for academic and research purposes.

Machine learning can be considered the most advanced information systems possible based on the technology already available. Neural networks are computers that have been designed to resemble the way a human brain works, hence the ‘’neural’’ in the name. Computer scientists are attempting to emulate the way information is passed and processed by neurons. A machine learning system learns through human feedback; a neural network will be able to work independently.

Neural networking could result in significant breakthroughs for risk and compliance automation. Still, it will take time for the technology to mature to a level where something as critical as risk and compliance can use it. Machine learning has already reached this level, so it is now being adopted in different capacities across the financial industry.

Machine Learning in Risk and Compliance Management

Machine learning can be used in the risk and compliance domains to monitor activities and detect problems. Machine learning is not a way to replace risk or compliance professionals within an organization – it is a tool that can be used by risk and compliance professionals to increase the efficiency of risk and compliance monitoring and reporting. Machine learning requires manual intervention from experts, and it learns from these interventions.

Machine learning is currently being used in a limited capacity in risk and compliance management. While machine learning was an easy implementation for tech companies, businesses in highly regulated sectors of the economy (like finance and healthcare) have to be much more careful when using new technologies. Regulatory compliance and risk management are considered to be too vital to automate right now completely. Most machine learning solutions in risk and compliance currently focus on detecting fraud. The technology is currently not being used commonly to manage risk and compliance levels throughout the organization.

As machine learning technology evolves and matures, it will be available for risk and compliance teams working in the financial sector. Machine learning has the potential to automate risk and compliance monitoring completely. Manual monitoring uses a lot of resources which would then be freed up. Risk and compliance experts will have more time to look at the strategy behind their risk and compliance initiatives.

Enterprise Risk Management Software

The intelligent approach right now is to digitize the risk and compliance management frameworks. Using a risk and compliance management platform generates performance metrics and data essential for any machine learning solution. The data generated by risk and compliance platforms are used for predictive analytics, early warnings about emerging problems, and more. Interested in seeing how your organization can benefit from the latest risk and compliance technology? Get in touch with our experts for a demo of Predict360, the American Bankers Association endorsed solution for risk and compliance.