Read through for an overview of a featured topic from our keynote speaker, Yashwant Divakar, Head of Solution Engineering @ HighRadius.
The FinTech industry most likely wouldn’t have been the giant that it is today if artificial intelligence (AI) and machine learning (ML) weren’t incorporated into the complex technology mix that it is composed of. But we’re no longer strangers to these two types of technology as they rose to immense popularity way before the new millennium even began, with ML being recognized as a subset of AI in the 1950s.
But before we further explore AI and ML in the financial setting, let’s first differentiate the two. Although they might seem similar, there are certain capabilities that distinguish them from each other. Here’s a comparison of some of their key characteristics:
- Simulates human behavior
- Develops systems that will perform tasks like how a human would
- Broad range of scope
- Focused on successfully completing tasks
- Performs different tasks, even the most complex ones
Versus MACHINE LEARNING
- AI subfield that learns from past data without human interference
- Instructs machines to learn from data to deliver accurate results
- Limited range of scope
- Focused on accuracy and patterns of data
- Performs only the tasks the machines are trained for
Decision Intelligence, Defined
We know that AI has the capability to influence and make decisions, but how exactly does this work specifically in the financial landscape? How do these help SaaS companies drive their financial strategies toward success?
AI and ML play a pivotal role in the modernization of financial management systems in order for real-time data to be generated accurately and efficiently. This includes establishing a framework that would utilize the joint capabilities of AI and ML, known as decision intelligence.
Theoretically, AI has the ability to synthesize big chunks of information better. After it has finished processing the data, it will produce the possible outcomes and recommendations which are bound to affect the decision you’ll make.
Now let’s look at it from a financial standpoint. Decision analytics are important in improving customer experience, influencing investment decisions, generating client insights, and even tracking fraudulent transactions and fulfilling other security functions. These also enable you to recognize spending patterns and see from the get-go how much impact it will have on your business’ scalability.
Artificial intelligence and machine learning prove to be highly valuable for SaaS firms because of the quick ROI they guarantee, and other long-term advantages when they’re used effectively. Decision analytics are crucial not only in systemizing your processes, but also in ensuring that you’re on the right track, and your finance management strategies are working smoothly.
Learn more about integrating AI and ML into your finance strategy in our Spotlight on Finance event. Save your spot today for June 2nd.