How RPA and Machine Learning Address Business Use Cases
By integrating machine learning (ML) and artificial intelligence (AI) into Robotic Process Automation (RPA) you can perform intelligent automation of repetitive tasks and operations with layers of human perception, judgment, and prediction.
What is machine learning?
Machine learning is an AI technology that allows systems to learn and improve from prior experience without being explicitly manipulated or programmed. It focuses on creating computer programs that can access data and learn on their own.
The learning process starts with observations or data such as direct experience or instruction to look for and establish patterns in data. This process allows for better decision-making based on the examples humans provide. Moreover, machine learning strives to enable computers to learn independently, with minimal or no human involvement, and modify their actions correspondingly.
Use cases solved by RPA and machine learning
Besides streamlining business processes, here are more specific use cases of machine learning:
Customer service automation
Managing an ever-increasing number of online client interactions has strained many businesses. They don’t have enough customer service personnel to handle the volume of calls they receive, which can cause delays for customers in connecting with a representative. And when customers finally connect, they’re likely to be put on hold as the representative scrambles to gather information relevant to the customer’s request or forced to repeat their request if transferred to another representative for escalated support.
RPA and digital assistants such as AARI help process customer requests faster, automatically gathering and displaying customer information on a representative’s screen so that the representative can focus on the customer conversation with no break in the service. In addition, chatbots and other automated systems, including Google Dialogflow, can fill service gaps, thanks to advancements in machine learning techniques. When implemented correctly, machine learning can streamline problem resolution and provide consumers with helpful assistance that ensures brand loyalty.
Cybersecurity experts have worked hard to respond to the ever-increasing spectrum of security threats as networks become more complicated. It’s challenging enough to keep up with rapidly changing malware and hacking techniques, but the growth of the Internet of Things (IoT) devices has significantly altered cybersecurity, with more ways threat actors can target an enterprise.
Fortunately, machine learning algorithms have enabled cybersecurity operations to keep up with such rapid developments. Specifically, predictive analytics allows for faster detection and mitigation of hazards than ever before. Machine learning can now monitor user activity within a network to detect abnormalities and security vulnerabilities.
Object and document recognition
The technology used to collect and read data has been available for many years. But moving to the next level of teaching computers to grasp what they are looking at has been, until recently, a complex challenge to overcome. Today, more gadgets have object recognition capabilities to address such a challenge, thanks to recent machine learning applications.
In business settings, the most common use case of object recognition is intelligent document processing. IQ Bot and Google Document AI use machine learning to extract structured and unstructured data from various types of documents automatically.
For decades, process analysis was performed most often by top consultancies as one of the most business-critical initiatives. Consultants drew the process flow diagrams and interviewed business users to map and analyze their activities.
Now, machine learning helps to automatically analyze process data in the form of user activities and system logs and detect repetitive patterns, which reflect the opportunities for process optimization and automation. Discovery Bot is an example of a solution automating process analysis.
Online financial transactions are increasingly becoming the norm in today's modern world. Consequently, this has also increased consumer awareness of numerous types of fraud. While people appreciate the convenience of making purchases and payments online, they also want to know that their financial information is safe and secured.
As a response, credit card firms and banks use machine learning algorithms to evaluate enormous volumes of transactional data to identify suspicious behavior. While these kinds of checks and protocols are nothing new, machine learning has significantly optimized the scope and function, detecting up to 95% of fraud and reducing investigation time by 70%. An example is RPA solutions leveraging fraud detection models built with Google AutoML.
More use cases to come
The breadth of applications and use cases for RPA and machine learning will undoubtedly expand in the coming years as technology advances. As the new decade begins, it is essential to be open to using machine learning applications to boost productivity, save costs, improve user experiences, and drive companies towards growth and development.