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Machine Learning in RPA: A Complete Guide to Intelligent Automation
Learn what intelligent automation is, how machine learning powers it, and who can use this technology to automate their business processes.
Are you ready to eliminate 8 hours of your process every week?
Well, 8 hours saved per employee is a nice first win, but that’s not all.
Your business can tap into new revenue opportunities and see productivity gains across departments by replacing costly, time-consuming manual tasks that rely on outdated processes with intelligent automation solutions.
But if you are wondering what intelligent automation is, how different it is from robotic process automation, and how to identify the right business use case for intelligent automation?
Well, this guide will take you through all of that and explain how machine learning is used in RPA to make it more intelligent and provide an overview of differences with other emerging technologies and business use cases to transform older processes and become more efficient in your daily business operations.
In this guide, you will get an overview on:
- What is machine learning in RPA?
- RPA vs. Intelligent Automation: A complete comparison
- When to use automation and intelligent automation?
- Intelligent automation use cases using machine learning & deep learning models
- How to calculate the ROI of automation investments?
- Case Study: How intelligent automation transformed risk assessment of loan portfolios
Let’s get started.
What is machine learning in RPA?
First, let’s understand machine learning and then get to machine learning in RPA systems.
Machine learning (ML) is a subset of artificial intelligence (A.I.) that enables computers to learn without explicitly programming. In machine learning, algorithms are used to parse data, learn from it, and then make predictions about something in the world. Today, ML algorithms run everything from our search engines, social media feeds, online recommendations, self-driving cars, speech recognition programs, and real-time language translation tools. And there is broad adoption of machine learning in b2b businesses.
Machine learning algorithms build a mathematical model based on sample data – known as “training data” – in order to make predictions or decisions without being explicitly programmed to do so. The primary goal of machine learning is to enable computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning in RPA systems is a type of artificial intelligence (A.I.) that enables computers to learn from past experiences and improve over time.
When ML is integrated with RPA systems, it helps you identify deviations from typical rule-based processes vs. machine learning systems in real-time by processing new data as it comes in. This enables you to make inferences about what‘s happening without being explicitly programmed for every possible situation.
RPA vs. Intelligent Automation: A complete comparison
The rise of RPA and intelligent automation shows no signs of stopping as companies worldwide continue to race towards full digital transformation. As this trend continues, many are asking: what’s the difference between robotic process automation (RPA) and intelligent automation (I.A.)?
As you prepare for your own transformation journey, it’s important first to understand the differences between these two powerful technologies and how they can be combined to create a single unified solution that can deliver real business value.
| Robotic Process Automation | Intelligent Automation using Machine Learning |
| Robotic process automation (RPA) is a rapidly growing technology that mimics human actions and reduces the need for manual data entry. It is designed to automate mundane, repetitive tasks. | Intelligent automation (I.A.) is a more recent technological development that combines multiple technologies such as machine learning, artificial intelligence, and natural language processing to improve automation beyond what RPA can do.
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| RPA is purely focused on automating repetitive tasks by mimicking the way humans interact with their environment.
| Intelligent Automation brings together the various technologies starting from RPA to A.I. and Machine learning with smarter, adaptive, and self-learning capabilities.
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| RPA typically deals only with structured data and rule-based systems.
| Intelligent Automation can handle unstructured data as well as structured data with ease. And works great with any system.
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| RPA uses bots to replace many manual processes, but it doesn’t have the ability to “learn” by itself.
| Intelligent Automation can learn from its interactions with data through machine learning or self-learning rule engines.
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| With RPA, there will be some processes that cannot be executed without human intervention as it does not have the ability to make decisions on its own.
| Intelligent Automation has decision-making capabilities because it can use A.I. algorithms to identify patterns and make smart decisions based on those patterns. |
| The common activities performed by RPA are copying and pasting data from one application to another, opening emails, carrying out calculations in an excel sheet, etc. | Intelligent Automation can be applied to complex problems that fall into different industries like banking, insurance, manufacturing, etc. |
When to use automation and intelligent automation?
According to research from McKinsey, more than 40% of all labor activities in the global economy could be automated with current technology.
In the context of RPA, automation is when software robots are used to execute a structured set of instructions. For example, a software robot can be designed to open an ERP system, access a specific transaction, and automatically extract required data fields onto an Excel spreadsheet. The robot will run as many times as needed but only within the confines of its programming. There are many such RPA use cases in I.T. industry.
Automation is functional when dealing with repetitive tasks that are not variable. Suppose multiple processes follow the exact same steps every time they need to be executed. In that case, automation can help improve productivity by allowing employees to focus on other tasks that require human judgment or creativity.
Intelligent automation (also known as I.A.) takes RPA one step further by adding cognitive elements such as machine learning and natural language processing (NLP) to automate activities across various systems. This enables software bots to be more flexible and adaptable in interacting with data and applications. As a result, they can perform higher-level tasks without human intervention, like interpreting information, making decisions, and communicating with people.
Intelligent automation solutions are best used on complex or unpredictable processes that could benefit from cognitive technologies like speech recognition or image recognition. You should also consider using them when manual workarounds are being used to integrate different systems that don’t communicate with each other in the standard way.
Intelligent automation use cases using machine learning & deep learning models
Machine learning & deep learning in RPA allows an organization to automate tasks with intelligence at scale, resulting in higher productivity for repetitive tasks, reduced risk of errors, lower costs, and rapid time to market for new products or services.
Here are the 5 best cases to get started for any industry.
Intelligent Automation Use Case #1: Employee experience
This is one of the most exciting and innovative use cases because it has the potential to directly improve your employees’ day-to-day lives. Using artificial intelligence (A.I.) and machine learning (ML), intelligent automation can help your employees automate any tedious work performed by several employees for numerous customers. It can also be used behind the scenes to identify trends in turnover or productivity so that you can adjust your policies for maximum efficiency to improve your employees’ day-to-day lives directly.
Examples include:
- Using chatbots to answer questions about benefits and company policy
- Using intelligent automation to automate time-off requests, approvals, and other processes related to HRIS systems
- Automating background verification processes of new hires
Intelligent Automation Use Case #2: Document scanning & processing
Data can be messy, and it’s essential to put it into a structured format.
Though, OCR platforms can solve the problem of extracting unstructured data. But, any OCR solution, no matter how accurate, is only good if it could tie up the extracted pixel data to existing metadata to achieve a single system of record.
This is a great use case for companies with a lot of paper documentation. Intelligent automation can scan your documents, read them, digitize them, categorize them, and even store them in order in an online data store. Not only will this save you time over manual document processing, but it will also make accessing those documents easier than ever before!
Intelligent Automation Use Case #3: Single source of truth systems using data engineering
One of the biggest challenges for large businesses is developing systems that can manage massive amounts of data, efficiently and effectively analyze that data to guide decision-making. Intelligent automation (I.A.) solutions are helping to solve this problem by creating a “single source of truth” system using advanced data engineering techniques. These techniques allow companies to automatically pull in all relevant information from disparate data sources and synthesize it into actionable insights.
Cloud-based business intelligence tools and I.A. solutions make it possible to create a single source of truth out of what previously seemed like an uncontrollably massive amount of information by quickly providing easy access to meaningful reports and key performance indicators (KPIs).
Intelligent Automation Use Case #4: Compliance
The use case for compliance in intelligent automation is to ensure that people follow policies and procedures within an organization. When these policies are followed correctly, there will be less risk of legal action from outside parties who might sue because their rights were violated or property damaged due to negligence on behalf of employees working inside a company’s facility. Intelligent automation uses artificial intelligence (A.I.) technology such as machine learning algorithms so that rules are enforced automatically without human intervention required at every stage, saving time, money, and resources while ensuring everything is done according to regulations set forth by regulatory agencies.
Intelligent Automation Use Case #5: Periodic report preparation and dissemination
Report preparation and dissemination is a critical activity executed by many organizations, companies, and teams. Historically, it’s been handled manually, with someone sitting down to write a document, attaching it to an email, and sending it out. However, this process is error-prone and time-consuming and doesn’t allow the business to react to current market conditions or other factors.
With intelligent automation, however, reports can be compiled automatically regularly. For instance, let’s say you are part of a marketing team that sends out weekly reports on the progress of your company’s efforts. You can save time by creating a bot that automatically gathers data from various sources (like social media channels) and combines it into a report for you (or your manager). Not only does this reduce human error in report generation, but it also lets your team react quickly to changes in the market—and frees up time for more high-value activities like planning campaigns or participating in outreach events!
How to calculate the ROI of automation investments?
The high-level question – to automate or not – is fairly simple to answer.
The more complex question, however, is what to automate. What are the right tasks to automate? Which processes should be prioritized first? How do you ensure that your automation strategy is scalable for different business areas and functions?
In order to answer these questions, organizations need to conduct a detailed assessment of their processes. To calculate the cost benefits of each automation use case, use the following formula:
This approach will help them determine each process’s complexity, frequency, and cost and make it easier to prioritize which tasks should be automated first.
Case Study: How intelligent automation transformed risk assessment of loan portfolios
Our client is one of Asia’s most prominent banks with total assets of over US$10 billion. The bank caters to a broad customer demographic group with different credit products – from overdraft loans to MSME and corporate loan products.
It manually monitored credit limits for Overdraft (O.D.) accounts to understand the borrower usage to increase or decrease the credit limits based on customers’ credit history.
Zuci Systems implemented its intelligent automation platform, which helped the bank predict credit limits based on unbiased risk assessment, enabled real-time credit usage limit and spending tracking, and streamlined and standardized all manual processes. It provided role-based access to all stakeholders, allowing them anytime-anywhere information access.
As a result, the bank was able to achieve 100% transparency in the loan approval process and saw a 7% decrease in delinquencies and 10x faster loan approvals.
Final Thoughts
RPA is not a new thing; it’s been around for some time now. However, artificial intelligence (A.I.) and machine learning (ML) have given rise to intelligent automation to the next level.
Now that you have an idea of what RPA is, how it can benefit your organization, and how Zuci Systems can help with intelligent automation, what’s stopping you from implementing it? If you find yourself in need of a team to help you set up a framework and automate your business process, consider Zuci. For more information on intelligent automation, feel free to reach out.
I write about fintech, data, and everything around it | Assistant Marketing Manager @ Zuci Systems.
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