IDP: A Booster Shot for your RPA, Chatbot and Low Code Implementations
Although businesses across various industries have benefited from automation technologies such as robotic process automation (RPA), low-code platforms, and chatbots, most are yet to explore the opportunity to automate their end-to-end business processes with powerful AI- and machine learning (ML)-powered tools.
RPA, chatbots, and low-code platforms are a great start to help cut manual costs, reduce process times and improve customer experience but as business processes get increasingly complex, these technologies fall short of reaping higher efficiencies. They can help you get small wins quickly but when you try increasing the level of automation you can achieve, you hit a wall because of their limitations.
The reason these technologies can fall short when handling complex processes is that most businesses need these types of tools to handle unstructured data of some sort. A few examples include:
* A customer uploads an expense receipt to its chatbot but it does not know how to understand it;
* A customer sends an attachment of shipping instructions to an email account that is being monitored by an RPA engine but it cannot figure out what is being shipped where;
* You try to move your mortgage application process to a low-code platform and get stuck on how to read data from the submitted W2 forms.
To solve these issues, manual processing will have to intervene in order to make sense of the documents received by these automation tools, and then feed them the data to complete the task. This means your automation journey is only halfway there.
To reap the full benefits of end-to-end automation, IDP platforms can complement your existing investments in other automation technologies and further broaden the range of functions that can be automated.
Let’s look at how IDP integrates:
In the past few years, chatbots have been deployed by businesses worldwide to answer questions, assist customers and reduce email or phone interactions. Chatbots are designed to handle conversations in short bursts and make sense of programmed sentences. Sometimes, to resolve a customer’s query, the chatbot may ask the user to upload a document.
If you have a chatbot handling your expense management process, then you may need the user to take a picture of the expense receipt and upload it to the chatbot. Since these documents do not follow a fixed format, chatbots cannot easily extract information from these receipts such as the nature of the expense, amount, date of transaction and currency, etc.
An IDP receipt classification and extraction model can complement the chatbot by extracting the receipt information and making it available in a structured format. Once the chatbot has this information, it can further manage the process, i.e., auto approve the expense if it falls within the pre-approved limits, ask users to provide a justification if the expense is likely to be rejected, or even request additional supporting documentation.
Low-code platforms are another set of automation tools that have made it easy for businesses to create new solutions as needed. In the past, enterprises spent months and millions of dollars working with system integrators to develop tools for handling various business processes. Some of these business processes can now be handled by configuring process flows in these low-code platforms. Unlike before, low-code platforms can do this in a few days or weeks, rather than months. But as these flows get more complex, they too will need to handle documents.
Let’s say you configure a low-code platform for the loan origination process. You get users to enter the application information on forms put together by the platform. As a part of the process, the loan applicant is expected to upload proof of income and current assets. All low-code platforms attach these documents and send them to the underwriters where they then will need to open these documents and manually extract this data which slows down the overall mortgage approval time.
With a complementing IDP platform, you will be able to automate data extraction from these uploaded documents and present it to the underwriters in a ready-to-use format. In fact, the low-code platforms will also be able to make some routing or approval decisions based on the information present in these documents.
Out of all the automation technologies, RPA is most widely used and deployed. With their orchestration and user simulation capabilities, they can deliver quick efficiency by automating simple manual tasks. At the core of RPA tools lies their process automation capabilities, which vary and depend on unstructured data being converted to structured format and delivered to them as CSV files.
Let’s use an insurance processing back office of a third party administrator set up on RPA as an example. The RPA bot listens to an email address for incoming messages for First Notice of Loss (FNOL) documents. This document does not have a fixed format as every business has its own layout and set of vocabulary to describe the incident. As new claim notifications land in this account, the RPA bot needs to understand the information present in these attachments. In absence of this information, the only thing the RPA bot can do is grab these documents and record the email of the sender along with the date and time. Then it needs to wait for a human to manually read the document and identify who this information is for and what type of incident it was.
When RPA is integrated with an IDP platform, the RPA bot can send this document for understanding and receive a CSV file with all the relevant information. Based on this information, it can decide where to route this document and make other business decisions.
Steps to integrate IDP with existing automation tools:
1. Identify data to extract: Before extracting data, the first step is to identify the document type and required fields from which to extract. AI-led IDP solutions use document classification models to recognize different document types, based on OCR capabilities and proprietary machine learning algorithms.
2. Configure and train the IDP solution: IDP solutions are flexible and can be configured to intelligently capture data from specific fields in a structured manner. This enables your RPA or bots that were previously struggling to infer the unstructured data, to easily recognize and process the extracted information. Once you configure the fields, you train the ML models to process multiple document variations, and the IDP solution will improve its accuracy as it becomes exposed to more variations.
3. Integrate with automation tools: Connect the IDP solution with primary automation tools such as RPA or chatbot. This can be done using APIs. This enables seamless connection and integration between the RPA and chatbot tools while the IDP solution allows you to automate processes end-to-end.
4. Continuous feedback: AI- and ML-powered IDP solutions continuously learn from corrections. When you extract data, IDP solutions assign a confidence score to the extracted data. This is the solution’s ability to know whether or not the extracted data is accurate. Some of the data may be extracted with a low confidence score and need to be routed for manual corrections. IDP solutions use various AI technologies and learn from each correction so that automated accuracy is improved for future extractions.
How can IDP boost your automation efficiency?
Intelligent Automation (IA) has two parts in its name — intelligence and automation. Automation tools like RPA bots, low-code platforms and chatbots are the arms and legs of Intelligent Automation. They help you take action. IDP is the intelligence part — involving understanding of content by becoming a brain. IDP is the cognitive logic that will make these arms and legs function better by sending them relevant instructions as needed. Combining them can help you reap incredible efficiencies from your automation investments. You can take your automation journey to new heights and exponentially improve your business processes by integrating the right IDP solution.
Originally published at https://www.infrrd.ai.
