Robotic Process Automation provided various benefits to organizations in the last decade and it is still an important opportunity for businesses since payback period of RPA implementation (9 months) is shorter than most other digital technologies. However, RPA technology has its unique limitations such as its inability to automate via unstructured data and companies are working on end-to-end automation approaches like hyperautomation to solve this problem.
We’ve explained what hyperautomation is and how it differs from regular automation. In short, hyperautomation is the end-to-end automation of processes through a combination of technologies including
- Robotic Process Automation
- Intelligent Business Process Management Suites (iBPMS)
- Process Mining
- ArtificiaI Intelligence/ Machine Learning
- Natural Language Processing (NLP)
- Optical Character Recognition (OCR)
- Digital Twin of an Organization (DTO)
We’ve identified 11 repetitive businesses processes where hyperautomation is possible with today’s technology.
Processes triggered by incoming documents or email
In these processes,
- Incoming document or email is collected by a script or RPA bot. This document or email can contain semi-structured or unstructured data.
- Document or email is processed using a machine learning model which extracts machine-readable data from the document
- Machine readable data is validated by either rules or ML models. For example, invoices could be validated for VAT compliance or to ensure that they are not fraudulent.
- Validated data is enriched by database lookups or ML models. For example,
- supplier ID can be looked up from the company’s master data
- Cost center can be added to the invoice based on company’s historical transactions
- If machine learning model confidence is low, the output can be reviewed by a human using a human-in-the-loop software
- Finally, this validated and enriched machine-readable data is passed to the next system of record (e.g. ERP)
Accounts Payable (AP)
Accounts payable process includes receiving, processing, and paying out invoices from suppliers that provided goods or services to the company. Manual processing
- is expensive
- leads to longer processing time,
- increases the risk of errors
For more on AP automation, feel free to check our comprehensive article on the topic.
Travel & Expense processes involves paperwork and repetitive tasks that can be automated through hyper automation. Some T&E processes are:
- collecting travel expense paper receipts of employees
- extracting data from receipts
- checking receipts to see whether they are compliant with the expense policies of the company
- completing payments or requesting approval on items that are not in line with expense policies
Order management involves processes such as
- retrieving email and relevant attachments
- extracting information about what customers want. Some possibilities are
- new order
- order update
- order cancellation
- updating internal systems based on the newly placed order or modifications to existing orders
- taking necessary actions regarding customer query
Workfusion claims that its SAP-integrated automation platform provides
- 50% reduction of manual effort in the process of removing critical order blocks
63% increase in automation rate of individual sales order fields
- Increase in accuracy of reimbursements by 99%
For more on order management, feel free to check our related article.
Other document processing
Manual document processing is a challenge for every industry. Different industries need to process different documents such as invoices, bill of lading, purchase orders, receipts, payslips, medical records & prescription. Businesses can automate the processing of these documents via hyperautomation. Document automation involves the following steps:
- Document processing
- data extraction
- In some cases, document generation will also be necessary. For example, orders may need to be generated from the quotes sent from suppliers. This includes:
- data capture
- transforming data to the desired format
- arranging content
- generating output document
With the combination of OCR and machine learning, a business can automate document processing end-to-end.
For more on document automation, please check our related article.
Customer Service Operations
Three technologies are involved in the end-to-end automation of customer service:
- NLP understands email, document, queries
- Machine learning algorithms classify information by category
- RPA bots or scripts for sending the mail to a particular person or responding with a template message
Fully digital processes
Not all processes require unstructured or semi structured data. Some processes get triggered with structured data from the client or the company’s internal processes. These are already automated or being automated.
Some examples are:
Marketing: Lead generation from anonymous site visitors
Most visitors do not provide companies with their contact information. Using IP and other data, website/mobile app owners can identify the businesses that are browsing their digital applications. This data can be automatically fed into an outreach platform which can identify relevant profiles in those companies based on the profiles of companies’ customers. For example, if a company sells to the specialists in the procurement department, those profiles would be prioritized. The outreach platform could
- display advertising to these profiles
- send a series of emails to these profiles and once they respond, they can be routed to the sales team
All touchpoints until sales rep responses are automated in this process.
Anti Money Laundering (AML)
To prevent fraud in transactions, companies can either work with end-to-end AML solutions or combine RPA bots to provide automation:
- RPA bots collect related data and processes to validate customer records
- Fraud detection models identify unusual patterns through ML algorithms
- RPA bots perform follow-up actions
Insurers can automate almost the entire claims operation without interruption from humans. Claims handling contains following processes
- claims intake: extracting data from documents
- claims assessment: understanding and analyzing claims to identify whether they are in line with the customers’ policy
- claims settlement: automating transactions for valid claims
Redaction is necessary to protect personal data. For example, in the US courts subpoena insurers for their customers’ medical records and insurers need to ensure that those records include only the requested data and nothing more. For example Nonpublic Personal Information (NPI) such as social security numbers or telephone information need to be removed from these documents.
ML based solutions can automatically identify NPI and remove it from documents.
The underwriting process of loan transactions can also be automated via RPA & AI. It includes these steps:
- collect data from external and internal sources
- fill required data fields in internal systems
- assess risk via ML models
- analyze the historical transactions of customers and provide pricing & interest options
In banking, customer onboarding is a document-heavy area due to know-your-customer (KYC) regulations. The processes involves
- identity verification
- customer due diligence
- account activation
Automation of customer onboarding is provided by
- pre-trained bots to extract information from documents, input data into their systems and build risk profile via machine learning
- human-in-the-loop and machine learning models to enable verification and validation of information
- intelligent bots are trained by historical data to improve their accuracy
If you are looking for vendors that can provide necessary technologies to achieve hyperautomation, don’t hesitate to contact us:
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