RPA & Computer Vision: An In-Depth Look at 5 Intelligent Automation Use Cases

ByPaul Christiano Last Update on

Hi there! Robotic process automation (RPA) has become wildly popular by replicating the actions of human workers on software UIs. But RPA is now evolving – by combining it with artificial intelligence like computer vision, we can create intelligent bots that see, understand, and interact with the world like humans do.

In this guide, we‘ll explore 5 compelling examples of how the power of computer vision is making RPA bots smarter across different industries. I‘ll explain the business problems being solved, illustrate how it works with detailed examples, and provide stats that quantify the benefits. Let‘s dive in!

Automating Tasks in Virtual Desktop Environments

The massive shift to remote work has led to many employees accessing company desktops and apps through virtual desktop infrastructure (VDI). While VDI improves security, traditional RPA bots can‘t automate processes through virtual desktops since they don‘t actually interface with the OS and software.

Intelligent RPA bots enabled with computer vision can now see and interact with virtual desktops just as human users do. For example:

  • They can visually observe and learn workflows by recording screen interactions.
  • Using optical character recognition, they can read text on the screen to extract data.
  • They can move the mouse and keyboard to click buttons, enter data, copy-paste across apps, etc.

This unlocks automation possibilities that were previously impossible in VDI environments. According to a McKinsey survey, 60% of Americans now work from home some days per week. And 43% of companies plan to allow remote work permanently due to COVID. Intelligent RPA allows these businesses to benefit from automation even with virtual desktop workflows.

Use Case Examples

Let‘s understand this better with a few examples:

  • Processing expense reports: The bot can log into the virtual desktop, open the expense management software, extract details from submitted expense reports using OCR, and populate fields in the ERP system. This is done by visually interacting with the VDI just as human accountants do.
  • Managing IT support tickets: The bot can access the IT helpdesk system through VDI, pull details using screen scraping, update ticket status in the system, and notify the user of resolution via email.
  • Onboarding new employees: The bot can automate employee onboarding tasks like accessing the HR system to create user accounts, provision software access, manage paperwork – all through the VDI.

Benefits

Companies implementing intelligent RPA with VDI see significant benefits:

  • 24/7 productivity: Bots work tirelessly without breaks, enabling processes to run 24/7.
  • Improved compliance: Bots reliably follow rules and processes without errors.
  • Cost reduction: Automating manual efforts cuts labor costs by ~65-70%, as per McKinsey.

Clearly, computer vision unlocks the ability to apply RPA in virtual desktop environments for major productivity gains.

Modernizing Legacy Systems

Legacy systems like mainframes and COBOL apps are still widely used in banking, insurance, healthcare and other industries. For instance, 80% of US healthcare firms rely on dated Windows Server 2003.

While replacing legacy systems can be prohibitively risky and expensive, leaving them siloed creates inefficiencies. Computer vision enables new integration possibilities by acting as the eyes and hands for RPA bots.

For example, an intelligent automation bot can:

  • Visually interact with old green-screen GUIs, typing inputs and reading screen outputs.
  • Transfer data seamlessly between legacy apps and modern cloud platforms.
  • Eliminate need for complex APIs: bots can directly interface at the UI level instead.

Let‘s see how this works for specific use cases:

  • Patient record migration: Healthcare organizations often have decades of patient data locked in mainframe systems. Bots can be trained to extract this data and port it to modern electronic health record systems by visually interacting with the old UIs.
  • New customer integration: In banking, new customer details input into legacy CRM tools can be scraped by bots and automatically synchronized with cloud-based engagement platforms.
  • Claims processing: For insurers, details of claims submitted in legacy systems can be transferred to analytics dashboards automatically for better reporting.

Migrating away from legacy systems can take years and cost millions. Intelligent RPA provides a faster, cheaper way to extend their lifespan by integrating them with newer technologies. An Accenture study found that conscious coupling of legacy and modern IT can cut costs by up to 25% while improving agility.

Digitizing Handwritten Documents

Optical character recognition (OCR) has been around since the 1970s. But reliably converting messy, ambiguous handwritten documents into digital text has remained challenging. Each person‘s handwriting is wildly different!

Modern deep learning techniques like convolutional neural networks have now made handwriting recognition much more accurate. For example, Google‘s machine learning model achieved 99.2% accuracy on the IAM handwriting database – better than most humans!

By incorporating these advanced computer vision capabilities, RPA bots can now quickly digitize piles of handwritten paperwork. This unlocks huge efficiency gains in domains like:

Healthcare

  • Prescriptions: Bots can automatically input handwritten prescription instructions into the digital health record. This prevents dangerous errors from illegible handwriting.
  • Patient intake forms: Registration paperwork, medical history forms filled out by patients can be digitized by bots for instant access.
  • Doctor‘s notes: Making clinical notes searchable and analyzable improves care.

An Australian hospital found that digitizing paper records reduced document processing time from 11 mins to just 4 seconds – a 99% improvement!

Financial Services

  • Cheques: Banks can speed up cheque-clearing by automatically extracting handwritten amounts.
  • Insurance claims: Handwritten witness statements, police reports, etc. can be converted to text for easier claims processing.
  • Mortgage processing: Application forms can be extracted without human data entry.

According to McKinsey, applying RPA to mortgage processing can reduce costs by 60-80% while cutting loan origination timelines by over 20 days.

Government Agencies

  • Census surveys: Can transcribe handwritten responses for better analytics.
  • Tax forms: Extract handwritten information from forms to auto-populate tax returns.
  • Claim processing: Healthcare/benefit applications can be digitized for faster processing.

By incorporating handwriting recognition powered by deep learning, RPA bots can unlock huge efficiency gains across sectors. This technology helps accelerate document processing, improves data quality, and provides better customer service.

Claims Processing and Fraud Detection for Insurance

For insurers, prompt and accurate claims processing is crucial – this function can consume up to 70% of total costs. Fraudulent claims also cost over $40 billion annually in the US alone.

Intelligent RPA bots can transform claims management by automating manual steps while improving fraud detection through computer vision techniques like:

  • Photo analysis: Assess damage from photos submitted by claimants to validate claims. This accelerates processing and settlement for honest claims.
  • ID verification: Match claimant photo IDs against policy documents to prevent fraudulent payouts.
  • Estimate triage: For auto insurance, analyze images of vehicle damage to assign severity and create repair estimates.
  • Police report review: Extract details from handwritten police reports using OCR to cross-verify claims.

Top insurance providers are already seeing massive improvements from applying intelligent automation. For example:

  • Allstate developed AI to review photos of vehicle damage, cutting estimate times from days to minutes while reducing costs.
  • Aviva and Zurich Insurance use facial recognition to verify customers and prevent fraudulent claims, leading to 20% faster processing.
  • The Cooperative Insurance improved motor claim processing efficiency by 75% using RPA and machine learning.

Mimicking the visual abilities of human agents allows intelligent bots to transform claims management – insurers reduce expenses, improve customer experience, and stamp out fraudulent claims.

Streamlining Customer Onboarding for Banks

Banks aim to onboard new customers smoothly and quickly while still maintaining compliance. Manual identity checks and paperwork slow this process down, often causing 25-40% of applicants to abandon signups.

Intelligent RPA bots can dramatically accelerate onboarding by automating cumbersome Know Your Customer (KYC) processes:

  • ID verification: Match applicant selfies against ID photos for biometric identity confirmation. This prevents fraud and errors.
  • Document digitization: Extract text from PDFs, scans, and images of paperwork using optical character recognition. This auto-populates application forms.
  • Address proofing: Validate addresses by checking embedded location data in document photos.

Leading banks have already deployed intelligent RPA and seen tangible improvements:

Automating cumbersome KYC procedures results in improved customer experience, reduced costs, and lower abandonment rates during onboarding. It‘s a win-win for banks and their customers.

As we‘ve seen through these real-world examples, combining computer vision with RPA results in intelligent automation capabilities that can deliver tremendous business value across many industries.

Tasks that previously required human vision and judgement – like assessing documents, verifying identity, or interacting with complex UIs – can now be reliably automated by smart bots. This drives increased efficiency, speed, accuracy and cost savings.

Computer vision gives robots human-like perception to automate processes end-to-end, while RPA provides the ability to directly interact with systems and tools. Together, they are a potent combination that will shape the future of work.

The rapid advancements in AI and machine learning will only expand the possibilities for intelligent process automation. Exciting times are ahead!

I hope you found this guide useful. Do let me know if you have any other questions!

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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