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As a highly customer-oriented industry, banking, financial services, and insurance (BFSI) has always been a prime candidate for digital transformation. COVID-19 catalyzed the adoption of digital technologies in this otherwise conservative sector, says Deloitte in their 2021 banking and capital markets outlook. Moving away from in-person interactions created challenges, forward-thinking financial services firms viewed those challenges as opportunities to create better experiences through automation.

To fully realize the digital promise, BFSI firms can use a variety of levers to elevate process efficiency and customer engagement. These can include creating an optimal mix of digital and human interactions, using data intelligently to better shape experiences, and incorporating artificial intelligence (AI)-based solutions to automate processes and free up capacity to focus on strategic activities.

Intelligent automation aided by AI and cognitive technologies can help to accelerate processing time and reduce the number of errors in complex processes end-to-end, removing the sector’s reliance on legacy methods like spreadsheets to get jobs done. This is a massive opportunity! According to McKinsey, AI could deliver up to $1 trillion in additional value for the banking sector.

Where to start using intelligent automation?

Even with all the digital transformation it underwent, the BFSI industry is poised to take further advantage of the technologies that can expand its field of vision and open even more opportunities, including intelligent automation.

Though the first wave of automation improved some financial service providers’ basic functions by employing robotic process automation (RPA) for repetitive tasks, there are many organizations that are still in need of far more sophisticated and intelligent applications of automation for their evolving business processes. Firms that are already scaling their intelligent automation efforts are leading with improved experiences across the value chain while reducing their operating expenses and driving better margins through significant process evolution.

These automations have proved to perform iterative tasks at scale. They ingest data from third-party sources, populate digital platforms, trigger notifications and initiate actions without human intervention, so the firms can virtually operate 24/7 without overburdening employees.

Forward-thinking firms continue to streamline their automation-readiness. These organizations are seeing the benefits of intelligent automation unlocked across multiple operating areas through use cases that have a significant, positive ROI. For instance, we recently helped automate redemption request processing for a US-based financial services leader, transferring metadata between the front-end and back-end systems, eliminating staff involvement altogether. Our customer continues to see recurring expense reduction, saving thousands of person-hours of resource expenditure with this engagement.

Based on our experience and expertise working in this industry, we have shortlisted a few similar business use cases where intelligent automation has been creating fast and incisive impact.

1. Letting customers open accounts remotely

AI is an integral component of intelligent automation and sets it apart from stand-alone, traditional RPA. Using AI, you can leverage technologies like Optical Character Reading (OCR) and cutting-edge facial recognition, blended with an integrated intelligent automation platform, to help fully automate and accelerate the account opening process. Customers need only to initiate a video call, and the facial recognition solution evaluates features to verify identity. Post the verification, the intelligent automation solution can then take over to extract the necessary details from remotely shared data to populate the fields in your enterprise resource planning (ERP) or core system.

2. Saving effort, costs, and time in data migration

Any digital transformation activity, where you are modernizing applications that have existed for decades, involves complex data migration. Lenders, credit assessment firms, insurance companies, and similar service providers rely on data as a key asset. Traditional migration of data would involve at least six stakeholders (a business user, a data custodian, a systems specialist, a database specialist, a product specialist, and an extract, transform, and load specialist). An intelligent automation solution that reads the legacy source, applies transformation/reformatting procedures, and loads the data into the new schema can significantly cut down the human effort, operational costs and turnaround time involved in this process.

3. Making credit risk assessment more accurate and scalable

The analytics technology needed to accurately screen prospective borrowers and assign risk scores already exists. However, human employees still need to go through this data, which can be cumbersome and prone to errors, especially when it comes to processing small-to-micro retail loans. An intelligent automation solution can connect with the analytics engine on one end, and the underwriting system on the other, to automatically process risk assessments and loan applications below a certain threshold.

4. Detecting fraud and setting up timely alerts

By mapping and continuously monitoring real-time transactions against data from ERP, business intelligence, and third-party providers, your anti-money laundering (AML) and fraud detection teams can detect suspicious behavior and signs of misappropriation. An intelligent automation solution can not only help them by keeping a constant watch for these tell-tale signals (purchase order mismatch, split transactions, payments made at unusual hours) but also alert the necessary parties in real time. Leveraging this, you can set up an automated workflow for low-value transactions, where suspicious behavior can be approved or blocked automatically.

5. Processing and validating applications while maintaining data integrity

Manual application validation processes – whether for banks, insurance, or asset management firms – are painfully error-prone and tedious. When done using spreadsheets (which is still a staple for the BFSI industry), there are the added risks of data inconsistency, inability to track lineage across multiple systems, and duplication. An intelligent automation solution, on the other hand, can extract and store data involved in all these processes, so it can be easily accessed, tracked, and used. Leveraging technologies such as OCR, Intelligent Document Processing (IDP), Machine Learning (ML), and Natural Language Processing (NLP) in the solution, your business users can process complex applications from large commercial entities within no time, and customize the solution to suit emerging process changes as and when needed, without depending on the IT team.

But does that mean you have to disrupt your existing IT landscape to build an intelligent automation system afresh? The best part is, it can be integrated /added into / onto your IT infrastructure seamlessly adding more value to it, and enabling bidirectional data flow with ERP, content management systems, regulatory databases, and custodian data portals.

These five use cases are just the tip of the iceberg. The potential use cases for intelligent automation in financial services are vast, including business-critical processes such as KYC/Re-KYC, card activation, audit processes, customer engagement, and reconciliation in wealth management.

Discover more ways that intelligent automation can enable you to unlock these hidden opportunities in our eBook How Intelligent Automation is Propelling Banking & Financial Services: Top Ten Use Cases Reimagined. The eBook also explains how JIFFY.ai’s integrated platform-based approach can help realize exponential returns from your automation investment.

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To learn more, visit https://jiffy.ai/solutions/banking-and-financial-services/

Connect with our intelligent automation experts and accelerate your automation journey.

Optical Character Recognition (OCR) technology became popular in the early 1990s during the digitization of historical newspapers. Before that, the only option to digitally format documents and extract data from them was to manually retype the text. This was a tedious, time-consuming, and error-prone process. OCR came in to replace manual document processing and is now most used to convert hard copy documents into an editable format.

What is Optical Character Recognition (OCR)?

OCR technology is used to automate data extraction from printed or written text from a scanned document or image file, and then convert the text into a machine-readable form to be used for downstream data processing like editing or enabling search capabilities.

For example, when you scan a form or receipt, your handheld device or computer saves the scan as an image file. Suppose you need to edit, search, or count the words in the image file—you cannot use a text editor to do that. However, you can use OCR technology to convert the image into a text document with its contents stored as text data.

In fact, OCR systems are made up of a combination of hardware and software that is used to convert physical documents into machine-readable text. The hardware includes an optical scanner or specialized circuit board that is used to copy or read text, while the software manages advanced processing.

Why does OCR fail?

Today, OCR technology has undergone several improvements and can deliver fairly accurate output. Many businesses depend on solutions built on OCR technology for document processing.

As a traditional tool that converts the data on a printed document or an image into a digitized format, OCR is a better alternative to manual processes. It works well on extracting text from documents like paper files, passports, invoices, business cards, printouts, letters, and images.

Despite how powerful it is, it is not perfect. With the high probability of data errors creeping in, the output from OCR-based data extraction solutions may not be useful for downstream enterprise business processes every time.

Even with the best-quality scanners, OCR-based solutions deliver a maximum accuracy of only 60%. Business users end up putting in more time to make manual corrections to the extracted data than the time OCR saved in extracting it.

When the going gets tough, OCR does not get going

OCR-based automated document processing solutions cannot deliver straight-through processing (STP) with accuracy because they work based on templates. That means documents must be processed in specific formats conforming to certain rules or OCR cannot extract data from them. Now, imagine a complex organization that deals with a large volume and variety of documents every day. OCR-based solutions will fail to deliver in that situation.

Extracting data from semi-structured, unstructured, and handwritten documents is tough territory for pure OCR-based solutions, and this makes them unsuitable for enterprise-grade implementation and rapid scaling.

The most significant challenge for OCR-based document processing solutions is their inability to extract context from the content. For example, if a number extracted from a table does not contain a quantifying unit (such as currency), it fails to convey the true value of that data. Once again, business users might have to spend time looking for the missing pieces of information in the original document to add value to the extracted data.

Intelligent Document Processing can manage scale and complexity

Intelligent Document Processing (IDP) solutions fill in all the gaps left by OCR technology, and help businesses conquer challenges of scale and complexity in data extraction.

IDP solutions combine the power of advanced cognitive technologies including Artificial Intelligence, OCR, Machine Learning and Deep Learning to process a wide variety of documents. They not only recognize, learn, and capture the content, but also deliver valuable business context. These solutions convert data to a structured form that can easily be processed by integrated downstream business systems.

JIFFY.ai’s IDP solution runs on a hybrid processing engine with self-learning machine models. This makes the system capable of handling dynamic and large volumes of documents, vendors, and formats. It extracts data accurately and quickly from multiple OCRs, fields and values, checkboxes and images, different formats, complex tables, handwritten text, address fields, camera images, various ID cards, driving licenses, receipts, and much more. So, enterprise teams can use it to derive actionable business insights from the data faster and more efficiently.

CFOs have a strong focus on managing working capital, knowing well that their cash is tied up in Accounts Receivables and the Order-to-Cash process. Releasing cash from inefficient Order-to-Cash (O2C) procedures is a priority for them, so they can reduce debt, invest in product development, or support strategic initiatives.

Record-to-Report (R2R) is an equally crucial process in an organization’s financial value chain. In fact, R2R plays several critical roles in the success of the business—from demonstrating compliance to providing the executive leadership with the data and insights needed for making smarter business decisions.

For truly efficient, error-free and straight-through O2C and R2R processing, F&A teams need hyper-automated solutions that provide delightful front-end experiences to users and vendors, and completely  “autonomous”     middle and back ends that are integrated closely with other third-party systems in the ecosystem.

Evaluating the As-Is State of Legacy O2C and R2R Processes

While the pandemic emphasized the importance of cash management, CFOs had already faced similar challenges during both large-scale crises and smaller-scale issues. A recent survey revealed      that 75% of CFOs plan to increase capital spending on their F&A teams with a focus on increased technology spend and use over the next few months.

Companies have been pursuing automation and integration of the O2C and R2R processes for a while now, aiming for increased efficiency and reduced errors, resulting in improved Days Sales Outstanding (DSO), Accounts Receivable (AR) turnover, and end-to-end financial data visibility. Progress has been made by automating specific tasks. However, the limitations of structural and ERP systems have hindered full O2C and R2R integration, and many automated processes still require a significant level of human intervention.

The O2C process involves various steps— such as order placement, credit management, order fulfillment, shipping, customer invoicing, payment collection, and reconciliation—in the sales cycle. Typically, it starts when a supplier receives an order for goods or services from a customer. This spans multiple departments and information sources, making it difficult for the F&A team to get a complete picture of the cogs and bottlenecks in the entire chain. Disparate systems like ERP, CRM, and BPM house O2C information, creating silos, and leading to delays and bad experiences     .

Similarly, information silos in the legacy R2R processes cause latent issues that hinder the smooth working of the F&A value chain. Ideally, the R2R process needs to connect with all data-generating activities of the enterprise, such as transactions, end-of-period records, procurement, case resolution, forecasting, and so on.

But data gaps in the legacy processes allow errors and delays to creep in. Consequently, the Financial close process for every month, quarter, or fiscal year tends to be a painstaking experience for most Finance teams due to inaccessible data and over-reliance on manual effort. When key stakeholders like analysts, investors, lenders, auditors, and regulators use records coming from an inefficient R2R process, their decision-making is likely to get clouded.

More often than not,      CFOs find themselves troubleshooting O2C and R2R processes and helping      stakeholders collaborate across the chain to fetch relevant data – rather than investing their time and effort in value-generating strategic business decisions for the enterprise.

How do organizations overcome this challenge? Most organizations may turn to RPA automation bots for Finance & Accounting, but this provides only a temporary reprieve. Some may pump in more resources or invest in expensive point solutions—both unsustainable quick-fixes.

Research shows that 55% of CFOs are aiming for a touchless Financial closure process by 2025. For faster, sustainable transformation of the Finance & Accounting process, Autonomous Finance solutions based on no-code technology is a better enabler than RPA bots. Let us explore why.

Where RPA Automation Bots in O2C and R2R Financial Processes Fail

Robotic process automation (RPA) is the conventional approach to replacing manual tasks with a software tool that performs the task just like a human would. Problems arise because RPA bots are not blessed with cognitive capabilities as human accountants to understand the nuances in the processes—when exceptions happen in the process, they stall. And O2C and R2R processes can be complex and highly variable, leading to several exceptions.

For instance, an error in goods delivery can lead to order cancelation, which throws a spanner in the works of a typical O2C cycle. Further, a high number of outstanding receivables due to delayed payments can cause liquidity worries. When such situational challenges arise – and these occur quite frequently – the RPA automation bots will struggle to find the right rules to fit the context.

The CFO organization expects the technology to complete the task automatically, with minimal human involvement. But in less-than-ideal situations, the bots get stuck on hundreds of exceptions that a human F&A executive may have addressed in a day. Such situations have prompted several CFOs to roll back their automation efforts (and continue to rely on manual effort) while others implement a new set of bots or even reconfigure the existing ones to “react” and fix the issue.

The result could be a vast automation sprawl with its own maintenance challenges as well as the risk of exceptions arising from each deployment. For rapidly growing organizations with a sizeable R2R operational volume, this turns out to be untenable. Eventually, it will impact the organization’s business growth, customer experiences, and market reputation.

Finance Transformation: How AI-powered Autonomous Technology Makes a Difference

The answer to O2C and R2R challenges is not to respond to complicated process exceptions with an equally complicated RPA ecosystem.

Autonomous Finance solutions, powered by cognitive technologies such as Artificial Intelligence (AI) and Machine Learning (ML), and no-code technology enable the CFO’s Office to solve the issues of fragmentation of large volumes of data and processes, connecting end-to-end processes with information. They also provide real-time reporting and data visualizations to facilitate faster decision-making.

They eliminate manual processes in the O2C process by up to 90%, free up staff for higher value-adding tasks, reduce DSO, and improve cash flow, enabling businesses to improve revenue and profits.

The Generative AI component empowers F&A team members to adapt these solutions to changing business landscapes and tailor them to manage exceptions, learn from new situations, and even recommend process improvements as they automate the O2C and R2R processes end to end.

Unlike RPA bots, the team does not have to depend on IT to build these automations and maintain them. They provide efficiency and real-time unified visibility to the F&A team, helping them to improve cash flow for better working capital management, and enhance customer experience. As they come pre-integrated with key F&A systems, in ready-to-use SaaS packages, they can be quickly adopted without ripping apart the existing technology infrastructure. For example, here’s how JIFFY.ai’s hyper-automated applications for Finance, or ‘Finance HyperApps,’ streamline the O2C and R2R processes and enable end-to-end finance transformation:

• Simpler master data management: Automates customer onboarding, maintenance, and reporting
• Better credit handling: Reduces human effort in credit approval, credit limit requests, and refunds
• Faster month-end cycles: Automates journal voucher tasks by synchronizing with ERP
• More accurate reporting: Populates R2R systems with error-free data for better analysis
• Hassle-free compliance: Automatically creates monthly and quarterly reports with cleansed data

Today, 98% of CFOs say that they intend to protect digital investments even amid cost-cutting measures, and 66% plan to increase their spending. JIFFY.ai’s AI-powered No-code Finance HyperApps can transform critical processes like O2C and R2R from ‘automated’ to ‘autonomous’ and provide maximum ROI for your technology investments.

How can you guide your organization through digital transformation when approximately 80% of business data still exists in unstructured forms such as emails, images, and PDFs?

Yes, you need a tool to quickly digitize all these documents with minimal manual effort. Intelligent document processing enables this and helps you automate document-related business processes at scale. Here’s how.

Intelligent document processing (IDP) is defined as a set of tools powered by Artificial Intelligence (AI), Machine Learning (ML), Optical Character Reading (OCR) and other technologies that can convert unstructured, semi-structured, and structured documents into machine-readable data, which is the foundation of business process automation.

Industries and enterprise functions that rely heavily on documents, such as banks, schools, healthcare institutions, HR and Finance & Accounting can save tremendous amounts of time, effort, and investment using IDP.

For instance, one of our clients, a leading automobile manufacturer, was able to achieve 85% straight through processing over a 12-week period across a volume of 150,000 invoices per month for 5,000 suppliers using our invoice processing HyperApp. The HyperApp that has built-in intelligent document processing capabilities helped their AP team to cut the time needed to process one invoice from 24 hours to just 3 minutes. The solution helped automate 90% of their invoice processing.

IDP can drive these outsized benefits due to its key advantages over traditional document processing automation solutions.

Intelligent Document Processing vs. Automated Document Processing

IDP improves upon pure ML-based document processing solutions in four ways.

What is Document Processing Software? IDP Software Explained, with an Example

An IDP software is an application that packages all the capabilities mentioned above (low-touch, GUI-based, AI-powered, and adaptive), into a single, business-user-friendly platform. For example, JIFFY.ai offers a hybrid IDP software that can handle heterogeneous documents and data formats using both ML and rules-based processing, along with sophisticated OCR. Using our intelligent document processing software, you can:

• Process a variety of documents, involving complex tables, tables with/without lines, multi-page documents, etc.
• Extract data from various ID card formats, receipts, driver’s licenses, and other similar documents
• Automatically extract and feed data to the destination applications, such as CRM, ERP, etc.
• Easily define and train new ML models for unfamiliar document formats
• Handle exceptions and automate document processing-related activities at scale, even when there are thousands of types of documents involved

The JIFFY.ai Approach to Document Processing: Efficient and Future-Ready

As companies continue to embrace and progress digital transformation rapidly, the efficiency gains offered by IDP will make it an enterprise staple and elevate employee experiences by eliminating tedious repetitive work.

JIFFY.ai adopts a hybrid approach to IDP so you can gain from AI’s predictive capabilities while learning from human inputs when exceptions arise. This places our document processing software ahead of most industry peers in terms of accuracy, scope, and user support. For example, JIFFY.ai extracts text inside complex tables with 15-20% more accuracy compared to competitors.

With true touchless processing and usage-based SaaS pricing (you pay only for the volume of documents processed), JIFFY.ai’s Intelligent Document Processing solution helps to defragment data extraction from myriad documents spread across the enterprise, and thus changes the paradigm of enterprise automation, thereby accelerating innovation.

Learn more about the IDP core that powers our Invoice Processing HyperApp.

Today, CFOs and Finance & Accounting (F&A) leaders are uniquely qualified and empowered to drive changes in how their companies experiment with new technologies, and execute transformation. If you’re transforming the F&A function, you probably already know Accounts Payable (AP) can be a valuable area to help drive your organization’s growth.

Typically, the invoice processing workflow of an AP function involves iterative tasks and template-based document management, which makes it a prime candidate for automation. Based on our continuous engagements with clients who are innovators in their own industries, we know that automated invoice processing can improve the AP team’s efficiency by 85% and reduce the time-to-process one invoice from around 24 hours to just three minutes. Yet, just 5% of organizations use a fully automated AP approach, and over one-third are still limited to paper invoices.

It is vital to understand the benefits of automated invoice processing and leverage middle office automation better – so you can eliminate inefficiency, save costs, and utilize your precious human resources for strategic and innovative activities.

What is Automated Invoice Processing?

Automated invoice processing can be defined as a technology-enabled invoice processing workflow where different types of invoices can be submitted by suppliers electronically, data can be extracted, invoices can be approved, and payments can be disbursed with minimal intervention from human AP teams. It uses technologies like machine learning (ML) for invoice recognition, optical character recognition (OCR) for data conversion into a structured format, and a human-in-the-loop approach to seamlessly handle exceptions.

5 Reasons to Automate Invoice Processing

By automating this key AP process, you can:

• Reduce errors – Invoice documents can be detailed and highly complex. A human AP employee could make mistakes due to negligence, lack of training or sheer fatigue. This can be completely avoided using an end-to-end intelligent automation solution like JIFFY.ai’s Invoice Processing HyperApp.

• Drive reusability – You don’t need to create a different workflow every time there is a new supplier, a regulatory change, or a new invoice template. The intelligent automation solution uses ML to learn from a single human-executed change and replicate it across similar future processes.

• Improve relationships – The automation solution includes a supplier portal through which invoices can be submitted directly to your ERP. Also, faster invoice processing means faster payments with nearly zero bottlenecks. This makes life easier for your vendor and supplier network.

• Scale easily – When you automate invoice processing, you also make AP workflows consistent across different business units, departments, and operational regions. This allows you to scale easily whenever you need to without having to recreate processes from scratch.

• Speed up ROI – If you have already digitized invoice processing without aiming for automation or straight-through processing (STP), your AP team might still be spending time on manual effort – the only difference could be that they are struggling with PDFs instead of paper invoices now. Intelligent automation lets you accrue returns much faster from your AP digitization investments.

Over time, you will see a significant improvement in cost savings, compliance, and employee satisfaction when you automate invoice processing.

How Does the Automation Flowchart Work? A Case Study

To understand the flowchart for invoice processing automation better, let’s look at a real-world case study.

A leading automobile manufacturer wanted to automate AP and invoice-related processes, covering 150,000 invoices a month for 5000+ suppliers. Their AP team typically needed one whole working day to process an invoice, which meant they needed a massive FTE team dedicated to invoice processing. JIFFY.ai deployed a low-code, AI-powered invoice processing automation HyperApp following this flowchart:

• Created automation training data from 12 months of historical invoices
• Created ML model to automatically tune parameters and choose the right algorithm
• Applied the ML model through the JIFFY.ai HyperApp
• Set up exception, validation and error handling through human-in-the-loop and self-learning systems

JIFFY.ai’s Invoice Processing HyperApp enabled 90% straight-through-processing (STP), improved efficiency by 85%, and enabled the automaker to achieve ROI in 6 months instead of a year.

Achieve STP for Invoices Using the HyperApp

JIFFY.ai’s Invoice Processing HyperApp augments the benefits of automated invoice processing. The best part is, you don’t need to write code from scratch to get it working in your organization because  it is a pre-built, no-code/low-code tool. And it is hosted on the cloud, saving you the costs of installation, configuration, or server management.

As companies try to modernize their AP functions, our intelligent automation HyperApp can enable 100% straight through processing for your invoices and help you to unlock exponential cost and control benefits.

Companies including the Fortune and Global 500, andBig4 consulting firms have worked with JIFFY.ai to address automation challenges and modernize their workplaces for the future. Email us at marketing@jiffy.ai to learn more.

One of the biggest reasons why businesses put off automation is that it can seem overwhelming in scope. Back in 2020, business process automation was estimated to be a $2.53 billion market, but it was given a big thrust by the COVID-19 pandemic. According to Deloitte analysts, two-thirds of business leaders used automation to respond to the impact of the pandemic this year, and adoption numbers were up by 15% from the last year.

Clearly, if you were planning to embark on that ambitious enterprise automation project, the best time to begin is “right now”. But before that you might need a solid checklist to identify which processes to target first, and where to begin.

Tackling enterprise automation one step at a time

It is a common misconception that enterprises exist either as a fully manual-effort-reliant or as 100% automated. In reality, automating entire process monoliths is a complicated task; rather breaking workflows down into manageable micro-services and automating them is a smarter approach. This way, you can extract component tasks that are of varying degrees of frequency and variability.

High frequency, low variability tasks are the prime candidates for automation. They require immediate automation attention so you can avoid losing out any more on efficiency gains. Low frequency, low variability tasks are also very automation-ready – but they might require more time to yield ROI that you should showcase, or they may not even need automation if you already have them running smooth with the help of your staff.

Low frequency, high variability tasks (for example, redesigning your brand logo) are least suited for automation as they typically involve a high degree of cognitive efforts.

Finally, high frequency, medium variability tasks promise the best returns from automation implementation. An intelligent automation solution can effectively reconcile any exceptions the workflow faces, given its variability levels. With this, you can liberate your skilled staff from these tasks completely.  Due to their high frequency in the business fabric, organizations stand to save significantly by automating such processes.

No matter where a specific task falls on the frequency variability matrix, it is advisable to  break it down to manageable services before starting to automate it. There’s one more important step before kick-starting your enterprise automation project —assessing the organizational landscape for automation-readiness.

Is your organization ready for automation? Here’s the checklist

This checklist serves two purposes – taking stock of your strategic readiness and understanding your technology needs.

1. Do you have prior experience in automating processes?
2. How do decision-makers feel about the ROI of automation?
3. Have you identified the right automation technology?
4. Is your process defined and documented?
5. Is the process frequently occurring?
6. Is the process consistent/stable?
7. Is the process digitized/ready to be digitized?

The answers to these questions can help shape your automation strategy and determine the automation approach to choose, and thereby enable you to take the first significant step towards evolving into an autonomous enterprise.

Wish to explore this automation-readiness checklist in more detail and gather practical insights on the hurdles en route before commencing your enterprise automation project?

Read our eBook How to implement intelligent automation for scale and unlock its true potential.

Today, the Accounts Payable (AP) team of an enterprise has a significant role to play in supporting the business —by managing supplier payments efficiently so that business isn’t interrupted.

Moving beyond their traditional responsibilities, AP teams are also driving growth, optimizing working capital, and mitigating risks. Therefore, ensuring maximum efficiency in their processes has become the prime focus of every organization.

One of the key hallmarks of efficiency in the AP function is the ability to leverage technology to achieve Straight Through Processing (STP) of invoices.

What is Straight Through Processing? Why can’t every company achieve this?

In the context of AP,  straight through, or ‘touchless,’ processing is defined as an invoice being received, approved, and paid without any manual intervention. With automated STP capability, the AP team can process a vastly higher number of invoices quicker and with far lesser effort. STP brings in enormous value as it is significantly cheaper and faster than any other invoice-approval workflow process.

Almost seven in ten AP teams (as many as 72%) spend up to 10 people-hours per week, or 520 hours per year on tasks that could be automated — such as invoice processing, supplier inquiries, supplier payments execution, PO matching, new supplier registration, and payment reconciliation. According to APQC, the cost of processing an invoice manually varies between $2 and $10 – and these are very conservative numbers.

Though most organizations realize the need to achieve true straight through processing of invoices, many find it tough to choose the right technology that can help them address all the perceived challenges involved.

Why OCR and RPA ‘bots’ are not intelligent enough

Gathering all the necessary information spread across invoices in multiple formats, item description matching, data cleansing and exception handling are the most important tasks of invoice processing.

Industry-favorite Optical Character Recognition (OCR) solutions fail through most of these processes. As they scrape data from the screen, errors are bound to happen and invariably your AP team members must spend hours or even days reviewing the data and tracking down the missing pieces. They might also have to cross-check whether the extracted invoice data conforms with the PO requirements and business rules. Errors and mismatches could spring up anywhere in price, quantity, dates, conversion of currencies, etc.

On the other hand, RPA ‘bots’ deployed in these processes have simply not been able to scale. Most organizations that have implemented RPA have not made it beyond a handful of business processes even after several years of work. 

RPA brings in an additional layer of architecture into the technology stack – or technical debt – which requires additional governance efforts. Even a minor change made to the UI, APIs or data transposition could potentially interrupt the bots’ functionality. Such breakdowns in automation can cause downtime and lost business value with the potential need for additional technical resources.

In order to avoid these cost and effort overheads, future-oriented AP teams are adopting intelligent automation to achieve STP for invoices. Intelligent automation platforms leverage the powers of Artificial Intelligence (AI) and Machine Learning (ML) and are the true enablers of STP in invoice processing.

Intelligent invoice processing automation with JIFFY.ai HyperApp

JIFFY.ai’s Invoice Processing HyperApp is a low-code application that helps your AP team to achieve end-to-end straight through processing. Its automated workflows connect seamlessly with third-party business systems such as ERPs. Pre-configured business rules enable your AP team to handle disruptions in the receipt of invoices, including fluctuations in invoice volumes, and suppliers requesting part or early payments.

With intelligent document processing capabilities, it can handle structured (e.g., invoices, loan applications etc.) and semi-structured (e.g., financial reports) data from many types of documents, and ‘learn’ these variances continuously. It can extract complex data from OCR, handwritten notes, and even from tables within PDF documents (deep document processing) thus enabling completely ‘touchless’ processing. By helping your AP team to set up a supplier portal, it enables suppliers to submit invoices electronically, and also to speed up the approval processes with minimal manual intervention.

Discover how JIFFY.ai’s Invoice Processing HyperApp, built on our intelligent automation platform, is changing the paradigm of enterprise automation in our white paper titled ‘Optimizing working capital for the enterprise with intelligent automation’.

Drop an email to marketing@jiffy.ai and our HyperApps experts can help you accelerate invoice processing, straight through!

Mention office automation systems, and people immediately think of eliminating paperwork. While that is one result of implementing office automation software, it’s far from the only benefit. Read on to learn about the different types of office automation systems and how automating office tasks can boost efficiency.

Types of Office Automation Systems

Office automation can be achieved through various systems, most of which offer a less-than-perfect solution. Here are three types of office automation systems.

OCR

Optical character recognition, better known as OCR, can recognize text in scanned documents and images and provide that information in an accessible electronic format. A significant drawback with OCR as an office automation system is that it introduces numerous errors as it scrapes documents for information. Staff then need to take hours or even days to track down the details and correct the errors.

RPA

Robotic process automation (RPA) utilizes technology managed by business logic and structured inputs. The rules that govern RPA bots are so inflexible that any change to the data transposition, user interface or APIs can require additional configurations, which cost time and money.

AI/ML

When used for office automation, artificial intelligence (AI) and machine learning (ML) employ flexible rules to identify and gather the necessary information and complete the appropriate business processes. JIFFY.ai’s Automate is an example of an office automation solution that puts AI and ML to good use, giving companies the agility to create the software they need quickly and easily.

The Role of Office Automation Systems

When properly implemented, office automation systems are powerful tools that can manage a wide range of functions, including:

• Data management
• Accounting
• Inventory management
• Facility management
• Training
• Other administrative tasks

In assuming these functions, back-office automation software can deliver significant improvements, such as:

• Eliminating manual processes
• Identifying inefficient workflows
• Facilitating informed decision making

The Benefits of Office Automation Systems

Regardless of its function, office automation system software helps reduce the costs associated with the tasks it performs. Office automation products also provide other essential benefits, including:

• Better accuracy
• Improved data storage and management
• Better business processes
• Streamlined information sharing

All of these benefits help improve efficiency in a direct, impactful way.

An Office Automation System Example

JIFFY.ai’s Invoice Processing HyperApp is an excellent example of an office automation system that boosts efficiency using AI and ML.

Using HyperApps, you can create office automation solutions that achieve straight-through processing (STP) of invoices. STP means that an invoice is received, approved and paid without manual intervention. That can lead to considerable efficiencies compared to traditional manual processing methods, which can take up to two business weeks to clear one invoice.

Our Invoice Processing HyperApp provides automated workflows that connect effortlessly with ERP and other third-party business systems. It also offers pre-configured business rules to manage aberrations like invoice volume fluctuations and supplier requests for part or early payments.

Develop Office Automation Systems With JIFFY.ai

You can use office automation systems developed with JIFFY.ai HyperApps to cut costs, improve workflows and boost efficiency. Contact us today to speak with HyperApps experts who are ready to help you get the most out of your office automation software.

What is automation in a modern enterprise environment?

In a nutshell, automation can be defined as a collection of technologies (bots, business rules, sensors, programming scripts, etc.) that can complete a task with little to no human intervention.

Investment in automation has increased in response to the pandemic, and digital automation platforms have proved to help organizations get more ROI, faster. Deloitte’s 2020-2021 survey found that 68% of business leaders leveraged automation to respond to COVID-19 impacts. 1 in 3 have accelerated their cloud-hosted automation spends, which indicates a growing demand for digital automation platforms hosted on the cloud.

So, what is a digital automation platform?

A digital automation platform refers to a software application that lets you automate iterative processes like employee onboarding, documents management, and invoice processing through the use of digital tools, cognitive technologies like AI, and integrations with existing enterprise systems.

This is a significant departure from traditional robotic process automation (RPA) which is more siloed and targets only standalone jobs or tasks in an enterprise. To understand this further, let us look at the defining traits of a digital automation platform.

Key Features of a Digital Automation Platform

A platform, by definition, is a foundational technology that acts as a base to integrate additional systems, extend processes, and drive interoperability so you can connect your enterprise seamlessly. A digital automation platform lets you deploy enterprise automation in a turnkey yet extensible manner. Its key features include:

• Extensibility – The platform integrates with your existing systems like enterprise resource planning (ERP), human resource management systems (HRMS), customer relationship management (CRM), configure-price-quote (CPQ), etc., typically via the cloud. It can exchange data and react to events happening on other platforms.

• Cognitive technology readiness – A next-gen digital automation platform must incorporate artificial intelligence (AI), which is among its biggest differentiators when compared to RPA. AI allows the automation platform to handle exceptions, understand diverse kinds of data, and perform other human-like activities.

• Ease of use – Digital automation platforms must be easy to implement without compromising on flexibility for customization. Typically, they provide a graphical user interface (GUI) experience, as opposed to RPA, which is usually script-based. Business users and technical experts alike can use these platforms to automate the processes they need.

What Are the Benefits of a Digital Automation Platform?

A platform that helps to automate tasks and entire processes end-to-end offers a host of benefits in the short-, mid-, and long-terms.

In the short term, it helps to improve the work experience of employees stuck with mundane and repetitive tasks like filling in forms for employee onboarding, making data entries for processing an invoice, or manually converting information from a PDF document to a machine-readable format. In short, it helps to unlock significant time and effort savings for the enterprise.

In the mid-term, the process landscape becomes more clarified because the digital automation platform powers interconnectivity between processes. For instance, invoice submission to payment disbursal can happen in a single workflow without data, time or effort lost in the gaps of process silos.

In the long term, the enterprise achieves greater digital maturity and transformation in work culture, and is able to focus more on value generation than rules-based task completion.

Digital Automation Platform at Work

One of the most perceivable examples of how an automation platform can be implemented to drive significant ROI is a process that involves significant volume of document processing. Whether it is for employee onboarding, invoice automation, or supply and procurement, workflows that require to process massive numbers of documents every day require a more sophisticated platform approach. A digital automation platform can accept document submissions, extract information using machine learning (ML) and optical character recognition (OCR), fill out forms, raise exceptions if any, and notify the stakeholders using a variety of integrated automation technologies seamlessly.

The future of digital automation platforms is poised to push beyond complex process automation. Providing organizations with the ability to assemble enterprise-wide applications that natively integrate automation into workflows, streamlining front-to-back-end data flows and deploying data analytics are also critical elements to achieving efficiencies and advancing digital transformation. Such integrated, intelligent automation platforms will usher in more opportunities for organizations to transition to cloud-native, API-driven systems, and will also help business leaders radically rethink product design and unlock the full benefits of digital modernization.

To learn more about how the JIFFY.ai AUTOMATE integrated intelligent automation platform can help your business rethink digital transformation, email us at marketing@jiffy.ai.

Robotic process automation is a crucial technology for digital transformation. According to Deloitte, 72% of companies would have already begun their RPA journeys by the end of last year. Within the next three years, RPA will become near-universal due to its beneficial impacts – cost and effort reduction, simplification of processes, scalability, and a better work experience. To understand how you could achieve these benefits, let us explore the concept of robotic process automation in detail.

What is Robotic Process Automation (RPA)?

Robotic process automation is a technology that eliminates manual processes by using software robots (i.e., bots) to perform the job, configuring bots in a manner that they can detect, respond to and act on events occurring across the enterprise.

RPA has several component technologies that allow it to emulate human labor:

• Automation scripts – These scripts define the workflow that the bot will follow.
• Application programming interfaces (APIs) – RPA uses APIs to connect with various enterprise systems and fetch data. APIs make it possible to detect events in different systems.
• Sensors and triggers – These are the data sources that inform an RPA bot whenever an action is needed. Typically, sensors reside in enterprise systems like CRM or ERP, and communicate with RPA via APIs.
• Bots – Software bots are programs that use automation scripts and APIs to detect an event and automatically perform a workflow.
• Graphical user interface (GUI) – This is an optional component that allows business users to configure the bot without having to write code.
• Artificial intelligence – Another optional component, AI allows bots to learn from past workflows and process unstructured data.

The last two components are characteristic of the next step in the evolution of RPA, which is intelligent automation.

How Does Robotic Process Automation Work? Key Functionalities Explained

RPA mainly applies to repetitive tasks that are not quite complex in nature. They involve fewer steps, which make them easier to automate. And because of their repetitive nature, you can implement RPA in bulk and automate a high volume of processes for maximum returns. For example, there are myriad use cases for robotic process automation in banking because banks have a large volume of repetitive, low-complexity processes for which accuracy is highly critical. These include application processing, reviewing and confirming know your customer (KYC) documentation, answering customers’ FAQs, data entry, and checking and confirming financial transaction validity.

Here is a step-by-step breakdown of how robotic process automation works:

Step 1 – The organization sets up the RPA software system by identifying the task to be automated, configuring the bot accordingly, and integrating with the necessary systems. For example, a financial services organization can set up a bot that monitors a stock price online and updates the stakeholders if it crosses a threshold.

Step 2 – The organization ensures that RPA is properly configured and integrated. In our financial services example, the bot mentioned above must be connected to social media, financial service information feeds, and online publications to fetch stock price data. It must connect to a notification system like a collaboration platform or email to send updates to the appropriate user(s).

Step 3 – Users are trained on RPA, so they can rely on these automated processes to improve productivity and outcomes. In our example, employees of the financial services organization must get trained to know which notifications to watch for and how to act on them for the appropriate stakeholders.

The Benefits of RPA Face the Challenge to Scale

Most early adopters are already vouching for RPA’s ability to boost process efficiency which has helped them save costs and effort. According to Deloitte, RPA has helped to improve compliance (92%), productivity (86%), and quality/accuracy (90%) in organizations.

For example, the HR team of a global professional services firm not only reduced onboarding time from three hours to 17 minutes but also removed manual intervention permanently so that its onboarding processes run automatically 24/7. 

However, organizations that have deployed only RPA for automating their tasks are facing the challenges of scale and technical debt the bots brought in: 

• RPA may not be integrated with your end-to-end enterprise stack, leading to lost opportunities. 
• Traditional RPA does not come with cognitive (AI) capabilities, which means it cannot handle process exceptions or unstructured data. 
• RPA requires complex code-based configuration, and you might need to rope in IT every time your workflow or process changes. 

Such limitations are pushing organizations to look beyond only RPA when embracing end-to-end digital transformation.

An intelligent automation platform, though, powered by natively integrated AI, ML, OCR, intelligent document processing, and a configurable low-code interface multiplies the benefits of robotic process automation and makes it sustainable for the long term.

To learn more about the benefits of enterprise automation and the JIFFY.ai AUTOMATE intelligent automation platform, contact us at marketing@jiffy.ai.