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How AI-powered Autonomous Finance Technology Helps Connect the CFO’s Office and Ensures Finance Transformation

Strengthening and energizing your business model requires a lot more than refining supply channels. Modernizing and streamlining the Financial value chain operating through the CFO’s)Office is just as essential. If the CFO does not participate in an ongoing integration project, they lose an excellent opportunity to shape how those integrations will impact work. 

Research shows that 1 in 3 Financial leaders prioritize having an integrated, single source of data as the biggest change they expect from digital transformation. Autonomous Finance technology takes you closer to the goal of Finance transformation with an integration-first approach, among other benefits. 

RPA Alone Is Not Enough

When companies adopt traditional robotic process automation (RPA) for Accounting and Finance, a lack of integration within the CFO’s Office is a common challenge. Initiatives to modernize Procure-to-Pay (P2P), Order-to-Cash (O2C), and Record-to-Report (R2R)— three key financial processes—can quickly fall through the gaps when Marketing and Sales teams – and not the CFO’s office – drive almost every aspect of digital transformation. 

Other departments simply “leave it to the CFO,” assuming that the CFO’s Office possesses the inherent expertise to drive automation, integration, transformation and other initiatives. The problem with this approach is that while everything else is being digitally transformed and optimized, Financial integration within the CFO’s Office is often left behind. 

According to a recent study, CFOs are eager to embrace the digitization of nearly every Finance & Accounting (F&A) function. Seventy-four percent of global CFOs are looking to accelerate into an era of enhanced reporting, moving beyond traditional R2R. Advanced analytics is a top priority with more than 1 in 3 CFOs investing in related technologies over the upcoming years. Without integrated Financial systems, these projects will fall short of expectations. 

What Happens When Finance Systems Do Not Speak?

Most businesses rely on different systems to meet various operational requirements. Different architectures prevent these disparate systems from interacting. In the absence of data interactivity, information silos form, inhibiting  growth and holding back opportunities for automation or more advanced autonomous decision-making. 

This problem is further compounded in large organizations. The greater the size of the business, the larger the number and intricacy of its components—spanning numerous departments, sub-entities, and stakeholders. This complexity permeates the CFO’s Office, where work involves sourcing and procurement (part of P2P), financial analysis and planning (R2R), risk mitigation and regulations, as well as reconciliations (part of O2C). 

To administer these complex processes, most F&A teams use a mix of point solutions, including spreadsheets, RPA bots, and ERP and CRM tools, among other technologies. In addition, the treasury employs a separate system because it requires an intricate structure to oversee settlements, legal compliance, liquidity, and cash. 

With the ecosystem built on independent, siloed solutions piled on top of each other,  extracting data in a timely manner becomes tedious and time-consuming. The pivotal P2P, O2C, and R2R processes remain disconnected, the back and front office systems do not speak to each other, and the entire environment is unsuitable for Autonomous Finance, which is far more mature and streamlined than traditional RPA. 

For example, if an important transaction such as procurement in P2P goes awry, the CFO does not have  immediate insights into the issue, because the relevant data resides in a system to which only the procurement staff has access. By contrast, R2R operates separately and without real-time synchronization.

This means the CFO must traverse a labyrinth of solutions to garner, review, and share an accurate perspective of the issue to other stakeholders. As such situations can arise on a daily basis, the CFO gets encumbered with daily transactions, and is left with less time for strategic decision-making. 

How Can Autonomous Finance Technology Help Accelerate Finance Transformation?

Autonomous Finance technology can play a vital role in addressing these issues. Powered by Generative AI and built on no-code technology, an Autonomous Finance platform can help to complete digital transformation in the CFO’s Office and make it meaningful for every stakeholder in the F&A ecosystem. 

The technology ensures that the P2P, O2C, and R2R processes are straight-through and streamlined, brings autonomous decision-making capabilities to F&A processes, and also makes sure that  the front and back offices can speak to each other and exchange data. It demolishes  information silos and reduces overreliance on coding and IT efforts. 

Autonomous Finance helps CFOs to adopt a micro-innovation approach; the platform provides building blocks to create intelligent self-learning and evolving apps to streamline various use cases – from price blocks and cash allocations, to cash flow, payment prioritization, and many more. These apps come with pre-built data models and system connectors designed to overcome the lack of interconnectivity within the CFO’s Office, and between Financial processes and the rest of the enterprise. 

3 Ways an Autonomous Finance Platform Connects and Speeds Up Financial Processes

Implementing an Autonomous Finance platform is a quick and effective way to overcome integration challenges and achieve autonomous decision-making capabilities.  It helps CFOs facing system and information silos in the following ways:

1. Empowers business users to build rapid solutions 

Traditionally, RPA automation relies on detailed scripts and extensive coding efforts. In the face of exceptions, this approach further adds to cost and operational overheads. On the other hand, Autonomous Finance empowers business users to build their own solutions involving complex third-party interconnections by leveraging their domain knowledge and understanding of the business. As it is a no-code platform, accountants, procurement professionals, and other executives in the CFO’s Office can quickly turn their visions into action. 

2. Makes use of pre-built models and workflows 

Autonomous technology goes beyond RPA in Finance, providing users with pre-built modules based on industry knowledge. This includes data models, workflows, Finance widgets, and system connectors that allow the different nodes of the CFO’s Office—P2P, O2C, and R2R, – to talk to each other. Integrations can be  built faster without compromising the quality of app development, and also without too much technical dependence. 

3. Opens up opportunities to integrate Generative AI 

Generative AI has unlocked new frontiers in Finance transformation. It can cut across system and data silos to accelerate app development and facilitate insights-driven decision-making. For example, an Autonomous Finance platform powered by Generative AI can be used to build Financial apps (or any other apps) 10X faster, by providing real-time suggestions, intelligent recommendations, and best practices for creating or customizing AI-powered hyper-automated apps. The platform also streamlines the flow of data between these processes to seamlessly connect disparate elements of the CFO’s Office. 

The Age of Autonomous Finance is Here 

Tedious manual processes impede the speed and efficiency of critical F&A tasks such as invoice processing, and managing the collection and reconciliation of receivables. This, in turn, extends Days Sales Outstanding (DSO) metrics and reduces net revenue resulting from delayed payments and inefficiencies in cash management. Even though automating these processes partly helped to curtail DSO to a certain extent, human involvement has still been necessary to ensure the right outcomes. 

In 2023 and beyond, CFOs should no longer struggle with the age-old problems of data deficiency and disconnected systems. CFOs advocating for Finance transformation through end-to-end automation in their organizations should consider switching to AI-powered Autonomous Finance technology. It helps them to achieve “touchless” F&A, significant cost reductions, and lower average DSO figures, while enhancing resilience and transparency in the face of unforeseen cash-flow challenges.

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 cfo.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://cfo.jiffy.ai/solutions/banking-and-financial-services/

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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.

cfo.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 cfo.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. cfo.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, cfo.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 cfo.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.

cfo.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, cfo.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), cfo.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 cfo.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. cfo.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 cfo.jiffy.ai/ HyperApp
• Set up exception, validation and error handling through human-in-the-loop and self-learning systems

cfo.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

cfo.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 cfo.jiffy.ai/ to address automation challenges and modernize their workplaces for the future. Email us at marketing@jiffy.ai to learn more.

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 cfo.jiffy.ai/ HyperApp

cfo.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 cfo.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. cfo.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

cfo.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 cfo.jiffy.ai/

You can use office automation systems developed with cfo.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.