The Credit Professionals Guide to AI and RPA · The RPA Maturity Scale: Understanding the 3 levels of RPA in finance technology RPA technologies have been around for a number of years,

The Credit Professionals Guide to AI and RPAAlmost everything you need to know about Artificial Intelligence and Robotic Process Automation.

A R I M I L I A P U B L I C AT I O N

© 2020 Rimilia rimilia.com 2

IntroductionWhen robotics is mentioned in conversation, people instantly think of physical applications, such as car manufacturing with robotic arms, conveyor belts and big automated machines. Physical robotics and automation have been used in factories and production lines around the world for decades.

Adoption of this form of robotic automation is high. The huge benefits of using robotics to replace manual workers on the production line are plain to see, but there is another type of robotic automation that may have an even wider impact on industry that isn’t as visible – at least not yet.

Software Robotic Process Automation (RPA) can provide just as groundbreaking results by replacing and expanding upon manually intensive data handling processes currently done by humans. The potential is staggering, but software RPA has been much slower to penetrate enterprises in back office functions.

178

2013 2014 2015 2016 2017* 2018* 2019* 2020*

221

254

294

346

378

433

521

The Rise of Industrial RobotsEstimated and forecast supply of industrial robotics worldwide (in thousand units)

Source: International Federation of Robotics *Forecast


PwC estimates that 45% of work activities could be automated using RPA technologies, which would save over $2 trillion in workforce costs globally.

Companies are starting to realize what a revolution this could be for them. Enterprise demand for RPA tools is growing rapidly at 20-30% each quarter, according to research by Gartner.

However, despite this, adoption remains low among finance teams. SSON’s State of the Shared Services Industry Survey 2016 found that 69.9% of respondents have not adopted robotic process automation. It’s surprising because over a third of companies that have implemented RPA have reported that they can see the technology impacting at least 60% of their processes.

45% 20-30%

45% of work activities can be automated

20-30% Enterprise demand for RPA tools

$2,000,000,000,000$2 trillion savings in global workforce costs


In comparison, those who hadn’t tried RPA simply didn’t see the opportunity – perhaps it’s a case of not knowing what they don’t know?

A recent survey by the Association for Finance Professionals (AFP) found that 58% of finance and treasury professionals say the biggest hurdle to adopting emerging technologies in their organization is “awareness and engagement.”

From all of this, it appears that lack of knowledge of Artificial Intelligence (AI), RPA and related technologies is a significant roadblock to driving technological improvement in finance teams.

Armed with the knowledge of AI and RPA, and what it means for finance, credit professionals will be able to navigate the new challenges, and exploit the huge new opportunities that it brings.

In this eBook, we’re going to: • Outline key AI and RPA terminology and what it means for you

• Explore the maturity scale of RPA, to make sure you don’t get stuck implementing outdated solutions that call themselves ‘AI’

• Uncover why Machine Learning and predictive analytics are key to true AI finance automation in areas such as cash application and credit management


Defining Artificial IntelligenceArtificial Intelligence is placed to have a deep and wide-ranging impact on modern society, across all industries and every job function. The impacts are already beginning to be felt throughout the enterprise. Finance teams are no exception to this massive technological revolution and without understanding the changes coming, and those already here, they risk being left behind.

With this revolution comes huge opportunities for finance teams. The finance professional that understands and utilizes new technologies, and ushers their teams into the AI age, stands to reap big rewards.

Artificial Intelligence as it appears today is not just a single standalone technology, but in fact, a set of several related technologies that together form the rapidly evolving new frontier of automation.

With this in mind, it’s key to first understand a few of these core technologies that come together to form AI.

Artificial Intelligence: The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, pattern learning and translation between languages. Also used as an umbrella term for technologies based on advanced automation and data analysis techniques.

Robotic Process Automation: Machine processes that can replicate manually intensive processes normally carried out by humans. This can range from simple process replacement, such as scanning technology, to the foundation behind predictive analytics and Big Data analysis.


Machine Learning: A computer process that provides systems with the ability to automatically learn and improve from experience without having to be explicitly programmed, similar to human learning. Machine Learning focuses on exploring large data sets to find patterns of behavior and incidents, to construct complex algorithms that cluster or predict future similar events. Machine Learning learns for itself.

Big Data: While there probably is no final consent on a definition of Big Data, it typically relates to the continuous flow of data that never ends and continuously changes. For example, Twitter data. Vast amounts of endless data that may be analyzed to reveal patterns, trends and associations, especially relating to human behavior and interactions. Big Data is often very difficult to interpret and utilize fully without implementing Artificial Intelligence-related processes, such as predictive analytics.

Predictive Analytics: Closely related to Machine Learning, predictive analytics is the practice of extracting information from existing data in order to determine patterns and predict future outcomes and trends. Predictive analytics allows us to take seemingly unmanageable amounts of data and build layer upon layer of information, one on top of the other. This intelligence can then be utilized in an enterprise for strategic decisions.

Cognitive Computing: The simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works and reasons.

Natural Language Processing (NLP): The field of computer concerned with allowing machines to process and meaningfully interact with natural (human) languages.


Robotic Process Automation: The bedrock of AIGrowing Interest in RPA

RPA is generating considerable excitement in the world of finance. See this Forbes article on how RPA opens new doors for finance and risk.

Management typically starts looking at automation and RPA by exploring those functions where there are high-volume and low-complexity processes, such as:

• Expense Management • Review and Payment of Incoming Vendor Invoices• Monitoring of Customer Credit• Credit Collections• Cash Application • Credit Management• Bank Reconciliation• E-Commerce

Optical Character Recognition (OCR) technology can scan invoices and automatically prepare payments, use logic and rules to validate and auto-match invoices, monitor compliance and route exceptions to ‘real people’ for review, decision-making and approval.

Although RPA is a well-established approach to cost reduction, quality improvement and productivity enhancement, there are large and important differences between the RPA of the past and modern, AI-powered RPA. RPA becomes exponentially more important as advanced technologies, such as predictive analytics, Machine Learning and cognitive computing, turbo charge automation to provide enhanced capability for enterprises.

However, to date, the real magic has been masked by misrepresentation of AI and RPA – as well as the mislabeling and/or relabeling of older technologies as AI.

https://www.forbes.com/sites/steveculp/2017/01/09/robotic-process-automation-rpa-opens-new-doors-for-finance-and-risk/#7ac45cb86ef8

https://www.forbes.com/sites/steveculp/2017/01/09/robotic-process-automation-rpa-opens-new-doors-for-finance-and-risk/#7ac45cb86ef8


The RPA Maturity Scale: Understanding the 3 levels of RPA in finance technology RPA technologies have been around for a number of years, and there are big

differences between the maturity and development of solutions that claim to use them.

It’s important when looking at RPA or AI solutions to know exactly what level of maturity

they offer. This could be the difference between implementing dated solutions that

were available 15 years ago and implementing modern, cutting-edge AI solutions that

will truly transform your team and your enterprise.

Level 1 ‘Simple Process Replacement’

This level typically consists of older technology, which is now being re-branded as ‘Artificial Intelligence’. This includes OCR or data capture engines with a new interface.

While they perform the role of simple process replacement and are a basic form of RPA, they are nowhere near a true AI solution and have often been in the market for 10-15 years.

Level 2 ‘Learning’

This level is also known as the ‘macros on steroids’ revolution, which started in the 1990s when companies employed their own macro expert in accounts to supercharge their organization’s spreadsheets. Technology providers at this level often talk about RPA, and technically they are correct, but the technology they employ is old, and under the hood, is really no different to what existed back in the 1990s.

Many solutions in level 2 actually have a one-size-fits-all version of RPA. They look at a set number of processes. They look at points of process repetition and seek out repetitive patterns.

They then talk about robotics and automation, and throw in lots of buzz words like Data Transformation. It’s annoying.


3 Levels of RoboticProcess Automation

Simple ProcessReplacementOCR scanning, single 1-to-1 matches, enhanced Excel lookups, not intelligent and no learning

LearningBuilding algorithms to remember previous actions, remembering aliases for customer application

CognitiveWorking with the user to provide suggestions on how to perform application and presenting application options

1

2 3


For some companies, data transformation may simply mean auto-correcting a reference number in a bank file: The machine knows the number 654231 should be 654321 and has the ability to perform a simple correction. This isn’t data transformation.

Same goes for level 2 auto-matching. This is not the advanced RPA that contributes to a truly AI solution, this is really a one-time transactional matching process.

According to Steve Richardson, director at Rimilia, companies employing level 2 ‘RPA solutions’ would expect to see an improvement of between 20-30% in matching rates. But you won’t achieve the big transformational jumps in performance, or the data insights that come from level 3 RPA solutions, within a wider AI solution.

Level 3 ‘Cognitive’

In levels 1 and 2, every action is performed like it’s the first time the software sees a transaction in a bank file, just as an example. However, there are solutions that go beyond levels 1 and 2 with cognitive functions – with Machine Learning. For example, when they see a match for the first time, they recognize it again the second time. They learn from previous experiences, providing the additional benefits that only come from level 3 RPA.

This is where the new technology of Machine Learning is introduced to turbo charge ‘simple’ RPA.

As a result, what you end up with is a system that’s constantly learning, improving and assisting the user with complex decisions; a system that is able to predict and forecast what’s going to happen the next time. The result is a much more efficient, machine-led process, which improves and learns over time.

Level 3 delivers a transformational 80-90%+ match rate benefit, compared to the 20-30% improvement in levels 1 and 2 – or older technologies.


First, let’s clear up a small issue about the terminology.

In the UK we talk about match rate, and across the pond in the US, it’s called hit rate. It means the same thing.

But be careful what you’re calling a match.

For example, for some, a match is the Accounts Receivable tool detecting a payment is being made to Account 123 using the account number. It’s a match.

Or is it?

Is it a match if we receive a payment from Account 123 and we apply that cash to the account?

We say no.

We say it’s a match when we see the payment to Account 123 and we can automatically/intelligently and accurately apply the payment to one or many invoices.

That’s how some from level 2 RPA try to claim an 80% match rate.

We talk about match rates of 95%+. That is fully matched. Straight through – from start to finish. Check the truth about what you’re being told. Or you may end up matching a payment to an account, and then still needing to manually apply the payment across the unpaid invoices.

Important: What is the Truth About Match Rates?


No Remittances are Required Here is an example to illustrate the differences between the 3 levels:

A company is looking for cash application process automation. It’s a real-world example and one which many finance teams are looking at to provide solid results.

Levels 1 and 2 RPA technologies approach the task by scanning remittances. They will scan 100% of the remittances, and they’re pretty good at it, getting a 60% auto-match.

With level 3 RPA, part of an AI solution, they get a transformative 90%+ match rate. Not only do they benefit from a much higher match rate, but because the system has integrated Machine Learning and is learning and not just processing, you may only need to scan 10% of remittances. A very big difference.*

*Note: It’s worth saying here that some companies still scan all remittances, even though they don’t really need to because they are using true AI. Some still choose to scan remittances for internal and external audit backup, or to satisfy their own confidence levels (i.e., keeping a complete set of files for 7 years).

The productivity benefits are immense. With the advent of faster payments (where the cash arrives in the bank sometimes 3 days ahead of the receipt of the remittance), there are significant advantages of improving the speed of closing the month and applying the cash.

Machine Learning in Accounts PayableThe integral Machine Learning aspect of level 3 RPA is an important technological advance. The machine learns what a customer is doing, their behaviors, their patterns of payment, and their values. The machine can learn from user input and how exceptions are managed. The machine remembers the previous actions (how the exception was handled) and uses the information/behavior to follow the action next time – without the need for human intervention.


“We’re getting an auto-match of 90%+

in most cases. If it’s not 100% correct,

Rimilia Cash will provide the user different

options to do a one-click match. So,

even though it might not know the exact

answer 100%, it will provide the user

machine-learned options. Generally,

99% of them are always the first option

anyway, with the way the machine works.

“The machine also learns about the

answers that are given on near matches.

It will measure how many times the user

took the machine-learned option and at

a certain percentage, you can turn that

machine option into an auto-match.“

- Steve Richardson, Commercial Director, Rimilia


Don’t Try and Change the CustomerYou can try of course, but in reality, you’re going to have limited success. We’ve seen it so often, a company trying to send out remittance templates for suppliers to complete. They just won’t do it – they might be dealing with 30,000 suppliers and they’re not going to change their AP system just for you.

We say: “Don’t always try to change the customer’s behavior because AI-driven software can analyze that behavior and adapt to it instead.”

If the customer is a good payer and paying on time, but just giving you bad information, the approach is to read that bad information and turn it into usable information. Let’s not go back and annoy the customer by making them change something they probably won’t do anyway. Let’s interpret and read it. For the repeat customers who are always causing you problems, you can start to identify the ones where you need a remittance and where the remittance always comes after the payment.

It’s in both the customer’s and the finance team’s interest to just ask for the remittance a little bit earlier via email. You’re prompting the right customers to change the right behavior for the right reasons, which is simple for them to change as well. You end up providing better customer service by focusing on the right change, not on complete change.

Predictive AnalyticsCurrently, most companies are sitting on a glut of raw data that they are not even close to fully utilizing. Predictive analytics helps companies take advantage of this huge resource by picking out patterns that are not visible in a spreadsheet or simple graph.

While some companies are realizing the value of this, with businesses spending on Big Data technology reaching $57 billion by the end of 2017, according to SNS Research, and three out of five people in leadership roles saying that a failure to get on board with Big Data could lead to obsolescence, out of 85% of companies trying to be data-driven, only 37% of that number say they’ve been successful. To truly be data-driven, finance departments need to harness the power of predictive analytics.


Predictive analytics, along with Machine Learning and RPA, contribute to a truly AI solution, and when used together, can produce tangible results above and beyond their use individually within finance teams.

Software utilizing predictive analytics can exploit patterns found in historical and transactional data to identify risks and opportunities. This can be used to great effect where there is a huge amount of historical data, such as in credit collections and credit management, providing additional intelligence to a credit professional to make strategic decisions.

Cash Can be PredictableCash coming into any business has always been important. However, if for example, a faster payment means the remittance lags receipt of the cash, sometimes the cash can’t be released for use in the business. With predictive analytics as part of an AI solution predicting payments and customer payment patterns, the system’s algorithm can do the application based on historical data. Predictive analytics can be used to forecast and predict future payments.

This method gives a much better view of what the cash looks like for the following month. It’s where intelligence starts to add real value to the business: the working capital model suddenly becomes automated and much more exact, providing benefits that impact not just the finance team.

You can go from a ‘finger in the air’ with a spreadsheet, to formulating concrete strategies on the back of real intelligence with solutions using predictive analytics.


Realizing Real-Time FinancesWith AI and RPA, it’s possible to take receipts from faster payments and match them to the right invoice and the right account, as well as update accounts, customer records and ERP in real time. It gives a real-time view of the cash, and therefore, a real-time view of the debt. As a result, it’s possible to:

• Collect against debt in real time• Update that debt in real time so the rest of the business can see what’s happening• See promises to pay in real time

Added to this, bank reconciliation is always in real time.

In short, with AI and RPA, you get a real-time view all the way from cash, collections and bank records, of what’s happening in the business.

Predictive Analytics is Driving the Transformation of CreditEvery transaction and contract agreed to has a level of trust involved, i.e., your customer trusts that you will provide a service or product, and you trust that the customer will pay you. The Latin origins of credit (credere) even mean trust.

When it comes to collecting debts, the traditional routine has been to target the largest customers with the greatest overdue debt first, working back typically via descending values and predominantly via a spreadsheet.

This is changing.

With the introduction of new credit technology and processes, specifically, the use of robotic process automation and predictive analytics, the increasing collection of data is more critical than ever.


Where segmenting customers purely based on their volume of aged items currently forms part of a standard approach, the use of numbers and availability of ‘Big Data’ is a step forward for many.

By transforming information into intelligence, it’s possible to move away from the norm and begin to automate and improve processes. Using an inbuilt forecasting engine based upon client behaviors, among other things, new automation platforms can predict when customers are going to settle invoices.

New credit management solutions, such as Rimilia Collect, do exactly this, and where they succeed is with the visibility provided to the end user. Rather than having to navigate through endless data points to fully understand a debtor’s true exposure, their risk nature and the contacts that have been taken, data is maintained within a single location, ensuring complete receivables.

This information, while displayed centrally, can then be interrogated by the credit management team through a series of segmentation processes.

There are always skeptics.

As with all new technologies and processes, there is skepticism in moving towards automation.

While for many credit professionals the difficult and time-consuming task of completing a cash flow forecast is a lot less scientific, the pressure of achieving a cash target is now more important than ever.

In its early stages, people are unsure of automation and whether it will truly benefit them, however, initial results from our customers using automation are significant. It’s possible to forecast payments with a significant degree of accuracy.


This ability to accurately predict payment behavior has two profound benefits.

1) Cash flow forecasts are provided with a greater degree of certainty and can be justified based upon actual data.

2) The re-alignment of collections resources to target customers, whose behavior means that they are unforecastable.

For a long time, credit professionals haven’t been able to accurately track the value-added benefit that each call by their collections teams really has on the overall month end debtor position.

How many of you have made or heard calls where customers’ payments are already in the post, because that customer has, over the last 6 years, always paid on the 15th of the month? How often have you reflected on your own collections, or made that nice phone call just to hit your KPI, knowing the money was probably already on its way?

In the cold, hard light of a month end, will you regret those missed opportunities to make a difference?

To actually make a difference, credit professionals have to be equipped with the right tools to drive real collection performance.

Strategies driven by credit management software using predictive analytics can ensure value, risk, and defaulted and new debt are covered. This approach has resulted in a greatly reduced level of bad debt, while showing an increase in values collected per customer contact for many companies.


Want to learn more about how AI and RPA can improve

your finance operations? Our senior consultants are

here to help and will be happy to talk you through how

companies like McKesson, Ingersoll Rand, Wesco, and

Veolia have all harnessed AI finance solutions with

award-winning results.

Contact Us

The Future of FinanceFinance departments are traditionally more conservative when it comes to adopting radically new tools. Credit professionals need to be cautious, as they handle some of the most important processes and sensitive data of any department within an enterprise, and this has led to a slower adoption rate of RPA than in other sectors within business.

However, finance teams are some of the most perfectly placed departments to take advantage of the rapid growth in RPA and AI technologies, for some of the exact same reasons they have traditionally been cautious. They have access to huge amounts of data, ideal for solutions utilizing predictive analytics, and run many important but time-consuming processes that can benefit from robotic process automation using Machine Learning.

To keep up with the growth of AI solutions and the rapid transformation they are providing for many enterprises, credit professionals not only need to be aware of the solutions available, but need to be thinking about how they will impact their teams in the future, and how they can be harnessed to provide results that echo throughout their business.

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Documents

The Credit Professionals Guide to AI and RPA · The RPA Maturity Scale: Understanding the 3 levels of RPA in finance technology RPA technologies have been around for a number of years,