AI and the Audit: What does a robot need to audit your numbers?

In the previous post, we examined the value propositions that Appzen's AI brings to auditing expense reports.

In this post, we analyze what insights we can extract from Appzen when it comes more broadly to applying AI to the external financial audit.

The following gives a refresher on how the Appzen AI audit works:

Based on this we look at a number of factors that exist in this process to develop

Standardized process:
The expense report process that has been fairly standardized for over a decade: employees submit a digitized report of what they spent, expense codes, commentary and all the supporting documentation (e.g. receipts, invoices, etc.).  This is similar to how factories needed an assembly line before they could be automated.

Standardized capture and presentation of audit evidence:
I think this is a key piece: the actual audit evidence (i.e. receipts) must also be included in what's submitted to the auditor. As the evidence is provided in a standardized format, it enables machines to analyze these digitized source documents to run the necessary correlative models to run the risk scores and enables the automated analysis.

Audit evidence retains its chain of custody through the digitization process:
The auditor does not need to expend additional resources verifying that the evidence actually relates to the item being audited, nor do they have to expend additional resources ensuring that the independence of the evidence wasn't lost through digitization process. For example, when receiving a bank confirmation the auditor needs to ensure that this received directly from the bank and not the client.

Evidence provider identity is verified and contractually obligated to follow-up with the auditor:
The party submitting the audit evidence, the employee, has been verified in the system through the employee onboarding process. The implication of this is that the auditor doesn't have to expend audit resources confirming the identity of the evidence provider. Secondly, and perhaps more importantly, the auditor doesn't have to expend significant resources following up with the evidence provider. For example, not all customers will respond to accounts receivable confirms and then auditor will have to perform alternate procedures.

Evidence provider has incentives to produce the proper evidence: 
The previous point is closely related to the issue of incentives: if the employee fails to provide evidence then they will not be reimbursed. This puts a strong incentive on the employee to provide the evidence in a timely manner.

Provider of the evidence is trained on providing evidence:
The employee has been trained to provide complete, accurate and valid evidence. They also have access to help if they have issues with submitting expense receipts or understanding whether that evidence will be accepted.

Violations can be clearly defined and examples of violations can be taught to the system:
For fraud or errors to be flagged there needs to be rules that can be fed into the system to identify whether the item submitted needs further review or audit. For example, if the amount on the receipt doesn't match this would be flagged and has a high likelihood of error. But more importantly,  the examples of violations identified can be fed into the system to teach the system (via machine learning) what to look for.

In a future post, we will use these factors to look at how easily (or not) AI can automate financial audits.

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

AI and the Audit: Why hire a Robot as your Auditor?

On this blog, we've covered the topic of exponential change and how audit/accounting is prone to such forces.

Despite this, I am tempted to say financial auditing is different. It's not like factory work or more controversially like the pharmacist profession where AI claims to offer a safer alternative to dispensing medication.

But does that make me just one of the people who think that their profession is unique because they are in the midst of seismic change but refuse to see the writing on the wall?

At the same time, I don't want to come across as an alarmist claiming that the world is going to end when it really isn't.

It's the challenge of nuance.

While trying to figure out how to tackle this challenge, I came across AppZen; an app that uses artificial intelligence to audit expense report. It was identified in this post as being one of the game-changers in the fintech scene and was also featured in Accounting Today.

According to the company's website, the application "combines computer vision, deep learning, and natural language processing to understand the full context of expenses, not just amounts, dates, and merchant names. ReceiptIQ detects unauthorized charges in real-time from receipt images, boarding passes, travel documents, cell phone bills and any other expense documentation. Cross-checks expenses in real-time against thousands of external and social sources to determine if they are legitimate and accurate... Real-time identification of unauthorized upgrades in airlines, hotels and car rentals as well as out of policy claims for hotel laundry, alcohol purchases, cell phone charges and more."

Reviewing the company's video, I was able to extract the following value propositions:

• "100% Testing":  I put it in quotes on purpose because the idea is that the whole population is analyzed but only the high-risk ones are further analyzed. That is, this is still "examining on a test basis" but uses a risk based approach to identify what reports should be further examined. This is in contrast to the manual approach of sampling.  
• Automated exception analysis: Closely connected to the previous point, but to emphasize that there is an automated review of the population.
• Real-time analysis: Reports can be analyzed instantaneously. Although not explicitly identified in the video, this could have real world savings. Faster reviews - leading to faster reimbursement to employees - could reduce the overall amount owing on corporate credit cards thereby offering more favourable position with the credit card companies. 
• Seamless integration into existing processes: Add-on to an existing process is a much easier sell than an app that requires replacing the existing app you may have just bought.  
• Use of external data: The app uses 100s of external data sources to develop. It seems that this assists in building an expectation of whether the expense needs further analysis. 
• Limited false positives: Not explicitly stated, but it is strongly implied that the number of reports that need to be reviewed is few - meaning it's not flagging reports that are valid.   
• Reduction of audit costs and fraud: Finally, the app promises greater efficiency in the use of audit resources deployed and greater effectiveness in catching fraud. 

When looking at these benefits of AI-enabled automation, they are based on certain assumptions that may exist in the expense report realm but not in the external audit realm. For example, accounting records at a company are not normally accompanied by a digitized copy of the source document (e.g. invoice, receipt, etc.) that provides evidence of its validity, accuracy, etc. of that accounting entry. 

So which of these assumptions applies in the world of external financial audits? 

This will be the topic of the next post where I will develop a list of factors that enabled expense report to by automated by AI and see if they apply to our world. 

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist that is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else

Virtual Personal Assistants: How far will they go? Part 2

In the last post, I spoke about the advent of the Virtual Personal Assistants (VPAs) in terms of Gartners predictions as to where they will go and how popular culture sees them coming to enable our lives.

For the second part of this post, I wanted to talk about my first work experience - ever - with a fully virtual assistant.

Let me set the context.

In the course of my work, I was dealing with a vendor who was trying to arrange a meeting with us through his personal assistant, Amy Ingram.

So we were going back and forth to fix a date and time for the conference call.

I responded to the initial request as follows:

"Hi Amy,

Actually out of town on Tuesday; Thursday is open though. Does that work with you?"

"Her" response was (using Billy as a pseudonym):

"Hi Malik,

I'm sorry, but that time doesn't work for "Billy".

How about Wednesday, Jun 22 at 11:30 AM EDT? "Billy" is also available Wednesday, Jun 22 at 3:00 PM EDT or Thursday, Jun 23 at 9:00 AM.

Amy"


When I read Amy's response, I thought to myself something like: "I told her that Thursday is open, so why did she say that doesn't work for the "Billy"?"  But I thought something like "whatever" and just responded with:

"Thursday at 9 am works, thanks"

To which Amy responded:

"Hi Malik,

Thanks for letting me know.

I'll send out an invite once I've confirmed a time with "Jim".

Amy"

[Jim is my colleague; true name hidden for confidentiality purposes]

Eventually, it dawned on me: I wasn't dealing with a person, but a robot!

And then it hit me: the future is here.

The one thing that I realized through my interaction is how forgiving I was about the error because I thought the thing on the other side was human: everyone makes mistakes and so it's no big deal that "she" didn't get that I was open on Thursday.

This has a deeper implication on how "knowledge work" gets automated.

When we gauge machines for the ability to perform cognitive tasks, such as booking meetings, we should be careful as to how good is good enough for us to work with machines instead of humans. As we can see based on my interaction, they don't need to be perfect - they just need to get the job done.

In my interaction above, we were able to schedule a meeting and the fact "she" didn't understand that I had told her Thursday was open had no real consequence on the overall role "she" was playing. The meeting eventually got booked and that was that.

Ironically, I realized that I had already come across Amy at the DLD Conference in NY that had attend a few weeks earlier.

Dennis Mortensen (Founder of x.ai.), describes the challenge of setting up meeting and how this technology can solve the problem (profanity alert!):

His talk starts 5m47s:

As Dennis mentions, it's a very basic problem but at the same time it's so complicated. Specifically, the challenge with dealing with politeness: it's hard for AI to parse through this and understand the substantive facts that pertain to setting up the meeting. If we take a look at my response, we can see the challenges first hand:

• When I said I was out of town that the AI had to understand that meant I am not available. 
• I did not include Wednesday as a date that was possible so that implies that I'm also not available that day.
• When I stated I was open on Thursday, I meant I was available all day. 

So what does this mean for jobs? Are accountants going to be replaced by Amy one day?

It's actually shows the level of complexity involved in the most basic of human interactions and how much more complex it would be to train AI in terms of doing even the most basic of auditing procedures - at least for now. 

Dennis actually made a good point about this in the Q&A portion of the discussion as it relates to jobs. The other presenter noted how he sees massive displacement as a result of AI; specifically in the truck driving industry. Dennis, on the other hand, was a bit more optimistic. He noted that what tools like his will do is essentially give assistants to people who don't have assistants. For example, the vendor we were dealing likely wouldn't have hired an assistant to help book appointments. 

And I think that's where auditors and accountants need to actually see how AI assistants, like Amy Ingram, can help with automating those mundane tasks that none of likes to do.  

Virtual Personal Assistants: How far will they go? Part 1

Gartner in a recent press release gave some predictions around "virtual personal assistants".

What are virtual personal assistants or VPAs?

Currently, they are the not-so-perfect voice-activated software that accompanies our mobile devices - Apple has Siri, Microsoft has Cortana and Google has Google Now

On the latest Google phone, Pixel, they have Google Assistant:


Although only available for limited release, the video is actually a good summary of the promise of VPAs: the software that will help us coordinate our lives through our-ever-so-central-to-our-lives smartphones.

And that takes us back to how important these VPAs will become. According to Gartner, within two years 20% of all interactions with our smartphones will be through VPAs.

The press release from the research giant also noted some interesting stats on how frequently people are using Siri and Google Now.

In the UK/US, 54% of people surveyed used Siri in the last 3 months. With respect to Google Now, 41% have used it in the UK and 48% have used the service in the US (in the last 3 months). They also noted that they will move from simple tasks (e.g. setting alarms) to more complicated things such as executing transactions.

By 2020, Gartner predicts that VPAs combined with machine learning, IoT, biometrics and other technologies will enable 2 billion devices to operate without a touch interface.

How far can this go?

When I was thinking about writing this post, I thought about my first interaction with an artificial intelligent assistant.  However, before going there I thought it would be first interesting to go back to the movie "Her".

I saw the movie on the plane on one of the business trips that I took.

The movie is about the ultimate stage of, well, virtual personal assistants.

As noted in the trailer below, the "OS" is something that exists on the mobile device but acts as a central management point that brings a persons data together. In the movie, the OS (voiced by Scarlett Johansson) has a real personality that in a sense accompanies the protagonist, played by Joaquin Phoenix, everywhere. The movie goes a bit crazy as they apparently start "dating".

On a side note, I thought the movie was interesting as it speaks to how technology has filled the void in the life of the atomized individual. The story shows how the protagonist has had a bad breakup and turns to this OS for substitute companionship.

Sure this is far-fetched.

But how many times have we left a real conversation with a real loved one only to get to the virtual world of our phones? Of course, it's not some fake person but it's not difficult to see how we could switch the artificial world of VPAs because we have become accustomed to interacting with this endless streams of notifications.

The other part of the movie that I found interesting was how the mobile device is so nondescript. For someone like myself, smartphones have always had this novelty. But in the movie it's a not anything exciting to look at it. In a sense, what's more important is the actual OS running the device. As Gartner predicts, what becomes more important is the "touch-free interaction" between the OS and Joaquin - and the device disappears into the background.

Only time will tell how far this technology go. But I think it's fairly easy to see how such VPAs will become more entrenched in our lives the more "human" they become.

Hacking reading: Is there a better way?

Came across Google's latest use of machine-learning: making "e-comic books" more readable.

One of the challenges of reading such fine literature on a mobile device is the small print that is within the bubbles.

Google's solution? Bubble Zoom.

As per Ars Technica:

"Google is tackling this problem the way it seems to be tackling every problem lately: with machine learning. Google has taught its army of computers to detect the speech bubbles in comic books, allowing you to zoom in on them with just a tap. The bubbles lift off the page and get bigger without affecting the underlying image. This lets you see the entire page while still reading the text. Google calls the feature "Bubble Zoom.""

Here are a couple of screenshots that show how it works:

For those that want to try this out on their Android device, you can download some free preview titles on the Google Play store.

Of course the obvious point, as mentioned by Ars Technica above, is that machine learning is being by Google and others to solve such interesting problems. The entire DC and Marvel comic book library has the Bubble Zoom feature enabled, which shows the power of machine learning to essentially reconfigure a massive amount of content.

The other point worth noting is how this technology fundamentally alters the way we consume text.

We have different channels, video, podcasts, and audio-books and can access books digitally but plain old reading has not changed that much. Zoom Bubble attempts to do that by building interactivity into the traditionally static medium of comic books.

To be honest I was surprised when I polled my IT Audit and Innovation class in January 2016 to see really none of them had shifted to e-books. They still rather have the physical copy, highlight and take notes.

That being said, a lot of credit should be given to Amazon for trying to go a long way to make it comfortable to read and enable you to access the content from multiple devices.

I’ve been experimenting with e-reading the Kindle, Samsung Note 4, iPad and iPhone.

The reader of choice depends on how you absorb information. If you want to savor your book and slowly digest, then Kindle is the easiest on the eyes

However, for us reading-for-productivity, i.e. if you are the type of person that needs to highlight and then extract notes, for the purposes presenting, researching, or blogging, then I think the Note 4 or the iPad is best. 

With the Kindle ecosystem, when you highlight the text (regardless of the device) its captured and stored on the cloud and then you can always access your notes there. For example, I highlighted the text below on my mobile device and it appears in the cloud (i.e. by logging into https://kindle.amazon.com/your_highlights): 

“ although GitHub is currently optimized for developers, similar platforms will eventually emerge for lawyers, doctors, publicists and other professionals. The platform has already been extended into enterprise software development with a successful paid business model, and can or soon will be used by governments, non-profits and educational institutions. GitHub charges users a monthly subscription—ranging from $7 to $200—to store programming source code. Andreessen Horowitz, one of the world’s leading venture capital firms, recently invested $100 million in GitHub. It was the VC firm’s largest investment round ever.”

In terms of iPad/iPhone versus Note 4, the Note 4 you can use its stylus to highlight text but you have to take an extra step to select the colour you want (you have 3 colors to choose from). In contrast, with iPad/iPhone you can just pick the colour right from the menu that pop-ups when you select a piece of text. The iPad’s larger form factor is also good for scan-reading. Of course the advantage for me on the Note 4/iPhone is that it’s my mobile device so it eliminates the need to carry around extra device.

One way to improve the readability is to change the background colour to Sepia from white. I have found it to be easier on the eyes.

The ability to move through multiple devices shows the brilliance of Amazon harnessing the power of open, mobile, cloud and seamless connectivity across platforms.

They could have gone the closed approach, i.e. you have to read their e-books off of their device. But by being open it enables the consumer to consume content in a manner that works for us. Microsoft has gone down this road as well with Office. I originally thought this was a bad idea but later recanted.

On a more critical note, as I have blogged before Amazon offers to US customers ONLY the ability to sync their audiobooks (Audible is owned by Amazon)  to their kindle ebooks for certain titles. It would be nice if this feature was also available out of the US.

What I've found to be a productivity hack, is to listen to the audio book on my Audible app at 2-3X speeds while driving around. I've self-diagnosed myself as an audiolearner it does help to learn things and get a good grasp of the topic. Such an approach can also help get the overall context of the material being presented. The Audible app enables you to bookmark, so that is a good way to track what you have to read up later.

Then I go through the Kindle e-book and highlight the parts I want to extract off the cloud. You can do this on the commute in or just waiting in line. The trick here is not to re-read the book but just extract those pieces of texts you wanted to focus on while listening to the audiobook. Moving the bookmarks from the audiobook to the e-books acts like a secondary review ensuring you've extracted all the content that's relevant to your presentation, research, blog post, etc. Alternatively, moving from the audiobook to the e-book may be the way you actually digest the content if you are more of a visual/text oriented learner. I personally need to do this with numbers and dates.

Finally, if you want to move the highlighted text off the cloud, try this to move the content to Evernote.

Although I think there are better ways out there to hack reading, I think the Amazon ecosystem goes a long way to get us there. One day, I hope, they will bring Immersion Reading to the world :)

Hey CPA: What's this machine learning all about?

Harvard Business Review online published a great article summarizing how the machine learning, and analytics works in a business context. It uses an illustrative set of decision trees to show how in a cable business scenario (something we can all relate to) and then ends with the following graphic on how a hypothetical algorithm would determine whether a customer would continue with the cable subscription or join the cord cutter crowd.

It's a great illustration of HBR breaks down these "glob" words like, machine learning, algorithm, etc., and transforms them into digestible concepts. Furthermore, and I would say more importantly, it illustrates a rising level of expectation of technology knowledge for client facing business professionals, like accountants and consultants. 

In a previous post, I had noted the following with respect to a couple of WSJ articles on information security and malware :  

"WSJ is a good litmus test of what the business press can expect a business professional to know about IT security, and technology related controls more generally. 

Although not explicitly mentioned in the first article, one of the key trends that has raised the level knowledge required for the average business professional is consumerization: individual have access to technology, such as tablets, smartphones, networks, etc. that were once the sole domain of corporate IT. Consequently, now the average business professional needs to increase their knowledge of IT and IT risks to avoid a virus or getting hacked. For example, I heard a couple of guys at the gym discussing the risks of downloading illegal movies: getting targeted by regulators and malware infection. "

We could also apply this to the HBR article: it too is a good litmus test of the level of competence that a Canadian CPA should know about leading edge topics such as machine learning and its relationship with analytics. 

We should recognize that the technology and security concepts discussed in these articles represent the minimum standard of what is expected from an accountant.  If we as a profession want to achieve the vision of being the  "globally respected business and accounting designation" [emphasis mine], then we must go above and beyond this minimum and surpass expectations of our clients, employers and business community at large.