Just finished attending a meet up sponsored by www.blockchaincanada.org. The room was filled to capacity - illustrating the excitement around the disruptive technology right here in Toronto.
Alan Wunsche, co-founder of the organization, walked through the road map of the non- profit organization, which looks at multiple initiatives to raise the profile of blockchain in Canada and prevent the departure of luminaries in the field, such as Vitalik Buterin (Alan wasn't so specific, but I decided to read between the lines).
The organization is driven by community, and the thoughtware to be produced by the group will rely on the volunteers. For example, I volunteered for the accounting working group to explore Canadian initiatives around triple entry bookkeeping and alternative accounting models.
For those interested, in a deeper dive into blockchain checkout their blockchain hackathon this weekend.
Interesting article on Forbes by Thomas Davenport on Big Data. The articles discusses how various government, including Canadian Public Safety Operations Organization (CanOps), have used big data tools for "situational awareness". These systems draw on myriad sources of data to give users (e.g. law enforcement) the information they need to deal with a particular situation.
Here are a few points that I thought were worth noting:
Government is making strides in big data: We often think of Amazon, Google and other tech-giants as key users of this data. However, as the Davenport points out that the government is using this technology to assist with decision making. However, whether this is something that should be celebrated remains to be seen (see predictive policing below)
Privacy versus Value trade-off: He talks about how CanOps use of MASAS, the Multi-Agency Situational Awareness System, is limited by the filtering of sensitive information: "breadth of MASAS is noble, but it seems to limit its value. For example, as the CanOps website notes, because agencies are reticent to share sensitive information with other agencies, all the information shared was non-sensitive (i.e. not terribly useful)." It seems that this continues to be a theme that we had noted in back a couple years when discussing a similar trade-off the companies face when dealing with big data. As I noted in this post:
"privacy policies require the user to consent to a specific uses of data at the time they sign up for the service. This means future big data analytics are essentially limited by what uses the user agreed upon sign-up. However, corporations in their drive to maximize profits will ultimately make privacy policies so loose (i.e. to cover secondary uses) that the user essentially has to give up all their privacy in order to use the service."
Consequently, there still needs to be a solution as to how privacy can be respected but organizations can use the data they have collected to make better decisions.
Predictive Policing is an emerging reality: The sci-fi movie, Minority Report, paints a future where law enforcement arrests people before they commit crimes.
That future seems to be well on its. Davenport mentions how "predictive policing" was introduced in 2014 to the NYPD. He also mentions how much data is being collected by the police:
"It collects and analyzes data from sensors—including 9,000 closed circuit TV cameras, 500 license plate readers with over 2 billion plate reads, 600 fixed and mobile radiation and chemical sensors, and a network of ShotSpotter audio gunshot detectors covering 24 square miles—as well as 54 million 911 calls from citizens. The system also can draw from NYPD crime records, including 100 million summonses."
Andrew Guthrie Ferguson, Law professor UDC David A. Clarke School of Law, wrote an article on how that predictive policing is something that has not be really sorted in out in terms of legality. He notes:
"The open question is whether this big-data information combined with predictive technologies will create “predictive reasonable suspicion“ undermining Fourth Amendment protections in ways quite similar to the stop-and-frisk practices challenged in federal court.
In two law review articles I have detailed the distorting effects of predictive policing and big data on the Fourth Amendment and have come to the conclusion that insufficient attention has been given at the front end to these constitutional questions. New York has the chance now to address these issues before the adoption of the technology and should be encouraged by the same civil libertarians and ordinary citizens who challenged the stop and frisk policies."
His commentary highlights another limitation: big data predictions are biased based on how the data is collected. The stop and frisk policies he refers to disproportionately targeted minorities. Furthermore, policing is more focused on poor, black/hispanic neighbourhoods. Michelle Alexander documents in her book, The New Jim Crow, how this happens:
"Alexander explains how the criminal justice system functions as a new system of racial control by targeting black men through the “War on Drugs.” The Anti-Drug Abuse Act of 1986, for example, included far more severe punishment for distribution of crack (associated with blacks) than powder cocaine (associated with whites). Civil penalties, such as not being able to live in public housing and not being able to get student loans, have been added to the already harsh prison sentences."
Consequently, if the data by law enforcement is used to predict crime that essentially the targeting of minorities will continue to target such groups given that it is based on biased data.
Technology often is seen to be a silver bullet for problems. However, we need to keep in mind that it is vulnerable to the human element that makes it. Given Microsoft's recent faux pas of accidentally allowing an AI avatar to become a Nazi, it is something that should actively be considered in the systems that are built to police and govern.
"What we know is that the vehicle was on a divided highway with Autopilot engaged when a tractor trailer drove across the highway perpendicular to the Model S. Neither Autopilot nor the driver noticed the white side of the tractor trailer against a brightly lit sky, so the brake was not applied. The high ride height of the trailer combined with its positioning across the road and the extremely rare circumstances of the impact caused the Model S to pass under the trailer, with the bottom of the trailer impacting the windshield of the Model S. Had the Model S impacted the front or rear of the trailer, even at high speed, its advanced crash safety system would likely have prevented serious injury as it has in numerous other similar incidents."
So analyzing the incident both the auto-pilot system and the driver didn't recognize the truck in the distance. With the fear of robots, it's easy to fan the flames of "robotophobia" and quickly blame the robots. For example, some could claim the driver would have been vigilant had he not had such an auto-pilot system. But this is mere speculation and hard to prove.
A couple other things should be noted when evaluating this incident.
Robot record is superior to the human only record: As Tesla has noted, that the car has driven safely 130 million miles, contrasting this to a fatality every 94 million miles driven in the US and 60 million world wide. In other words, the robot record is superior to the human only record.
What about the times the robot has saved people from crashes? The other problem is how do you balance this bad news with the good news that never gets reported. This refers to the time the autopilot acted to save the human beings from crashes. It similar to investments in information security that save the company from countless malware incidents. However, because nothing happens no one really notices the value of technology. Similarly, we're not able to balance the "fear, uncertainty, doubt" associated with this incident with all the times the auto-pilot system actually avoided a crash.
The incident does point out, however, a bigger looming issue of how human beings and machines work together. Despite the caveats, people are already eager to let the auto-pilot drive them around. And really why not? Commuting is giant waste of time and we could be more productive while letting the computer drive us around.
Nicholas Carr explores this issue in his latest book The Glass Cage. In the book, he explore how the more reliant we are on a technology, the less connected we are to the world. For example, by moving from manual to automatic transmission, in his opinion, driving is less fun. He also points out how airplane pilots are really just babysitting the computer that actually flies the plane. The trouble occurs when there is a crisis situation where the pilots are unable to handle the situation because they have lost their ability to actually fly planes.
To be fair, this was not the issue in the Tesla crash - it's way too soon to say that the individual driving the car was overly dependent on the car. However, it is plausible to see how this will occur quite quickly if someone like Google were to offer driverless cars to the massed (as I noted in this post). However, the government didn't made it mandatory to learn to ride a horse - just in case all the cars stopped worked. So I doubt they will force us to learn to drive cars, just in case the autonomous cars stop driving.
Amid this chaos, some investment strategies fared well - thanks to the use of robots.
According to the WSJ article, "Who Made Money in the Brexit Chaos? Machines, Not Humans", machines were immune to the fear, uncertainty and doubt that plagued markets (italics, highlight mine):
"This fund category, sometimes called commodity trading advisors, or CTAs, uses customized trading algorithms to spot market trends and place bets on futures and other derivatives. Most of the models didn’t factor in British election polls, bookmakers’ odds or the political-tea leaf reading that swayed other investors looking for an edge. In the weeks leading up to the Brexit vote, the trading models at many of these firms adopted a defensive pose. They favored high-quality government bonds, gold and safer currencies like the yen, while mostly avoiding riskier bets like oil and emerging markets.
That positioning paid off after Brexit caused the pound and more volatile assets to plunge as Thursday’s results came in. Société Générale’s CTA Index gained 1.5% on Friday. AQR Capital Management LLC, Fort and Welton Investments Partners LLC were among the big gainers... A key to CTAs’ success, their managers say, is that their models can tune out noise around market moving events—like an election or crucial economic data—that are important to investors but can be difficult to accurately forecast."
The article also quoted Lara Magnusen, portfolio strategist for Altegris’s main fund, who said (bold mine):
"Our models aren’t going to be affected by the same sentiments a human would be"
I thought that this was interesting as it illustrates how the machines can be seen as a way to provide an anchor when people are getting caught up in an emotional frenzy. Think of the implications for the world of audit and assurance, where professional judgement are made to determine what accounts, transactions, etc. are risky and should be tested. Imagine an audit algorithm that can be as an independent monitor that vets judgments of the audit professional - in a "race with a machine" scenario (for more on this idea see the Ted Talk below with MIT professor Eric Brynjolfsson). This could potentially improve auditor judgment, stakeholder confidence and audit quality.
Initially, I think this would be a way for audit firms to reduce the level of uncertainty associated with reviews from the PCAOB, CPAB and their equivalents in other jurisdictions. This would especially be the case if such audit oversight bodies would "bless" such algorithms and be able to ensure that the firms applied such judgment consistently, e.g. by having access to the "audit logs" produce by such programs.
The next - and more controversial step - would be to argue that independence rules can be relaxed in light of such automated oversight. To be honest I think there's a low likelihood of such an idea making traction with regulators in the near future, given that Europe has sought to require mandatory rotations of auditing firms. But it is something that should at least be contemplated, especially when automation becomes commonplace and attitudes may change towards how algorithms can play nicely with humans.
As noted in this Business Insider article, Goldman Sachs (GS) published a report on blockchain that identifies a number of scenarios where the technology can save billions. The BI article extracted the following use cases from the 88 page report:
Better authentication of individuals partaking in the sharing economy: Leveraging the "smart identity" functionality of the blockchain, peer-to-peer sharing businesses sites (e.g. Airbnb) can give both the customer (e.g. the renter) and the supplier (e.g. the home owner) greater assurance that the customer is really who they say they are. The GS report also links the identity to smart contracts that facilitates automated performance based payments
Accounting system for renewable energy power generation: Where individual homeowners are generating wind or solar power, the blockchain can be the natural accounting system to manage the "debits and credits" transferred back and forth between the energy producer and the network. It also enables payment transfers as well.
Reducing back end administration for title insurance: The actual GS report notes how the vast majority of the cost associated with title insurance can be reduced by about 30% using blockchain to manage the underlying property records. Other interesting notes is that they attribute part of the decline to improved actuarial risk calculations due to "greater historical transparency".
Improving accuracy and timeliness of trading various securities: The financial services industry usage of the blockchain is quite straightforward - replace the chaotic world of spreadsheet accounting with the streamlined world of blockchain - it is a database technology after all. NASDAQ use of Linq was featured in this DUPress article and can also be found here. The GS report goes into much more granular detail as to the different scenarios on how the back-end system can be improved resulting in less verification issues and improved trading times.
Better authentication of customers aka KYC (Know-Your-Customer): As noted in the BI article, "Like with the Airbnb example, Goldman envisions identity data stored on a blockchain that could help finance firms easily and quickly check new customers as part of "know your customer" regulation — a bit like a digital passport."
I went through the long report and extracted the following accounting automation insights:
Blockchains can reduce spreadsheet funk. Sharing spreadsheets has become the norm in the financial world for being a flexible way to send quantitative information along with context and some formulas here and there; it's how we auditors often get data from the client when asking for a breakdown of an account we are looking into as a part of the audit. It's also error prone. Blockchain, in a sense, is a "napster-esque" way of sharing financial information that ensures a common data structure between the sender and the receiver thereby eliminating the manual verification/handling of spreadsheets (see pages 3 and 10).
Blockchain can enhance "assurance", where it's not feasible for auditors to do so. On page 16 the report discusses the role of smart identities in assisting the sharing economy. It talks about how required digitally signed user reviews will have greater data integrity as it could reduce the risk of self-inflated reviews. Although people rely on such reviews to buy books, rent hotels, etc., there is risk of fraud where the seller will inflate reviews or pay people to do so. However, with a blockchain enabled smart identity there's a higher level of assurance that the end-user can place on the reviews as it harder for the site owner to fake the reviews due to fact the review is digitally signed. Of course no audit firm would have audited such reviews. But I think that's the point: the blockchain technology fills an an assurance need that auditors couldn't, simply because the delivery of such a service wouldn't be profitable.
Blockchain automation will reduce the need for back-end clerical staff (aka accountants): When looking at the application of blockchain to the title insurance industry (see pages 33-39), it notes how 75% of the industry premiums relate to headcount costs. GS puts the reduction in clerical staff by 30% and a 20% reduction in variable expenses (e.g. commissions, marketing, etc.). Blockchain - without AI enhancements - will automate accounting work as part of the automating knowledge work trend. This is of course more clerical tasks, but blockchain will likely result in less headcount within the finance department.
Role for third party assurance reports in a permissioned blockchain: The consensus mechanism in the permissioned blockchain is quite different than it's public counterpart, which relies on the proof of work (POW). (See the Khan Academy video, below for the POW and the blockchain section in this post) This is not the case for permissioned system which require the consortium who set-up the blockchain to determine how they will work with each other. This could require auditors to provide assurance over the implementation of blockchain similar to what the SOC report does for cloud computing companies. The report discusses how (see page 29) on how the blockchain will enable "Smart Grid Blockchains" to essentially acts as the record keeping and payment system of energy exchanged by the household owner who has windmills, solar panels to the power grid. But how do we ensure that this being calculated properly? Well, that's where the Processing Integrity Principle of the SOC3 assurance report comes in. It could provide assurance that the blockchain-accounting-payment system is processing the data in complete, accurate and timely manner.
Greater visibility, means greater opportunity for audit analytics. One area of cost savings associated with a blockchain enabled title insurance industry is that actuaries will be better able to assess risk because of "greater historical transparency and immutability into the property registration system" (see page 38). Consequently, where a material amount of transactions are on a blockchain auditors will have (1) easier access to the data (not a trivial matter by any means!) and (2) can run better analytics to identify irregular transactions and (3) enable better ways to assess estimates.
Value versus hourly billing: In a number of the use cases identified (e.g. title insurance, settling equities, KYC; see pages 38, 51, 75) noted how the gains (read: headcount reduction) from blockchain enabled automation are expected to be passed on to the customer. Why is this relevant to audit? Audit firms could be expected to hand over automation windfalls to the client and further reduce fees. On the one hand, the more automated the audit, the potentially less fees that audit will capture. On the other hand, regulators may want the auditors to do more with the budget that has been freed up. So the revenue, profitability of highly automated audits will depend on how the regulators re-draw scope in light of such advances.
Despite having the reputation as a great-vampire-squid, the GS report is quite useful for those working in the blockchain space in identifying the potential for this exponential technology.
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
Normally when I discuss artificial intelligence on the impact of work, it's in the context of the automation of accounting and auditing work (A3W). However, a story that's been circulating for past few days in my Google alerts is the story of the movie Sunspring. Unlike most movies it's not written by human being, but rather it is written by a computer.
Ars Technica, who hosted the online debut of the movie, noted that the script was "authored by a recurrent neural network called long short-term memory, or LSTM for short. At least, that's what we'd call it. The AI named itself Benjamin."
The movie is really odd to put it nicely. However, it does comes across as one of those art movies that (also) don't make any sense. The song in the movie is also generated by the machine.
However, this is not the first time that algorithms have been trained to be artists. Chris Steiner in his book "Automate This: How Algorithms Came to Rule Our World" notes that Emmy, an algorithm, "produced orchestral pieces so impressive that some music scholars failed to identify them as the work of a machine". In a piece he authored for the Wall Street Journal he notes "analyzing only the script, an algorithm from Epagogix, a risk-management firm that caters to the entertainment industry, predicts box office grosses. Epagogix broke into the business when a major studio allowed the firm to analyze script data for nine yet-to-be released films. In six of the nine cases, its predictions were spot-on. Algorithms have since become an essential tool in Hollywood."
If the chaotic world of creative works can be automated by algorithms, then I think the predictable, routine world of debits and credits can't be too far behind.
US households owe $12 trillion in debt of which $436 billion is 90+ days past due.
Companies who discharge the debt sell it for pennies on the dollar to a growing number of companies that specializes in debt buying.
One company, Encore Capital, notes that in 1 in 5 Americans owes or has owed them money.
Debt that's been paid "come back to life", which is affectionately known as Zombie debt.
There was some controversy, however, about who he worked with to write-off the debt (they noted their grievances here, to which John responded here) and the value of the debt. On the latter count, is it really fair to criticize an act of charity that improved the lives of approximately 9,000 people?
Nothing good happens in Excel.
But the segment which is most relevant to us is when he starts talking about how the information is actually sold. It is sold on spreadsheets. Oliver gets quite dramatic as he shares his his phobia of Excel and notes how "nothing good happens in Excel". He also explains that the spreadsheets are sold "as is"; meaning that the seller does not guaranty accuracy of the information related to the debt contracts being sold.
And that's where the jokes stops.
In the segment, he has footage from interviews with Jake Halpern, who wrote "Bad Paper: Chasing Debt from Wall Street to the Underworld". The book follows the life of a debt buyer of Aaron Siegel, who is born to a rich family in Buffalo, New York. He takes an array of characters, including his Brandon, who is an ex-con who does that gritty part of work of finding the debt, ensuring its good and collecting on it.
What caught my attention as I was going through book, is that it gave a bit more detail to what John Oliver mentioned about the banks selling the paper "as is". Halpern notes on page 58 of his book (see below for the link to the book), that when Washington Mutual sold Joanna and Theresa's debt to Aaron, the credits awarded against their accounts that were not reflected in the spreadsheet that was given to the debt buyer.
And that's how accounting errors can ruin lives.
When you read the life stories of these two ladies it's heart wrenching to think that a few lines on an Excel spreadsheet could have a detrimental impact on their lives. Some would cynically say this is over dramatic and try to find reason to blame Joanna and Theresa falling into this problem. But I don't think that's fair. When you read the lives of these people, it's clear that they were affected by factors beyond their control. It's really this broken system of debt collection that is responsible for them failing to get the debt relief that they were owed.
The way accounting systems and spreadsheets are designed and operated can have real impact on real people. As an accountant myself, I often wondered what value is accounting in the grand scheme of things. But as Halpern's story illustrates the accountants, bookkeepers, etc. had a real impact on the livesof these two women.
No one is saying that accountants have the same impact on the lives of people the way a cancer specialist does. But at the same time a few a lines on Excel spreadsheet could be the difference between perpetual anxiety and a good nights sleep.
