GoldmanSachs on Blockchain: Insights into Audit & Accounting Automation

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

Will "data lakes" cure companies info management problems?

Anyone working in the analytics space will know that one of the primary obstacles to performing analytics is the state of the underlying data. One may, naively, expect that organizations would see systems as a strategic asset that requires top attention. The reality is that the state of systems is much to be desired. This phenomenon was actually highlighted in Michael Lewis's latest book: Flash Boys. He notes in this interview, that his investigation into High Frequency Trading (HFT) began with the arrest of Sergey Aleynikov:


Aleynikov was arrested for stealing code from Goldman Sachs related to its HFT platform and it was assumed that he would use this code at his new employers trading department. However, as Lewis explains, the new firm used a totally different code base and the information he took was the equivalent of the notes one keeps in their notebook (Lewis explains that this was the judgement of Aleynikov's programming peers).

So if this is relatively useless information, why was Aleynikov kept in jail for a year?

The theory is that Goldman was afraid that the inadequacy of the HFT platform would be revealed to the world. Yes. Goldman. A bank deemed "too big too fail" - thereby having access to unlimited government funding and bailouts - can't spare the capital to invest in a state of the art trading platform. This was one of the reasons that seems to explain why Goldman ultimately backed the IEX platform that Brad Katsyuma set up to essentially eliminate the advantage that HFT firms have over everyone else.

Give that this is the reality of the technology, it should be no surprise that the promise of data analytics often gets killed due to the inability to get to the data. And this is where data lakes come into the picture.

As noted in this Forbes article, "data lakes" differ from "data warehouses" in that there is no upfront cleansing, sorting and categorization of the data into a specific structure. Instead, the data is stored in "in a massive, easily accessible repository based on the cheap storage that’s available today. Then, when there are questions that need answers, that is the time to organize and sift through the chunks of data that will provide those answers."

Will this then solve the world's data access and cleansing problems? Gartner does not think so.

In an analyst report released last week, Gartner effectively slammed this concept pretty hard. According to Gartner, they noted the following issues with the concept of data lakes:

• No consensus around  what "data lakes" means. As with any new techno-buzzword, each vendor will define data lakes to mean something different. With cloud computing, NIST quickly came on to the scene to define the concept and most commentators at least referred to this definition as a starting point before proposing their own. 
• Skills gap exist preventing the common user from leveraging "data lakes": According to Gartner, data lakes "assumes that all are highly skilled at data manipulation and analysis, as data lakes lack semantic consistency and governed metadata". In other words, companies who save time/effort on the upfront preprocessing of the data, need to equip their users with the necessary skills to leverage the technology. 
• Data lakes remove context, security and other meta data. Without context, users likely cannot use the data carefully. Imagine the difficulty in assessing the dimensions of inventory when some of the subsidiaries use the metric system, where others do not. 

It appears that data lakes in their current iteration are a work in progress when it comes to solving the data accessibility and quality issues that analytics expert face. However, it appears with more robust tools on the front-end that would address the issues, can make data lakes a potential tool to access the organization's disparate data.