Blockchain as a method to enhancing value

Blockchain as a method to enhancing value

Abstract: The trend for increasing speed of transactions and creating greater transparency is driving disintermediation and could be a long-term risk to Arrow as a value added distributor. However, Arrow is in a unique situation as a key player in the technology supply chain.  In addition, vendors require greater assurance that Arrow transactions are aligned with contractual obligations, which increases the cost of doing business.

Emergence of a New Transactional Paradigm

As suppliers look for opportunities to enhance value directly with customers (“DTAM to TAM” or “disintermediation”), a significant opportunity exists on how supply chains operate.  An emerging concept that is gaining popularity in further accelerating disintermediation is blockchain that improves efficiency, provides greater transparency, provides complete assurance of transaction validity.

According to a recent article by PwC, “blockchain may result in a radically different competitive future … where current profit pools are disrupted and redistributed towards owners of highly efficient blockchain platforms.  Blockchain can guarantee the authenticity of transactions across organizations reducing the overhead and cost of transacting business between partners.

In fact, Blockchain is gaining momentum with backing from respected companies and industry groups.  One such example comes from the Wall Street Journal:

Guided by the Linux Foundation—the non-profit that oversees the Linux operating system, the software that serves as the foundation of the modern Internet—a new blockchain-like effort has developed the Hyperledger Project.  Hyperledger’s stated objective is “a collaborative effort created to advance blockchain technology by identifying and addressing important features for a cross-industry open standard for distributed ledgers that can transform the way business transactions are conducted globally.”  The Project includes as its members big tech names including IBM, Intel, and Cisco as well as financial outfits such as JP Morgan, Wells Fargo, the London Stock Exchange, and the DTCC.

Finally, the Wall Street Journal recently reported that IBM is developing a blockchain service that enables companies track items through the supply chain.[1]

What is Blockchain?

Blockchain is a distributed ledger that provides a way for information to be recorded and shared by a community.  Each member maintains its own copy of the information and all members must validate any updates collectively. It has the potential to reduce the cost and complexity of getting things done.  The information could represent transactions, contracts, assets, or identities.[2]

Blockchain is the digital equivalent of public ledgers that were once used in towns to record important events and transactions.

Blockchain enables value to be transferred securely through a distributed network.  Users of the network create transactions which are loosely passed around a network.  The definition of what constitutes a valid transaction is based on the system implementing the block chain.  The block chain is  encrypted to record recent valid transactions into "blocks".   Each block includes the prior timestamp, forming a chain of blocks, with each additional timestamp reinforcing the ones before it.  Each blockchain record is enforced through encryption and hosted on machines working together to check the validity of each transaction.

In order to ensure the integrity of the transaction, each block in the blockchain builds on the blocks that preceded it.  Any alteration of the blockchain by one party is rejected and invalidated by the other members of the network.

How is Blockchain different?

The current transaction paradigms require a central authority (e.g. middle man) to enable the transmission and validation of transactions.  Each entity has its own independent ledger that records its own transactions.  Each party involved in the transaction relies on the central authority as the transactional facilitator.  

While blockchain is seen by some to further propel the elimination of third party intermediaries, a need by many organization will remain to protect market share and proprietary information.  In these instances a semi-private blockchain could be used by a consortium of companies or a “market facilitator.”

Blockchain Use Cases

Blockchain has the flexibility to encode any data that requires validation. For example, a smart contract is an implementation of blockchain where logic can be included in the blockchain that automatically executes transactions based upon pre-set critieria.  The contract is both tamper-proof and self-executing without the need for trusted third parties. Given that transactions are recorded in the distributed ledger in real time, GRNI and other accounting that is currently required because of timing issues could be greatly reduced.

 

Other use cases where blockchain could increase transactional efficiency could include the following:

·       Refunds

·       Warranty claims

·       Returns

·       Payment / wire instructions

·       Software licensing

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[1] http://www.wsj.com/articles/ibm-pushes-blockchain-into-the-supply-chain-1468528824, accessed July 18, 2016

[2] “Blockchain: Democratized trust,” Tech Trends 2016, Deloitte University Press, p.81

Assessing Risk - Start by seeing the business like investors view it

Assessing Risk - Start by seeing the business like investors view it

Frequently internal auditors are looking for the analytics that will better enable them to identify areas of risk.  All too often, however, auditors focus too much effort on information found on financial statements and focus their risk discussions with Finance personnel.  While certainly important, financial statement data does not tell the whole story.  In fact, some of the financial statement data they think is important may actually be of little value in understanding what drives the behavior of business leaders. Internal auditors can spend a lot of time talking about all sorts of risks, which are certainly worth discussing, but without keeping the view in mind, internal auditors are missing important insights.  The following are some recent analyst comments about the performance of a large public company:

• The company is on a growth trajectory, gathering momentum from its positive earnings surprise history and strong fundamentals. It posted positive earnings surprises in three of the last four quarters, with an average positive surprise of 2.86%
• Its top line and bottom line not only came ahead of the respective Zacks Consensus Estimate, but also marked solid year-over-year improvement
• ...[T]he company issued an encouraging fourth-quarter 2016 guidance
• The stock’s long-term earnings per share growth rate is 7.4%
• The company has always had a good amount of debt on its balance sheet.
• Liquidity is low, since cash and liquid assets are just a fraction of its total assets. We think that the company has limited financial flexibility because of its high debt burden, and further increases in debt could make investment in the shares risky.

Comments like those above are great sources to begin to understand the psyche of the business.  Internal auditors then can start ask the following questions:

• How does the business measure performance? 
• How do investors measure performance? 
• How do these measure drive behavior and introduce risk?

As internal auditors get a view into they metrics and other KPIs, they can then now have a list of key risk indicators (KRIs) that are the basis of very basic yet powerful analytics.

Audit Analytics - What is it really and who makes a good data analyst?

Audit Analytics

What is it really and who makes a good data analyst?

Over the course of my career, I have had the opportunity to participate and lead teams where there was a heavy need for analytics across many different client engagements and in many different environments.

At the beginning of my career Computer Assisted Audit Techniques (CAATs) was the term used to describe procedures used to verify the accuracy of certain calculations and reports used by clients that including AR aging and journal entry testing. The tools used then were fairly rudimentary and were constrained by available computing resources. These procedures were primarily used in fairly basic validation procedures.

Fast forward to 2015, and data analytics  or data science are the en vogue terms that connote a broader field than CAATs or other audit analytics.  This has led to a wide variety of interpretations of what analytics are and has been particularly acute for me as I interview candidates for a data analytics position open on the Data Analytics team I lead.

One recent definition of data analytics stated the following:

Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight  (emphasis added).  I would substitute "operations research" for "a certain amount of domain knowledge."

I find that most candidates, whether those professing data analytics experience or those newly minted graduates of data analytics programs, lack sufficient knowledge in statistics, computer programming, databases, and/or data visualization.  I have worked with several very capable and intelligent people; however, they tend to come from computer programming, and to a lesser extent, database administration backgrounds.  These are important and highly desirable skillsets in information technology, but often are not sufficient.

Implicit in the definition is the need to actually do analysis.  Frequently, the idea of performing analytics seems to be producing data in a slightly different form than what is already available.  I see very limited analysis of data to identify trends in data. I believe this is driven by two causes--(1) a lack of understanding by the data analyst of what the data means and (2) the inability to effectively communicate what is discovered.  In addition, in order to have effective analytics in an Internal Audit department, one must have an intellectual curiosity about the area from which the data is pulled. I believe this is where many miss opportunities to add value.

All too often, the analytics teams are put into a silo where the audit teams give them a list of things to produce or perhaps are given a set of analytics "tests."  Rarely, have I seen a team that is truly integrated with members from financial and IT audit and data analytics working together starting with the planning phase of a project to can brainstorm what are the items with the greatest impact to the project and, potentially, of greatest risk / interest in the eyes of management.