Blockchain and Discrete Process Manufacturing

I just arrived home from this evening from the first week working with my new client, the Digital Trust team at T-Systems, and my mind is spinning with ideas.

Over the coming months, I am going to be working with this team to define a set of product offerings that make the most of the investment and capabilities that T-Systems has already developed in the DLT space. Naturally, a good portion of my time this first week was spent beginning to get an understanding of what the team had already accomplished. The quality of thought that this team has displayed in its solutioning impressed me in a number of ways, but what I find myself thinking about the most is the work they’ve put into a DLT use case I have not spent any time exploring until now: discrete manufacturing process control.

I have to admit that the idea of driving a manufacturing assembly line with real-time decisions made by blockchain smart contracts did not strike me as intuitively obvious. Assembly lines might manufacture goods for different clients, but typically on behalf of a single manufacturing enterprise; I failed to understand how having DLT drive assembly decisions would add value. Could you accomplish the same thing less expensively with a single, non-distributed application? Where was the potential for world-changing transformation that we see with other DLT use cases?

The way that the Digital Trust team chose to illustrate that potential was nothing short of brilliant. Being part of Deutsche Telekom affords access to a wide variety of capabilities, and in this instance the team really made the most of that: they constructed a working model assembly line which actually took instructions in real time from a blockchain smart contract.

This is the model assembly line they built. It is a mini manufacturing rig that assembles and packages model widgets with embedded RFID tags. There are several assembly options, and the configuration of those options is driven by an Ethereum smart contract.

This working model illustrated — in a way that PowerPoint slides never could, how truly revolutionary the potential is. When we think of disintermediation, we tend to think of brokerages, professional services like accountancy or law, and other facilitators of the information economy. Until I saw this machine, it never occurred to me that manufacturing itself could be disintermediated. Startups and medium enterprises would be able to share expensive and specialized equipment they could otherwise never afford. Smaller medical facilities could do likewise with the most modern diagnostic and imaging tools. Entire factories could tool themselves to meet the needs of separate, small-batch units of demand in real time.

I will need to think about this some more, do some research, and validate ideas with people who have a lot more experience in this area than I do. But the possibility for change here is vast, and my intuition says its full potential won’t be realized until someone can articulate a business and process model that could never have existed without this breakthrough.

That someone is may be the Digital Trust team at T-Systems. They certainly have a head start, and this is only one of several really interesting ideas they have cooking. This team is looking way beyond the standard payment, token, or supply chain solutions that everyone else is building. I am really excited about working with them on this.

Finserv Experts signs DLT Solutioning agreement with T-Systems

We are pleased to announce that Finserv Experts has reached an agreement to work with T-Systems in developing DLT solutions. T-Systems is a global ICT provider and is part of the Deutsche Telekom global enterprise.  The development team within T-Systems has made significant investments in blockchain, and have developed an impressive set of capabilities.  Finserv Experts will be working with this team and with the T-Systems product marketing team to develop a set of targeted product offerings that deliver measurable business benefits to T-Systems customers.

We are honored and thrilled to have T-Systems as our newest client!

Finserv Experts gives evidence in Parliament

22 January 2018

Finserv Experts managing director Areiel Wolanow gave evidence this evening for the All-Party Parliamentary Group on Blockchain.  Areiel spoke on the transformative potential for blockchain in the insurance industry, and cited work carried out by Finserv Experts consultants here in Indonesia, Kenya, and here in the London Market.

Here is a written transcript of his remarks…


Speaker Introduction

Areiel Wolanow is the Managing Director of Finserv Experts, an independent consultancy that provides advisory and delivery services for technology-enabled business transformation.  Areiel has been working with blockchain solutions since 2014.  At IBM, Areiel was asked to create their blockchain services practice for the ASEAN region; in this role he was responsible for selling and delivering IBM’s very first blockchain consulting project:  a trade finance prototype for HSBC and Bank of America.  Areiel has advised central banks and financial regulators around the world on blockchain adoption, and most recently led the delivery of a working DLT prototype for Lloyd’s of London and the London insurance market.  He is currently engaged in designing a mobile-based microinsurance solution for natural disasters in Indonesia


Main Content

In many industries, blockchain is thought of first and foremost as a tool for disintermediation.  While this is also true to some extent in the insurance industry, significant disintermediation has already taken place in our lifetimes, well before blockchain was invented.  Thirty years ago, retail brokers were the primary distribution channel for motor, home, and life insurance.  Today, retail brokers are almost non-existent, aggregators and direct consumer search have been proven to meet the needs of insurance buyers at a much lower cost than brokers were able to meet.

Nevertheless, blockchain has the potential to be a major disruptor for how insurance is delivered in several key ways.  Blockchain provenance solutions, for instance, can provide assurance that insured goods are genuine, reducing the incidence of fraudulent claims and thereby the overall cost of insurance delivery.  Blockchain market solutions, similar to the prototype we developed for Lloyd’s, can significantly lower the cost of placing large, complex insurance contracts, as well as the cost of administering claims.  Currently, when an insurance contract is placed in the London market, many parties need to create their own record of the same contract in their individual IT systems.  This leads to huge amounts of work in re-keying and reconciliation; it also is the root cause of many errors or gaps in quality.  By enabling multiple parties to share a single version of the truth, blockchain solutions can at the same time reduce cost and increase the quality of delivery.  This allows insurance professionals to spend far less time on administrative tasks and more time selling and directly supporting clients.  The magnitude of these cost savings is dramatic – there are several solid empirical evidence sets of administrative cost reductions of 80%-90%

But perhaps the single greatest change to insurance that blockchain can make possible is in the realm of microinsurance.  Insurance has never been available to most people in the world because the cost of administering a £200 policy is not that different from the cost of administering a £2,000,000 policy.  For instance, Finserv Experts is right now working on a project that will make natural disaster insurance available to people living in Indonesia.  In the event of a disaster, such as the recent tsunamis, the policy would provide a minimum of one year’s income.  It would be paid parametrically – no claim investigation would be required.  The policy could be taken out via mobile phone, and the claim paid via mobile phone as well.  The ability of a community to recover from these disasters will be completely transformed.


Guidance for lawmakers

There are two pieces of guidance I would like to offer

First, blockchain is one of the few areas in which businesses would welcome more regulation rather than less.  The benefits of DLT are increasingly well understood, but innovators are still wary of taking risks because of the level of uncertainty about whether or not a given business model will ultimately be allowable.  In the fintech space, the UK already has a great reputation for forward-thinking policy.  By providing a clear framework under which blockchain-based business can operate, lawmakers can both capitalize on and further differentiate the UK’s thought leadership.

Second, and far more important, I would like to invite our lawmakers to consider directly sponsoring microinsurance initiatives here in the UK.  The benefits of making insurance of all kinds available and affordable to everyone in the UK.  The benefits go way beyond mitigation of risk.  Affordable insurance will make it possible for far more people to buy homes, start businesses, and otherwise engage in more productive, successful, and fulfilling lives.


Areiel Wolanow interviewed by Plexus

To further secure their brand leadership in the DLT resourcing space, Plexus Resource Solutions has launched a regular interview series featuring UK thought leaders in the DLT space. I am honored to have been selected for their inaugural edition. Many thanks to Colin for a thoughtful and enjoyable conversation; I look forward to continuing our dialog. 

You can read the interview here:


Auditing Smart Contracts

Every major breach involving cryptocurrency, blockchain, and ICO solutions to date can be traced to poorly implemented smart contracts, so it is welcome that Blockgeeks have had a go at establishing guidelines for smart contract auditing.  You can find it here:

Their approach looks well thought out and quite thorough, but focuses mainly on the code base. While a technology audit is obviously a necessary component of smart contract assurance, a full audit will require two additional components. The first is a business audit, in which the executives of the business using the smart contract are provided assurance that the contract does what they think it does in business terms. The second is a legal audit; since DLT solutions by definition involve multiple parties, a smart contract is also a real contract, and DLT solution owners will need legal confirmation that their contracts are binding, lawful, and enforceable.

The last thing we need is for this three-part approach to be formally adopted by an internationally recognized standards body, so that enterprise architects around the world Kudos to Blockgeeks for taking this on.

photo sourced from the original Blockgeeks article

How the UK is addressing some of the biggest challenges to blockchain adoption

In Singapore, a pair of global banks built a blockchain trade finance prototype for letter of credit origination in 2016. The actual build of the prototype was estimated to take 15 weeks, and successfully finished on time. But agreeing the intellectual property rights, as well as other aspects of the contract, took more than twice as long as the actual build. And despite the success of the prototype, obtaining architectural approval to move forward with a production solution took over a year and a half.

This account serves as an excellent illustration of the main challenges facing the adoption of blockchain, and distributed ledger technologies (DLT) in general, as a new standard for transaction accounting. More and more, people and enterprises around the world are coming to understand DLT’s transformative potential. Enterprises, governments, and system integrators alike are discovering through experimentation: not only are DLT solutions relatively straightforward to develop, it is becoming increasingly common that they cost less to build than their technological predecessors. And the market for technologists with genuine DLT delivery experience is growing rapidly. But despite it becoming increasingly straightforward to conceive of, and then build, DLT solutions, the actual adoption of those solutions is progressing far slower than anticipated.

Probably the single greatest obstacle causing this slowdown is a lack of standards against which potential DLT solutions can be compared. The importance of this gap becomes readily apparent when one considers how enterprise applications are typically released into production. One of the main jobs of any enterprise architect is to validate that a proposed solution is safe to use; this check is made against a large and growing set of dimensions: security, data privacy, regulatory compliance, availability, business continuity, and so on. In providing this validation, the architect must rely upon more then their opinion, no matter how expert they might be. She must demonstrate and record evidence that the solution meets a set of relevant industry or governmental standards in each of these domains.

To provide an illustrative example, let us consider one of these domains: data privacy. One of the things an architect must prove before agreeing to release a solution into production is that the solution provides adequate protection for the data of both the enterprise and its customers. And one of the most common prevalent global standards for data privacy is that an application must never make customer data available outside the enterprise’s firewall. Since a blockchain application will, by definition, have that data physically resident in every node of that blockchain’s network – and many of those nodes are likely to be owned by the enterprise’s direct competitors – how are architects able to certify this application is safe for production? The simple answer is: they can’t. Through cryptography and sound design, the blockchain application might actually do abetter job of protecting customer data than its traditional predecessor, but without an independent and evidence-driven basis for approving it to release, it won’t matter. This is what blocked the advancement of the trade finance prototype, and this is what is blocking the advancements of numerous, otherwise excellent, DLT solutions around the world.

Seeking a way out of the impasse

In tracking the growth and adoption of many innovations over history, one is often tempted to think of governments and regulators as impediments to progress. But here in the UK, the initiative to break this impasse may be coming from lawmakers themselves. Two members of parliament, Rt Hons Grant Shapps MP and Damien Moor MP have established an All Party Parliamentary Group on Blockchain to better inform parliamentarians about blockchain/distributed ledger technologies and their impact on industry and civil society. The APPG on Blockchain will particularly focus on engagement with the government to ensure the right regulations and policies are put in place. The group had its first formal meeting in January of this year, and its mission statement reads as follows:

The mission of the APPG Blockchain is to ensure that industry and society benefit from the full potential of blockchain and other DLTs, making the UK a leader in Blockchain/DLT innovation and implementation. We bring evidence, use cases, and future policy scenarios while considering industry and societal implications as well as environmental opportunities.

 The APPG Blockchain agenda and work plan

The APPG Blockchain’s Advisory Board and Expert Advisors Group include experts from industry, academia, public policy, and of course the lawmakers themselves. Its work is facilitated by an innovation and policy hub called Big Innovation Centre, who is also the Secretariat for the All Party Parliamentary Group on AI, and whose team has a long track record of facilitating public policy work on transformative innovations.

The initial roadmap for the APPG Blockchain covers 2018 and 2019. The first goal is to build within the Advisory Board a shared understanding of blockchain’s capabilities, limitations, and transformative potential. This goal will be accomplished primarily through a series of evidence sessions held at Parliament itself, in which the experts provide lawmakers a briefing in their respective areas of expertise. The first three of these evidence sessions have already been conducted; more are planned through the balance of 2018.

Having achieved this shared understanding, the APPG will then construct a roadmap for building pragmatic solutions to achieve the group’s mission. These solutions are likely to encompass such diverse topics as policy, regulation, industry practice, and infrastructure development. Should the APPG Blockchain achieve its mission, it may end up providing exactly the kind of clarity and guidance that DLT needs to achieve its full potential, both in the UK and globally.

More information on the APPG Blockchain and its work can be found at

Invitation: How AI and machine learning are driving financial inclusion around the world.

One of the biggest concerns about the emerging commercial viability of AI is the possibility that a huge number of people might lose their jobs to software and robots that can do those jobs better and/or cheaper. This is a valid concern, and we obviously need to address the significant ways in which AI is likely to disrupt people’s lives. But this is only half the story; AI also has a huge role in job creation, particularly in emerging economies. To be clear, this is not potential future impact; this change is happening already, and has been under way for several years now. Perhaps the most surprising thing is that, despite the growing dominance of online retailers, this phenomenal job growth is being fueled primarily by the success of small local businesses. And AI is playing a major role in making that success happen.

I’d like to invite you to join me at IP Expo, at London’s ExCeL centre on 3 October 2018, for a session where we will explore this phenomenon in detail. Our exploration will be evidence-based: we will be focusing on proven research and delivered results from some real-world implementations in various countries around the world, including Indonesia, China, Kenya, and others. The discussion will include:

  • An understanding of the outsized effect that small business growth has on job creation
  • An understanding of the biggest problems facing small businesses in both emerging and mature economies
  • A detailed look at how AI is already being used to address those challenges
  • Some of the truly surprising discoveries we have made along the way, some of which we never would have uncovered any other way

You can sign up for IP Expo at:

The basic conference pass is free until 2 October, and there is an early bird discount on the full access pass as well.

I look forward to seeing you there,


SEC announces most ICO’s are securities, most cryptocurrencies are not

The chairman of the US Securities Exchange Commission has announced that most #ICO tokens are securities, while most #cryptocurrency tokens are not.  Obviously, not everyone will agree with this decision, and it does make floating an ICO more burdensome, but this is still a hugely positive development.  It provides welcome and long overdue regulatory clarity, and should go a good way towards removing a significant element of risk from many investment decisions.  Let’s hope a framework for applying this guidance follows shortly.

Understanding the Potential of Mechanism Design

Understanding potential of mechanism design

People have been trying to model how decisions get made for a long time.  In western history, the earliest person we know of to try and model decision making was Aristotle in his studies on ethics, probably around 330BC; similar but independent models were put forward by mathematicians and philosophers in China and India. Arab scholars – who through their trade networks had access to all three bodies of work – made significant advances during medieval times, and their work was picked up by Italian and French scholars during the Renaissance.

But since the end of WWII, the study of decision making has truly exploded, giving rise to a growing number of methods and disciplines: operations research, game theory, neural networks, and so on. There is even something called the theory of elevators, which attempts to model why it is that elevators in tall office buildings tend to clump up.  Yes, this really is a thing.

This explosion of decision study was motivated in large part by the advent of digital computing, first because computers made it possible to model and simulate in ways never previously available, and second because of the desire to build systems that were capable of making high quality decisions.

One of the newest of these decision-making disciplines is something called mechanism design (MD).  It closely resembles game theory, from which it derives.  The difference is that, with game theory, you start with a decision-making model, or “mechanism” and study how people (or computers) make decisions that may or may not lead to desirable outcomes.  In MD, you start with the desired outcome, and then try to come up with a mechanism that will get people or computers to make decisions that lead to that outcome.

Why is mechanism design important?

There are many possible applications for mechanism design, and it’s something senior business leaders are paying attention to.  The idea for writing this article, for instance, was sparked by a query from Shirine Khoury-Haq, COO of the Lloyd’s of London Insurance market, who is actively interested in how Lloyd’s might leverage MD to do a better job of delivering value to its customers.

But to me, the greatest potential for mechanism design lies in the core delivery method for business transformation, regardless of industry.  Any time you change the way a business operates, you need to not only specify the new process you want a business to follow, but KPI’s to measure that process, and people to be accountable for its performance.  Coming up with KPI’s to motivate the decisions you want people to make is notoriously difficult, human intellect is notoriously bad at it, and the results of making a mistake can be notoriously disastrous. Consider the following real-world examples from recent history:

  • When Netflix launched its new streaming business, they quite sensibly wanted to get people to migrate to this new fulfilment channel, as it was far cheaper to operate than their traditional DVD-by-post channel.  So to set the right incentive, their head of sales was given a huge incentive target to get people to switch to the new platform.  To motivate his customers to switch, he hiked the price of their DVD-by-post business by something like 60% in 2011.  The exec in question blew out his numbers and enjoyed a massive bonus, but Netflix as a whole lost a million subscribers in that quarter, and their stock dropped 70% in that year.  Obviously they have since recovered, but it was a huge disruption.
  • At Bank of America, I think this was in also 2011, a new head of retail banking was incentivized to improve the per-person profitability of the mass market segment, so quite straightforwardly he instituted a monthly $5 charge for any mass market customer who used their ATM cards in that month.  The individual profitability of the segment soared, and the executive exceeded his target, but the bank as a whole lost hundreds of thousands of customers, about 30% of their value, and created a brand image they still haven’t recovered from
  • At a leading bank in Mexico, the team responsible for selling credit cards was, unsurprisingly, compensated based on the number of cards they sold, and had solid record of exceeding their targets for several years running, but the bank was losing money on their card business.  Unable to figure out why, they commissioned an investigation that uncovered that over 30% of the cards that were sold were never even activated, and the bank was spending an enormous amount of money producing and issuing cards that never got used


All these examples share a common chain of events.  An enterprise defines a strategic objective, and designates an executive to be accountable for achieving that objective. The executive implements a set of changes to how the enterprise sells and delivers, and these changes are hugely successful in achieving the objective the executive was asked to deliver.  Then, a series of unintended and unanticipated consequences produce adverse results far in excess of any positive benefit that might be realized from achieving the original objective.  It is worth thinking about this in the overall context of business transformation.  The generally accepted method for business transformation tends to be as follows:

  • Define a set of outcomes, typically measured by KPI’s, you would like a business to achieve
  • Make someone accountable for achieving those KPI’s
  • Understand the capabilities a business would need to achieve the KPI’s
  • Identify the changes in business process and/or technology needed in order to bridge the gap between current capabilities and required capabilities
  • Implement the identified changes

The important thing to note is that defining the desired outcomes is the very first step.  This means, if you get it wrong (as people did in our real-world examples), not only do you have to deal with huge set of negative consequences, but you have just spent a huge amount of time, money, and opportunity cost to achieve those consequences.  Also, it is worth noting that these mistakes were not made by idiots; the decision makers in the above examples were all seasoned executives who had reached their positions via a long track record of successful business decisions.  Designing good KPI’s is really hard, and has always been more art than science.

How can MD help?

So how would MD help us do a better job of avoiding real-world business disasters like the one above?  There are already ways of studying and modelling decisions.  Game theory, for instance, starts with the decision model itself, and then tries to predict how rational actors are likely to behave.  Behavioral economics looks at how human behavior tends to deviate from rational decision-making in predictable and repeatable ways.  MD builds on these disciplines, but rather than using the decision model as the starting point, MD starts with the desired outcome, and then tries to come up with a model in which rational (or to borrow the title of a book by Dan Ariely, predictably irrational) people are likely to make decisions that result in the desired outcome.

Of course, as our real-world examples show, the ability to come up with a great decision model is useless, or perhaps actively harmful, if we pick the wrong objective to begin with.  This is where MD really shows its potential.  Because it starts with the desired outcome rather than with the constraints of a known model, it should be possible to build MD-based test harnesses.  The idea is that using a combination of simulation and machine learning, it should be possible to actually test and identify at least some of these KPI train wrecks before implementing them in the wild.  Preventing even one of these would make the investment worthwhile.

What next?

This is a new idea; it will need to be tested and prototyped before anyone would consider trying it in the context of a real enterprise transformation.  The next step will be to identify a suitable prototype use case; one in which we could deliver measurable value from trying this idea in a tightly focused and even more tightly scoped way.

This is something that I and my company will be exploring further. If you know of someplace where this approach might prove useful, or if you would like to work with us to explore the idea further, please don’t hesitate to get in touch.

-photo courtesy of Digital Trends