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How Basis for Buying Decisions Is Changing

Building a business around speed and convenience is nothing new. Fast food drive-thrus, cell phones and FedEx overnight delivery services were just some of the predecessors to today’s Ubers, apps and same-day Amazon orders. But in most of these cases, purchase decisions were based upon simple factors — “I’m hungry,” or “We need delivery of a legal document,” or “Of course it would be nice to be able to make a call from my car.”

There were other services for which people understood that immediacy wasn’t an option. Many financial decisions took time. If you wanted to earn a little extra interest by using a certificate of deposit instead of savings, you would have to wait months or years for maturity. Securing life insurance was a multi-week (sometimes multi-month) underwriting process. Applying for a home loan with multiple credit and background checks took time. For the most part, people accepted these elongated processes and delays with resigned and good-natured patience. This was life. Important decisions required time, not only in the preparation, but also in the education and execution. Two hours with a life insurance agent would allow you to learn about all of the products available and understand their complexity, and it would help the agent to fit products to your needs. You valued the time spent learning, understanding and choosing based on the trusted relationship with your agent.

The convergence of generational shifts and technological advancement created a new mindset that rewrote expectations and priorities for many. Patience is no longer always considered a virtue. Insurance relationships are no longer always valued. Time-crunched people seek time-saving services. Value is seen in immediacy, uniqueness and ease.

See also: Innovation: a Need for ‘Patient Urgency’  

Enter the new generation of insurance companies redefining the insurance engagement. Lemonade, TROV, Slice, Haven Life and others who are redefining speed and value to a new generation of buyers … are placing traditional, existing insurers on notice.  From purchasing a policy in less than 10 minutes to paying a claim in less than three seconds … speed and simplicity are the new competitive levers.

Out of necessity, this has changed an insurer’s view of competition. Insurers used to know their competitors. They understood their distinctive value propositions. They debated on what were the real product differentiators. Insurers understood the reach of their agents, their geographic limitations and the customer and agent loyalty they could count on because of their excellent service.

While all of these factors still guide insurance operations, the competitive landscape has shifted to different factors critical to acquiring and retaining customers. Insurers are feebly groping for just a tiny bit of space in consumer minds —enough to plant the seed of need and just a little more to water the plant into engagement and completing a transaction — because today’s consumer isn’t going to listen well enough to grasp distinctive details. He or she is looking for an easy and quick fit.

A 2015 study of Canadian consumers estimated that the average attention span had dropped to 8 seconds from 12 seconds in 2000, driven at least in part by consumers’ constant connections through digital devices.

Need. Purchase. Done. Happy.

A 2012 Pew survey of technology experts predicted what is now coming true, “the impact of networked living on today’s young will drive them to thirst for instant gratification, settle for quick choices and lack patience….trends are leading to a future in which most people are shallow consumers of information.”

Only five years later, insurers are feeling the impact.

A key reason many of the new, innovative companies are appealing to consumers and small and medium-sized businesses (SMBs) is because they simplify and remove some of the cognitive effort required to make decisions about insurance. In his book, Thinking, Fast and Slow, the Nobel Prize-winning behavioral economist Daniel Kahneman described human decision making and thinking as a two-part system. Greatly simplified, System 1 thinking produces quick (i.e. instantaneous and sub-conscious) reflexive, automatic decisions based on instinct and past experiences. These are “gut” reactions. System 2 thinking is slow, deliberate, reason-based and requires cognitive effort.

In general, most of the decisions we make each day are through System 1, which can be both good and bad; good because it increases the speed and efficiency of decision making, and because in most instances the outcomes are acceptable. However, not all outcomes are good, and many could have been improved had System 2 thinking been engaged. The problem with System 2 is that it takes effort, and humans naturally try to minimize effort.

See also: Insurtech: Unstoppable Momentum  

So, a traditionally complex industry is intersecting with a cognitive culture that is mentally trying to simplify, reduce effort and be more intuitive. This has consequences for decisions throughout the customer’s journey with an insurance company. Good decisions about complex issues like insurance should be based on System 2 thinking. However, during the research and buying processes, the cognitive effort to do so can lead many people to choose other paths like seeking shortcuts to in-depth research and analysis or delaying a decision altogether.

In a recent report, Future Trends 2017: The Shift Gains Momentum, Majesco examined how impatience is driving a shift in behavior that is causing insurers to look at the anatomy of decisions. What behaviors are relevant to purchase? To renewals? To service? How can insurers still provide risk protection to individuals who won’t take the time to learn about complex products? We’ve drawn some of these insights out of the report for consideration here.

For one thing, insurers clearly recognize that the trends affecting them are far broader and bigger than the insurance industry. Businesses and startups across all industries are capitalizing on the lucrative opportunity afforded by meeting the ever-increasing demands for speed and simplicity made possible by technology and re-imagined business processes. Amazon Prime, Netflix, Spotify, Uber/Lyft, ApplePay/Samsung Pay, Rocket Mortgage (Quicken Loans), Twitter, Instagram and other technology-based businesses represent contemporary offerings that have simplified the customer journey.

Retailers such as Walmart, Best Buy, Staples, Amazon and even eBay are testing same-day delivery for items ordered online. Simplifying a customer’s entire journey with a company by making it “easy to do business with” is more critical than ever for insurers.

What is the good news in the world of impatience? Insurers are quickly finding ways to counter the disparity between the need for speed and the need for good decisions. They are also using a bit of psychology to positively influence decisions, and they are buying back some brain space with techniques that both inform and engage.

In Part 2 of this series, we will look at these techniques as well as product adaptation, framework preparation and planning for transformation that will meet the demand for quick decisions. For more in-depth information on behavioral insurance impact, download the Future Trends 2017 report today.

Is It Time to End the Annual Policy?

Is there anything more emblematic of the largely antediluvian state of the insurance market than the concept of the annual policy?

Admittedly, there is a certain convenience for the customer only having to worry about his or her insurance once a year and for the insurer only having to process the relevant paperwork every 12 months. And for years, of course, insurer returns were almost entirely built off the back of the profits to be gained from investing up-front premiums — as in, not on serving the customer.

But haven’t things moved on?

Certainly, today’s low interest-rate environment (nothing lasts forever, but it is hard to envision what might shift this dynamic in the short- to medium-term) means insurers have to focus far more on correctly pricing risk rather than on yield arbitrage. And our increasingly technically sophisticated and connected world surely raises questions regarding whether market practices essentially inherited from the 17th century are still appropriate.

See also: The Most Effective Insurance Policy  

Consider the humble motor policy.

At the risk of gross over-simplification, the market is currently centered on selling an annual policy with pricing essentially dictated by a number of important risk factors (such as the value of the vehicle, driver age, anticipated annual mileage, where the car is kept at night, previous convictions, etc.) But the price you pay reflects little about the environmental factors that really drive risk when you are behind — or not behind — the wheel. While the industry is far more sophisticated than it was 20 years ago, pricing is typically set according to statistical averages for whatever broad risk grouping you happen to fall into — with all the imperfections this implies — to the inevitable detriment of lower-risk drivers within each of those categories.

Today’s technology — of which telematics is a pretty rudimentary example — enables a different approach.

Rather than an annual policy, why not specify a daily standing charge that reflects the true risk to the insurer of the car sitting in your garage, say, where the risk of accident or personal injury or theft is extremely low? Think of it as a standing charge.

However, as soon as you take your car out of the garage, an additional cost would apply — think of the Uber surcharge — but this additional cost would vary depending on the time of day or the driving conditions. Taking the car out in the rain or when it is icy would be more expensive than when the sun is shining. Driving in the middle of the night when there is less traffic is inherently less risky than battling your way through rush hour. Far fewer accidents occur on the motorway per mile driven than on crowded urban streets. And geo-location software could confirm whether you are, in fact, parking your car at home at night as you have claimed or whether you have left it for a few nights at the airport while you fly off to Rome or Miami for the weekend.

This approach starts to suggest some interesting outcomes. First, it allows insurance companies to price far more accurately for the actual risk they face, based not on relatively blunt risk category averages but for each individual driver down to each specific trip. Second, it ensures that drivers pay the true costs of the risk they represent rather than subsidizing their higher-risk fellow drivers. For most drivers, this is likely to result in a lower price because the average is hugely skewed by the tail risk.

Perhaps most interestingly, the approach also enables the driver to better understand the relationship between how and when she drives and the cost of insurance; thus, it potentially acts as a spur for drivers to moderate or modulate their behaviors accordingly, which is where you start to drive some real alignment of interests and benefits for both drivers and insurers.

The good news is that the necessary technology essentially exists today in the mobile device you are probably reading this post on. The various data feeds — weather, time of day, geo-location, etc. — are already there. Even today, my iPhone varies the time at which I need to leave one meeting to make the next depending on traffic and weather.

Of course, as with any radical change to established operating models, there are some important practical issues to be overcome in terms of the customer interface, billing and mechanisms through which customers would physically agree to a surcharge before, during or after a journey, etc. Insurers would need to work through the change in their cash flow profile and may also feel that the complexity of some of the larger risk classes continues to favor an annual cycle. And the impenetrability of pricing in the mobile phone industry, which marches under the banner of increased customer transparency and choice, stands as a stark warning to how the best intentions can lead to utter confusion.

See also: Insurance Disruption? Evolution Is Better  

There are some broader potential social concerns, too, around third party tracking of your movements. There are also implications for higher-risk drivers who find themselves priced out of the market where, today, their costs are essentially subsidized by the rest (particularly in circumstances where the constraints of people’s day-to-day lives and jobs may not give them a huge amount of choice regarding the conditions under which they choose to drive), although that particular genie is probably halfway out of the bottle, anyway.

The question, as ever, is whether the inevitable change will come from the incumbents that are weighed down by their legacy positions or from some new entrant that has the freedom of movement but lacks the scale, brand and capital to compete in a meaningful way.

One thing is certain: In a world where one of the world’s largest hotel companies doesn’t own any rooms (Airbnb), one of the world’s largest car hire companies doesn’t own any cars (Uber) and one of the world’s largest retailers doesn’t own any merchandise (Ebay), the insurance industry’s continued attachment to the annual policy feels increasingly like a relic from a bygone age of quill and parchment.

Competing in an Age of Data Symmetry (Pt. 3)

The Internet is a mirror of sorts — a data mirror. Right now, it is a sort of fuzzy data mirror, but the pictures grow clearer as the available data grows. Soon, the image of an insurers’ customer service, pricing and claims experiences will grow crisp. How will it happen? How will insurers respond and remain competitive?

In Part 1 and Part 2 of our series, we discussed data symmetry — the leveling of the playing field that is currently happening because insurers are gaining access to many of the same streams of data. The trend runs in contrast to data asymmetry, which allowed insurers to comfortably differentiate themselves by being good at the analysis of their own in-house data. As insurers use more and more of the same data and some of the same analytics tools and methodologies, they will find themselves in a pool of sameness. Differentiation by price and service will be less about introspective analysis and more about finding and delivering on real brand promises.

So, in today’s blog we are crossing a bridge of sorts. We are going to look at how the consumer will achieve data symmetry by gaining a clear view of the real insurer.

See also: Data Science: Methods Matter

Changes in scrutiny are causing data symmetry

Insurers are the subjects of constant scrutiny. The NAIC, the Federal Insurance Office, the Department of Labor, every state and every consumer protection organization have an interest in watching insurers. Yet all of that scrutiny may pale in comparison to the impact of the coming wave of individual consumer scrutiny.

Consumers are using ratings, stars, comments and shopping patterns to give instant feedback to all service providers. Feedback (real experience) is a sales tool for aggregators and retailers. It is a reason for consumers to choose particular channels or pipelines. Amazon and eBay don’t have to build trust for any one product. They only have to facilitate feedback and let the products, services and suppliers speak for themselves.

These outside views are the result of symmetrical data availability. Prospects are now able to compare any product or service, including insurance, with greater real data, including both sources that are verifiable and those that contain unstructured data. Consumers may look at an insurer through the lens of an insurance aggregator, such as Insure.com or The Zebra, or through simple search terms such as “worst auto claims experience in my entire life.” They may also witness an insurance interaction through their relationships with friends on social media.

Reputation analysis will hold tremendous power to validate or invalidate brand promises. Does the insurer make it simple to file a claim? Does it have a poor track record in paying claims? Are renewal rates much higher or lower than competitors’? These bits of information weren’t as public in the past. Today, they are common and easy to find.

See also: What Comes After Big Data?

Data symmetry’s effect on the insurer will operate much like a looking glass. The insurer will begin to see itself, not as it has attempted to portray its brand, but as it is perceived during real interactions. This will lead some insurers to make course corrections.

The good news is that data symmetry will supply healthy doses of reality. Insurers will know and understand their competition. They will have an unprecedented, timely idea about what customers really want and how well they are supplying it. If they are prepared for the coming levels of data symmetry, insurers will also be able to make agile shifts and meaningful steps toward selling insurance through many different channels. Many of these details are still food for our insurance visions. One thing is certain, however. Data and analytics will continue to unlock the secrets of market positioning to keep insurers competitive. Data’s relevance to business decisions will always grow.

The Real Powerhouses in Silicon Valley

One of the most important lessons that Silicon Valley learned, that gives it a strategic advantage, is to think bigger than products and business models: It builds platforms.

The fastest-growing and most disruptive powerhouses in history — Google, Amazon, Uber, AirBnb and eBay—aren’t focused on selling products; they are building platforms.

The trend goes beyond tech.  Companies such as Walmart, Nike, John Deere, and GE are also building platforms for their industries. John Deere, for example, is building a hub for agricultural products.

Platforms are becoming increasingly important as all information becomes digitized; as everything becomes an information technology and entire industries get disrupted.

A platform isn’t a new concept; it is simply a way of building something that is open and inclusive and has a strategic focus. Think of the difference between a roadside store and a shopping center. The mall has many advantages in size and scale, and every store benefits from the marketing and promotion done by others.

See Also: Pursue Innovation or Transformation

They share infrastructure and costs. The mall owner could have tried to have it all by building one big store, but it would have missed out on the opportunities to collect rent from everyone and benefit from the diverse crowds that the tenants attract.

Platform businesses bring together producers and consumers in high-value exchanges in which the chief assets are information and interactions. These interactions are the creators of value, the sources of competitive advantage.

The power of platforms is explained in a new book, Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You, by Geoffrey Parker, Marshall Van Alstyne and Sangeet Choudary. The authors illustrate how Apple became the most profitable player in the mobile space with the iPhone by leveraging platforms.

As recently as 2007, Nokia, Samsung, Motorola, Sony Ericsson and LG collectively controlled 90% of the industry’s global profits. And then came the iPhone with its beautiful design and marketplaces — iTunes and the App store. With these, by 2015, the iPhone had grabbed 92% of global profits and left the others in the dust.

Nokia Shutterstock

Nokia and the others had classic strategic advantages that should have protected them: strong product differentiation, trusted brands, leading operating systems, excellent logistics, protective regulation, huge R&D budgets and massive scale.

But Apple imagined the iPhone and iOS as more than a product or a conduit for services. They were a way to connect participants in two-sided markets — app developers on one side and app users on the other.

These generated value for both groups and allowed Apple to charge a tax on each transaction. As the number of developers increased, so did the number of users. This created the “network effect” — a process in which the value snowballs as more production attracts more consumption and more consumption leads to more production.

By January 2015. the company’s App Store offered 1.4 million apps and had cumulatively generated $25 billion for developers.

Just as malls have linked consumers and merchants, newspapers have long linked subscribers and advertisers. What has changed is that technology has reduced the need to own infrastructure and assets and made it significantly cheaper to build and scale digital platforms.

Traditional businesses, called “pipelines” by Parker, Van Alstyne and Choudary, create value by controlling a linear series of processes. The inputs at one end of the value chain, materials provided by suppliers, undergo a series of transformations to make them worth more.

pipes

Apple’s handset business was a classic pipeline, but when combined with the App Store, the marketplace that connects developers with users, it became a platform. As a platform, it grew exponentially because of the network effects.

The authors say that the move from pipeline to platform involves three key shifts:

  1. From resource control to orchestration. In the pipeline world, the key assets are tangible — such as mines and real estate. With platforms, the value is in the intellectual property and community. The network generates the ideas and data — the most valuable of all assets in the digital economy.
  2. From internal optimization to external interaction. Pipeline businesses achieve efficiency by optimizing labor and processes. With platforms, the key is to facilitate greater interactions between producers and consumers. To improve effectiveness and efficiency, you must optimize the ecosystem itself.
  3. From the individual to the ecosystem. Rather than focusing on the value of a single customer as traditional businesses do, in the platform world it is all about expanding the total value of an expanding ecosystem in a circular, iterative and feedback-driven process. This means that the metrics for measuring success must themselves change.

But not every industry is ripe for platforms because the underlying technologies and regulations may not be there yet.

See Also: InsurTech: Golden Opportunity to Innovate

In a paper in Harvard Business Review on “transitional business platforms,” Kellogg School of Management professor Robert Wolcott illustrates the problems that Netflix founder Reed Hastings had in 1997 in building a platform.

Hastings had always wanted to provide on-demand video, but the technology infrastructure just wasn’t there when he needed it. So he started by building a DVDs-by-mail business — while he plotted a long-term strategy for today’s platform.

According to Wolcott, Uber has a strategic intent of providing self-driving cars, but while the technology evolves it is managing with human drivers. It has built a platform that enables rapid evolution as technologies, consumer behaviors and regulations change.

Building platforms requires a vision, but does not require predicting the future. What you need is to understand the opportunity to build the mall instead of the store and be flexible in how you get there. Remember that business models now triumph products—and platforms triumph business models.

parties

In Third Parties We (Mis)trust?

Technology is transforming trust. Never before has there been a time when it’s been easier to start a distant geographical relationship. With a credible website and reasonable products or services, people are prepared to learn about companies half a world away and enter into commerce with them.

Society is changing radically when people find themselves trusting people with whom they’ve had no experience, e.g. on eBay or Facebook, more than with banks they’ve dealt with their whole lives.

Mutual distributed ledgers pose a threat to the trust relationship in financial services.

The History of Trust

Trust leverages a history of relationships to extend credit and benefit of the doubt to someone. Trust is about much more than money; it’s about human relationships, obligations and experiences and about anticipating what other people will do.

In risky environments, trust enables cooperation and permits voluntary participation in mutually beneficial transactions that are otherwise costly to enforce or cannot be enforced by third parties. By taking a risk on trust, we increase the amount of cooperation throughout society while simultaneously reducing the costs, unless we are wronged.

Trust is not a simple concept, nor is it necessarily an unmitigated good, but trust is the stock-in-trade of financial services. In reality, financial services trade on mistrust. If people trusted each other on transactions, many financial services might be redundant.

People use trusted third parties in many roles in finance, for settlement, as custodians, as payment providers, as poolers of risk. Trusted third parties perform three roles:

  • validate – confirming the existence of something to be traded and membership of the trading community;
  • safeguard – preventing duplicate transactions, i.e. someone selling the same thing twice or “double-spending”;
  • preserve – holding the history of transactions to help analysis and oversight, and in the event of disputes.

A ledger is a book, file or other record of financial transactions. People have used various technologies for ledgers over the centuries. The Sumerians used clay cuneiform tablets. Medieval folk split tally sticks. In the modern era, the implementation of choice for a ledger is a central database, found in all modern accounting systems. In many situations, each business keeps its own central database with all its own transactions in it, and these systems are reconciled, often manually and at great expense if something goes wrong.

But in cases where many parties interact and need to keep track of complex sets of transactions they have traditionally found that creating a centralized ledger is helpful. A centralized transaction ledger needs a trusted third party who makes the entries (validates), prevents double counting or double spending (safeguards) and holds the transaction histories (preserves). Over the ages, centralized ledgers are found in registries (land, shipping, tax), exchanges (stocks, bonds) or libraries (index and borrowing records), just to give a few examples.

The latest technological approach to all of this is the distributed ledger (aka blockchain aka distributed consensus ledger aka the mutual distributed ledger, or MDL, the term we’ll stick to here). To understand the concept, it helps to look back over the story of its development:

 1960/’70s: Databases

The current database paradigm began around 1970 with the invention of the relational model, and the widespread adoption of magnetic tape for record-keeping. Society runs on these tools to this day, even though some important things are hard to represent using them. Trusted third parties work well on databases, but correctly recording remote transactions can be problematic.

One approach to remote transactions is to connect machines and work out the lumps as you go. But when data leaves one database and crosses an organizational boundary, problems start. For Organization A, the contents of Database A are operational reality, true until proven otherwise. But for Organization B, the message from A is a statement of opinion. Orders sit as “maybe” until payment is made, and is cleared past the last possible chargeback: This tentative quality is always attached to data from the outside.

1980/’90s: Networks

Ubiquitous computer networking came of age two decades after the database revolution, starting with protocols like email and hitting its full flowering with the invention of the World Wide Web in the early 1990s. The network continues to get smarter, faster and cheaper, as well as more ubiquitous – and it is starting to show up in devices like our lightbulbs under names like the Internet of Things. While machines can now talk to each other, the systems that help us run our lives do not yet connect in joined-up ways.

Although in theory information could just flow from one database to another with your permission, in practice the technical costs of connecting databases are huge. Worse, we go back to paper and metaphors from the age of paper because we cannot get the connection software right. All too often, the computer is simply a way to fill out forms: a high-tech paper simulator. It is nearly impossible to get two large entities to share our information between them on our behalf.

Of course, there are attempts to clarify this mess – to introduce standards and code reusability to help streamline business interoperability. You can choose from EDI, XMI-EDI, JSON, SOAP, XML-RPC, JSON-RPC, WSDL and half a dozen more standards to “assist” your integration processes. The reason there are so many standards is because none of them finally solved the problem.

Take the problem of scaling collaboration. Say that two of us have paid the up-front costs of collaboration and have achieved seamless technical harmony, and now a third partner joins our union, then a fourth and a fifth … by five partners, we have 13 connections to debug, by 10 partners the number is 45. The cost of collaboration keeps going up for each new partner as they join our network, and the result is small pools of collaboration that just will not grow. This isn’t an abstract problem – this is banking, this is finance, medicine, electrical grids, food supplies and the government.

A common approach to this quadratic quandary is to put somebody in charge, a hub-and-spoke solution. We pick an organization – Visa would be typical – and all agree that we will connect to Visa using its standard interface. Each organization has to get just a single connector right. Visa takes 1% off the top, making sure that everything clears properly.

But while a third party may be trusted, it doesn’t mean it is trustworthy. There are a few problems with this approach, but they can be summarized as “natural monopolies.” Being a hub for others is a license to print money for anybody that achieves incumbent status. Visa gets 1% or more of a very sizeable fraction of the world’s transactions with this game; Swift likewise.

If you ever wonder what the economic upside of the MDL business might be, just have a think about how big that number is across all forms of trusted third parties.

2000/’10s: Mutual Distributed Ledgers

MDL technology securely stores transaction records in multiple locations with no central ownership. MDLs allow groups of people to validate, record and track transactions across a network of decentralized computer systems with varying degrees of control of the ledger. Everyone shares the ledger. The ledger itself is a distributed data structure held in part or in its entirety by each participating computer system. The computer systems follow a common protocol to add transactions. The protocol is distributed using peer-to-peer application architecture. MDLs are not technically new – concurrent and distributed databases have been a research area since at least the 1970s. Z/Yen built its first one in 1995.

Historically, distributed ledgers have suffered from two perceived disadvantages; insecurity and complexity. These two perceptions are changing rapidly because of the growing use of blockchain technology, the MDL of choice for cryptocurrencies. Cryptocurrencies need to:

  • validate – have a trust model for time-stamping transactions by members of the community;
  • safeguard – have a set of rules for sharing data of guaranteed accuracy;
  • preserve – have a common history of transactions.

If faith in the technology’s integrity continues to grow, then MDLs might substitute for two roles of a trusted third party, preventing duplicate transactions and providing a verifiable public record of all transactions. Trust moves from the third party to the technology. Emerging techniques, such as, smart contracts and decentralized autonomous organizations, might in future also permit MDLs to act as automated agents.

A cryptocurrency like bitcoin is an MDL with “mining on top.” The mining substitutes for trust: “proof of work” is simply proof that you have a warehouse of expensive computers working, and the proof is the output of their calculations! Cryptocurrency blockchains do not require a central authority or trusted third party to coordinate interactions, validate transactions or oversee behavior.

However, when the virtual currency is going to be exchanged for real-world assets, we come back to needing trusted third parties to trade ships or houses or automobiles for virtual currency. A big consequence may be that the first role of a trusted third party, validating an asset and identifying community members, becomes the most important. This is why MDLs may challenge the structure of financial services, even though financial services are here to stay.

Boring ledgers meet smart contracts

MDLs and blockchain architecture are essentially protocols that can work as well as hub-and-spoke for getting things done, but without the liability of a trusted third party in the center that might choose to exploit the natural monopoly. Even with smaller trusted third parties, MDLs have some magic properties, the same agreed data on all nodes, “distributed consensus,” rather than passing data around through messages.

In the future, smart contracts can store promises to pay and promises to deliver without having a middleman or exposing people to the risk of fraud. The same logic that secured “currency” in bitcoin can be used to secure little pieces of detached business logic. Smart contracts may automatically move funds in accordance with instructions given long ago, like a will or a futures contract. For pure digital assets there is no counterparty risk because the value to be transferred can be locked into the contract when it is created, and released automatically when the conditions and terms are met: If the contract is clear, then fraud is impossible, because the program actually has real control of the assets involved rather than requiring trustworthy middle-men like ATM machines or car rental agents. Of course, such structures challenge some of our current thinking on liquidity.

Long Finance has a Zen-style koan, “if you have trust I shall give you trust; if you have no trust I shall take it away.” Cryptocurrencies and MDLs are gaining more and more trust. Trust in contractual relationships mediated by machines sounds like science fiction, but the financial sector has profitably adapted to the ATM machine, Visa, Swift, Big Bang, HFT and many other innovations. New ledger technology will enable new kinds of businesses, as reducing the cost of trust and fixing problems allows new kinds of enterprises to be profitable. The speed of adoption of new technology sorts winners from losers.

Make no mistake: The core generation of value has not changed; banks are trusted third parties. The implication, though, is that much more will be spent on identity, such as Anti-Money-Laundering/Know-Your-Customer backed by indemnity, and asset validation, than transaction fees.

A U.S. political T-shirt about terrorists and religion inspires a closing thought: “It’s not that all cheats are trusted third parties; it’s that all trusted third parties are tempted to cheat.” MDLs move some of that trust into technology. And as costs and barriers to trusted third parties fall, expect demand and supply to increase.