[From Brookings Institution´s TechTank blog, 3 June 2015]
One of the foundational notions of digital financial services has been the distinction between payment rails and services running on the rails. This is a logical distinction to make, one easily understood by engineers who tend to think in terms of hierarchies (or stacks) of functionalities, capabilities, and protocols that need to be brought together. But this distinction makes less sense when it is taken to represent a logical temporal sequencing of those layers.
It is not too much of a caricature to portray the argument —and, alas, much common practice— like this: I’ll first build a state-of-the art digital payments platform, and then I’ll secure a great agent network to acquire customers and offer them cash services. Once I have mastered all that, then I’ll focus on bringing new services to delight more of my customers. The result is that research on customer preferences gets postponed, and product design projects are outsourced to external consultants who run innovation projects in a way that is disconnected from the rest of the business.
This mindset is understandable given limited organizational, financial and human resource capabilities. But the problem with such narrow sequencing is that all these elements reinforce each other. Without adequate services (a.k.a. customer proposition), the rails will not bed down (a.k.a. no business case for the provider or the agents). In businesses such as digital payments that exhibit strong network effects, it’s a race to reach a critical mass of users. You need to drive the entire stack to get there, as quickly as possible. Unless, you develop a killer app early on, as M-PESA seems to have done with the send money homeuse case in the Kenyan environment.
It is tough for any organization to advance on all these fronts simultaneously. Only superhero organizations can get this complex job done. I have argued in a previous post that the piece that needs to be parceled off is not the service creation but rather cash management: that can be handled by independently licensed organizations working at arms length from the digital rails-and-products providers.
What are payment rails?
Payment rails are a collection of capabilities that allow value to be passed around digitally. This could include sending money home, paying for a good or a bill, pushing money into my or someone else’s savings account, funding a withdrawal at an agent, or repaying a loan. The first set of capabilities relates to identity: being able to establish you are the rightful owner of the funds in your account, and to designate the intended recipient in a money transfer. The second set of capabilities relates to the accounting or ledger system: keeping track of balances held and owed, and authorizing transactions when there are sufficient funds per the account rules. The third set of capabilities relates to messaging: collecting the necessary transaction details from the payment initiator, conveying that information securely to the authorizing entity, and providing confirmations.
Only the third piece has been transformed by the rise of mobile phones: we now have an increasingly inclusive and ubiquitous real-time messaging fabric. Impressive as that is, this messaging capability is still linked to legacy approaches on identity and accounting. Which is why mobile money is still more an evolution than a revolution in the quest for financial inclusion.
The keepers of the accounts —traditionally, the banks— are, of course, the guardians of the system’s choke points. There is now recognition in financial inclusion circles that to expand access to finance it is not enough to proliferate the world with mobile phones and agents: you need to increase the number and type of account keepers, under the guise of mobile money operators, e-money issuers or payment banks. But that doesn’t change the fundamental dynamics, which is that there still are choke point guardians who need to be convinced that there is a business case in order to invest in marketing to poor people, that there are opportunities to innovate to meet their needs, and that perhaps all players can be better off if only they interoperated. A true transformation would be to open up these ledgers, so anyone can check the validity of any transaction and write them into the ledger.
That’s what crypto-currencies are after: decentralizing the accounting and transaction authorization piece, much in the same way as mobile phones have decentralized the transaction origination piece. Banks seek to protect the integrity of their accounting and authorizations systems —and hence their role as arbiters of financial transactions— by hiding them behind huge IT walls; crypto-currencies such as Bitcoin and Ripple do the opposite: they use sophisticated protocols to create a shared consensus for all to see and use.
The other set of capabilities in the digital rails, identity, is also still in the dark ages. Let me convince you of that through a personal experience. My wallet was stolen recently, and it contained my credit card. I can understand the bank wanting to know my name, but why is the bank announcing my name to the thief by printing it on the credit card, thereby making it easier for him to impersonate me? The reason is, of course, that the bank wants merchants to be able to cross check the name on the card with a piece of customer ID. But as you can imagine, my national ID got stolen along with my credit card, and because of that the thief knows not only my name but also my address. That was an issue because I also kept a key to my house in the wallet. None of this makes sense: why are these “trusted” institutions subverting my sense of personal security, not to mention privacy?
The problem is that the current financial regulatory framework is premised on a direct binding of every transaction to my full legal identity. As David Porteous and I argue in a recent paper, what we need is a more nuanced digital identity system that allows me to present different personas to different identity-requesting entities and choose precisely which attributes of myself get revealed in each case, while still allowing the authorities to trace the identity unequivocally back to me in case I break the law.The much-celebrated success of mobile money has so far really only transformed one third (messaging) of one half (payment rails) of the financial inclusion agenda. We ain’t seen nothin’ yet.
[From Center for Financial Inclusion blog, with David Porteous, 29 April 2015]
In a recent new paper, we look at identity from two opposite but complementary perspectives. The first is a narrow biological perspective, under which identity is associated with one´s uniqueness as an autonomous living organism with a unique genetic makeup. The legal basis for identity tends to be based on this perspective, and leads to questions that focus fundamentally on the confidence with which identities can be asserted and confirmed.
Beyond the definitional question of what it is about one´s person that creates his or her individuality, there is the empirical question of how it can be verified by someone else, such as a financial service provider, through observation. Generally your identity is established indirectly, by demonstrating your command over some proxies (e.g. a signature, a card, a PIN) that have been linked to your identity. The core decision for providers is therefore to determine when they consider that they know someone with good enough probability.
The second perspective is information-based, and views individuals as an irreducibly complex web of personal information and attributes. Digital markets tend to take this view of identity, with customers characterized more in terms of defined attributes, preferences, and transaction histories that can drive customer segmentation than on intrinsic uniqueness. This perspective leads to questions that focus fundamentally on what information about themselves it is legitimate to expect people to reveal to build up their identity, and what information they have the right to keep private.
Why do we so resist websites´ attempts to extract personal information from us? Why do we distrust organizations that appear to squirrel information away and use it to build profiles of us? Oxford University philosopher Luciano Floridi (see chapter 5 in this book) argues that people want to be in control of their personal information because that makes them feel more in control over their own identity. If I were completely transparent and held no secrets, anyone would be as enabled as I am myself to define who I am. By withholding personal information, we feel that we have some control over how we project ourselves. Our management of personal information is central to how we shape our identity in various spheres.
We manage our personal information, and through that our identity, chiefly by compartmentalizing it based on the different roles or personas that we assume in different circumstances. You don´t present yourself in the same way to your employer (you are employee number X and report to Y), your family (you are a stern-but-kind parent), your friends (you want to be seen as fun and easy-going), and indeed at the passport office (you are a neutral, non-suspicious face). Again, you are able to have different personas to the extent that you are able to control which information is disclosed and acted upon in each circumstance, based on what´s most relevant.
These facets of your being can be construed as distinct identities, linked to the same person. Each of these identities is attached to a different —and maybe even contrasting— set of personal attributes. Who we really are is no more and no less than the combination of these distinct identities, but none is necessarily more real than the others. We want to be able to shift easily among them as we go through our daily life.
The two perspectives on identity are profoundly different. You are an unvarying sequence of genes, but also an evolving social being. You are an indivisible entity, and you are a loose accretion of diverse personal traits and roles.
And yet these two perspectives need to be reconciled because they bring together the two key trust aspects or gaps that are at the heart of most identity problems: security (i.e. the confidence with which identity can be established) and privacy (i.e. the sensitivity and sense of personal control with which the information associated with one´s identity is revealed and distributed). These two aspects are often seen as being at odds: to be sure of who you are, I need to know more about you. But when security and privacy are not handled appropriately, trust gaps appear between social entities, between customers and providers, and between citizens and the State. You need to see my full name, exact date of birth, and ID number to let me into a bar or to pick up a parcel at the post office? Really?
The most promising approach to reconciling these diverse notions of identity is building digital systems that (i) permit an unbundling of personal information, and (ii) put users in control of how these unbundled bits of personal information are linked and exposed. Here is how it might work (see this book by David Birch for a fuller explanation):
• My uniqueness can be represented abstractly by a unique number assigned to me by a trusted authority (likely a government entity). Imagine that this number is linked to my biometrics, so that only I can claim to be the person represented by the number.
• I can then link this unique number to different personas (represented by pseudonyms) that I want to assume in different circumstances, for different reasons – say my electric utility, library card, or my Amazon log-in. These entities don´t need to know specifically who I am and what my unique number is, just that I have one so that they can consider me a real person and that a trusted party has this information in case I break the law.
• I can then link specific personal attributes that are relevant to each of these different pseudonyms. For instance, I’d reveal my physical address to the electric utility so that they can service my house and the fact that I am over 18 at a bar, but I may not want the bar to know my address or Amazon to know my age. My personal attributes would be digitally confirmed by a host of different entities that are in a position to verify it.
The first step with dealing with digital identity is, therefore, breaking out from unitary notions of one trusted party knowing everything about me, or one ID card serving all purposes. Users can be in control of their identity, supported by trusted third parties who help them assert digitally whatever personal information they wish to establish. Financial institutions would seem to be well placed to become such trusted third parties, since they acquire substantial amounts of personal information through mandatory Know Your Customer (KYC) requirements, loan and other product applications, and regular customer usage. They could put this customer knowledge at the service of each of their customers, by validating specific attributes that customers wish to have confirmed to others.
[From Fletcher School´s CEME InclusiveFinance blog, 7 April 2015]
We have been handed down a fresh round of evidence on the impact of microcredit from six randomized control trials (RCTs) undertaken by eminent economists (see summary paper here). The results are what most reasonable people would expect: that microcredit is useful for many but by no means transformational. Perhaps the more novel finding is that the impact on average is zero, not negative.
So does this finally settle the debate then, some forty years after Muhammad Yunus’s first (non-random) experimentation with microcredit? Alas, I don’t think so, though it will certainly affect how arguments are pitched henceforth. There are four reasons why evidence from RCTs alone will not close the debate.
First, RCTs are pure empiricism, devoid of any theory. Give a bit of something to some, withhold it from others, and compare. So when the results come out, all one can do —indeed, what the dozens of online commenters on these studies have done— is to interpret them by retrofitting them into one’s own prior theories and mental models. How did the impact happen? Why the differences between men and women, young and old, richer and poorer? Would this work elsewhere in the same way, or if the interest rate were lower? But all we have learned is that six concrete programs are not impactful, on average. The rest is inference and theorizing. We are back to extrapolating on the basis of isolated (now n+6) data points.
Second, RCTs measure impacts on a number of variables: consumption, investment, schooling, female empowerment, job creation, etc. Results are often a mixed bag, so we back up into the problem of how to total up these impact categories to get a net-net impact. There’s now the inevitable suggestion that we should be measuring how people’s happiness is affected, as a sort of grand bottom line. How can we hope to measure ultimate impact if we lack even a basic definition of what we mean by impact?
Third, there’s the question of what we are supposed to make of averaged impacts. We know that debt is not right for everyone, so why would or should debt be held to an average standard of goodness? Surely debt is good for some people, in some circumstances, at certain times. What’s the point of diluting —and possibly cancelling— the benefits to these people statistically? The issue is one of targeting interventions, not passing across-the-board judgments on them.
Finally, for all the sophisticated statistical tools deployed, it all hinges on the quality of the data collected. Given that RCTs are fundamentally an empirical exercise, it is odd that in the dozens of online commentaries on the six studies there hasn’t been much discussion of the empirics itself. The research papers are often not written for people like me to understand (have you tried reading them?), so I am relegated to being a user of the abstract, introduction, and conclusions sections. But what I can read —what we can all process— is the survey questionnaire. I would urge all those who feel they need to have an opinion on these studies to start by reading the questionnaires. Take this one, for example. In your mind, is this fit for the purpose of evaluating the impact of microcredit? Does this meet the lean research standards proposed by Kim Wilson? Try to have the questions answered tonight by your partner or spouse. Then try to imagine what kind of data you’d get if you were going around asking these questions of perfect strangers, showing up at their doorstep unannounced.
The suggestion that such empirical studies can settle the impact question of microcredit is just as naïve as the prior suggestion that the best way to get poor people out of poverty is to get them into recurrent cycles of debt. I agree entirely with the findings from these RCTs. Was anyone really hopeful that the six RCTs would turn out otherwise?
[From Brookings Institution´s Tech Tank blog, April Fool´s Day 2015]
I feel like I need to see a shrink. A work shrink, that is, one that can help me address some deep-seated issues and conflicts I’ve accumulated through my years working in economic development.
My right brain tells me that we need to take holistic approaches to development; that it´s futile to build marketplaces if there are no roads that lead farmers to them, to give traders microloans if they don’t have basic commercial skills, or to invest in primary education if we cannot tend to the children’s health. My right brain is full of ideas, but knows that there are no miracle cures, no silver bullets. It prods me to try different things and not lose faith that things can get better. Development is a process not of putting individual balls in motion, but of balls colliding in complex, reinforcing ways.
Yet my left-brain wants me to move methodically through all the issues, sequentially, cutting them into small chunks so that the impact of each can be assayed through purposeful, careful, randomized (i.e. clinical) experimentation. It urges me to avoid chasing grand theories, and instead to cash in small impacts here and there. My left-brain wears a white coat; it demands verifiable evidence that can be attributed to specific factors, not general observation.
I’m delaying the visit because I know what the shrink will tell me: that I don’t have to choose, that I should engage both sides of my brain at the same time – it’s the power of and.
But how? My right brain accuses my left-brain of being on a futile search for not one but a whole sequence of silver bullets – for isn’t the expectation of a single, small thing having an impact by itself the very definition of a silver bullet? My left brain retorts that the right brain is trying to get to El Dorado without a map; should we not properly call that a wild goose chase? I’m at a mental stalemate.
I thought I’d reconcile them by proposing to do clean experiments not only individually on each of the n possible development interventions, but also on each of the 2n combinations between them. Get evidence not only on each ball, but also on each collision scenario between any number of balls. But nobody will give me the money or the time to do that. If we assume (rather harshly) that there are only 20 things that may matter at all in development, then that will require us to do over a million (=220) Randomized Controlled Trials (RCTs). There probably aren’t even enough econ PhD students in the whole world to slave on these RCTs. That can’t be the way.
I learned from Daniel Pink’s book A Whole New Mind that I am better off building up my right brain credentials in the coming years. With the increasing delegation of routine tasks and data analytics to machines, the balance of (human) power will shift from the reductionist, analytical left-brained to the holistic, empathetic right-brained. Suits me fine: I am good at numbers, but I like interpreting them through stories. I feel that the development world is veering sharply to the left-brained, because the spinning of detailed hypotheses, research plans, and evidence-bases is so attractive to donors. This may validate the main conclusion of the book: in the data- and computing-rich western world where correlations are a dime a dozen, the right brained rule. But in a data-scarce development situation, the left-brained are supreme.
Talking to the shrink is not just a matter of seeking professional guidance on which is the most impactful approach. There is also an element of personal branding and self-esteem involved. Who wants to get in front of the data science train and be accused of being faith-based? Equally, who wants to be dismissed for being (oh that awful word, outside academia) academic?
In fact, I need to go to the shrink mainly because both brains are making me (professionally) depressed. The right-brain because, the chance that we will stumble upon the right cocktail of interventions and the left-brain because it deals with components rather than entire systems, and we are trying to measure small impacts with such coarse-grained rulers.
Anybody know a good development shrink?
[From Savings Revolution blog, April Fool´s day]
If rigorous impact evaluation can improve the lives of poor people in developing countries, why couldn´t it improve yours? But few of us have the time or inclination to fill in the necessary questionnaires, the discipline to refrain from polluting behaviors that can get in the way of precise measurement, and the patience to wait a couple of years to get the results. Gamification may hold the answer: if it makes you do and buy things online that you otherwise wouldn´t do and buy, why not gamify your self-improvement research?
The smart folks at Controlled Human Impacts Corp (CHIC) have come up with a board game that takes a group of friends through the process of evaluating impacts on a range of daily activities and chores.
This is how it works. The board is split up into a sequence of zones, which players need to transition through. Everyone starts in the Faith Zone, and to move into the next zone, the Evidence Zone, they must cross the Base Line. They do so by picking up 578 cards from the Instrument Pile on the Evidence Zone, and answering the question on each card. The questions are drawn from among the best that real researchers have used in the field, such as (from here): During the last week, how many days were you bothered by things that usually don’t bother you? Thinking about two weeks ago, how much did your household spend on cold cuts and sausages that week? In a scale from 1 to 10, do you think most people would try to take advantage of you if they got a chance, or would they try to be fair? Who decides whether to buy an appliance or not for the home, you or your spouse?
Questions must be answered out loud and in rapid-fire fashion. Other players can call out “hesitation!” when answers are not delivered with enough conviction, or “inconsistency!” when the answer to a question contradicts the answer given to a previous question, but that does not affect the course of the game in any way.
The next step up from the Evidence Zone is the Treatment Zone. Here each player picks a single card from the Treatment Pile, which contains an action that the player must do or avoid. These actions for all players are themed around a given topic, which need not be particularly significant but they need to be easy enough to do – or not do. If the theme is dishwashing, for example, the actions on the cards might be things like: Stick a note on your forehead reminding you to do the dishes tonight. Or think up three good reasons why you should do the dishes tonight.
Once you have accepted your action or inaction, you move into the Observation Zone. This part is timed, using the sand clock provided with the game, and tends to be the slowest part of the game. While you are in the Observation Zone, you can think or not think about your action or inaction. At every turn of the sand clock, each player needs to pick up and declare the answer to another set of 459 cards from the same Instrument Pile used previously. Here you might get to tell the other players: During the last 30 days, due to lack of money or resources, how many nights did you or your child go to bed hungry? In the last two years, have you bought or sold a mattress or a heater? In the last month, how much did your household receive from jobs without a fixed salary? On a scale of five, how much trust do you have in your family?
This is done two or three times (players can decide that as they go along), and on completion of this process, players move automatically to the Trial Zone. In this Zone, the players look at each others´ responses and have to decide whether Impact has happened or not, based on how action and inaction changed their answers. For this, they can use a calculator provided in the game box that only has two function buttons: average and subtract. If a player finds impact on someone else (for instance in the previous example, that more dishes have indeed been washed), he or she should shout out: “ME! ME!” (short-hand for impact that has been measured and evaluated). If no impact is deemed, players can still declare ME! if they can find specific circumstances under which impact could be detected. For instance, if more dishes got washed on even-numbered days, if more dishes didn´t get washed but those that did were more sparkling, or if the left arm did more washing than the right arm.
Impacted players pick up a Science Point, and can then start all over again but each time they must pick a different action or inaction card in the Treatment Zone. The winner is whoever collects most Science Points after n rounds.
CHIC´s game is sure to transform our lives. Their motto is: “a life with more data is a life with more meaning.” But the game doesn´t come cheap. CHIC insists that the price is the only data point that is not significant. They cite research with dozens of people who have played the board game where the action was to buy the game, which consistently shows ME! ME!
[From Brookings Institution´s Tech Tank blog, with David Porteous, 10 March 2015]
We tend to think of having a formal identity as an enabler for social and economic inclusion, but in fact identity can have entirely the opposite effect. Once socioeconomic interactions are based on a standardized notion of identity, it is likely that social status based on past achievements, family histories, personal connections, political backing, wealth and education levels will influence socioeconomic outcomes — thereby potentially reinforcing the established class hierarchy. Systems that are based on anonymity might in fact be the most equitable and inclusive, in the sense of ensuring equal participation by all, by systematically stripping out social status.
But anonymous systems carry a high cost in terms of efficiency. Reputations would be impossible to establish, contracts would be hard to enforce, and there would be more insecurity as it would be much harder to track and clamp down on illicit activities. It is therefore not at all certain that the poorer segments of the population would be better off in absolute terms if the economy worked on the basis of anonymity.
The need for digital identities for inclusive access
In fact, giving lower-income people digital identities would make it possible for them to participate in the modern digital economy in many ways: to open accounts and receive moneys from anyone, assert their rights over digital services they have contracted and digital assets they have purchased, settle disputes, etc. But establishing a formally recognized identity can be a major hurdle in itself, especially in countries that do not have digitized national ID schemes.
It is ironic that the difficulty of establishing formal identity in the first place often prevents so many lower-income, and especially rural, people from accessing digital services. Identity systems with selective coverage of the population create a double whammy of inequality: on the one hand, these partial systems help the haves to carry their social and economic status symbols and reputations into every market interaction they are engaged in, and on the other they negate digital visibility and access to digital services for the have not´s.
We argue in a new research paper that it should be the government´s responsibility to ensure that every citizen in fact has a digital identity, not merely to create a platform that enables people to have digital identities. The Indian government´s Aadhar push to provide everyone in India with a unique number ID linked to biometrics is a good example of such a policy.
The demands of identity verification systems
The problem is that different policy agendas converge on the issue of identity and have different requirements for a digital identity platform. What works as an identify standard for financial systems may not be good enough for law enforcement agencies. The risk is that governments adopt the highest standard, with the result that the inclusion agenda and the needs of the poor are ignored.
If there is no centralized government system for identity, then what we need is a system that:
This is the notion of social identity. Let people with meager resources help each other overcome their limitations: each may have very little voice, but collectively they represent a potentially vast information system for official identification purposes. That is hard to reconcile with the way governments and formal institutions tend to handle identity verification: in silos, contained within databases and cards. We need more flexible notions of identity, which build layers of identity information and verification through social networks – as well as bureaucratized ID-seeking processes.
[From Business Fights Poverty blog, 6 February 2015, with David Porteous]
The limitations of what may be termed the push era of digital financial inclusion are becoming increasingly apparent. We have seen strong outreach by a relatively small number of very powerful players —mostly telcos and some large banks— to recruit customers onto their digital transactional platforms. In some cases, where the client proposition has been strong enough and the provider has invested sufficient resources, there has been dramatic uptake (e.g. M-PESA). However, in most cases, actual usage of digital services lags, dormancy is very high, and usage profiles are quite narrow. Across the developing world, 70% of registered mobile wallets in 2014 had not been used during the last 90 days, according to the GSMA.
As digital channels become more widely available, and in particular as smart phones become accessible and affordable to far wider sections of the population, there is a need and an opportunity to develop stronger pull propositions which cause customers to want to use digital transaction services. Since financial services are rarely if ever desired for their own sake, these pull propositions will likely be oriented towards solving daily problems which people experience as consumers, as members of communities and social networks, or as they conduct their business.
We believe that the key to creating these pull propositions is to embed the digital financial service capabilities within software tools (i.e. smart phone apps) that directly address these broader needs. An adequate software solution can help contextualize the need for the financial service and improve its presentation, thereby greatly enhancing the customer experience.
The appropriate package of digital transactions plus software can drive more systematic use, and usage creates data. Thus, in the process of solving narrower problems, opportunities are created to offer new financial possibilities to those customers based on new insights gained.
We can summarize the general approach to financial inclusion thus:
transactions + software tool = Customer usage patterns + data analysis
If the push era was about stand-alone financial transactional services, the pull era will be about software solutions that can weave through transaction patterns and data. This much is clear, but the harder questions are which specific customer problems these solutions will be trained on, and what sorts of insights the corresponding data will generate.
In a recent paper, we have set out three different pathways in which this future can unfold: (i) through advanced data analytics that shed light on individuals´ needs and characteristics; (ii) by supporting small businesses through which financial services can propagate into local communities; and (iii) by supporting social and peer networks so that individuals who are better known to financial service providers can channel services to others within their networks.
We are particularly intrigued by the latter two pathways, which create a fuzzier distinction between users and channels. In the lower income segment, where the amount of available data is likely to remain low for a long time, training big data resources on networks seems more promising than focusing it entirely on individuals.
[From Brookings Institution´s Tech Tank blog, 22 January 2015]
Recent reports have highlighted how mobile-based financial services are transforming banking and payments in Kenya, Bangladesh, and Peru, and all the hype about how they are about to explode everywhere else. For all of the promise that digital financial systems have for lowering costs and helping people all over the globe, it is unfortunate that their development is hampered by regulation that protects the interests of the largest providers. These regulations create significant barriers and raise the total costs to achieve universal financial inclusion.
It is indeed conceivable that purely digital financial transactions could be handled at vanishingly small unit costs, from anywhere. But the cost that won´t go away is that at the interface between the new digital payment system and the legacy payment system – hard cash. Cash in/cash out (CICO) points are like tollgates at the edge of the digital payments cloud.
Cash is Still King
Even in areas with flourishing mobile banking usage, people tend to cash in every time they want to make a mobile payment, and to cash out immediately and in full every time they receive digital money. Rather than displacing cash, digital platforms have made local cash ecosystems more efficient. Without full backward compatibility with cash, digital payment systems could not take root.
The bigger issue is not the size of the CICO toll, but the fact that small players cannot expect to have the transaction volume to sustain a widespread CICO network. The incumbent banks and telecommunications firms have built in competitive advantages. They can quickly form agreements with brick and mortar shops, attract users from the current customer base, threaten new entrants, and aggregate enough transactions to induce CICO outlets to maintain sufficient liquidity on hand.
Therefore, the competition in digital financial services will not be determined primarily by what happens within the digital payments market itself, but rather by what happens in the contiguous cash market. The power of digital services is their ability to transcend geography, and yet success in the digital payments space will go to whoever has the best physical CICO footprint.
Regulators treat the digital payments service and the CICO service as conjoined twins: each digital financial service provider must have its own base of contractually bound CICO outlets. When the two services are bundled it is not surprising that the tough economics of CICO —and, therefore, the incumbent— dominates.
A Two Market Regulatory Approach
In a recent paper, I argue it is necessary to split up these two markets, from a regulatory point of view. The market for effecting electronic payments (issuing payment instructions and debiting and crediting electronic accounts accordingly) is logically distinct from the market for exchanging two forms of money (hard cash versus electronic value).
Most regulators approve of stores receiving electronic money from customers in exchange for packs of rice on a store shelf. But, if that same electronic money was exchanged for cash then it would violate the law in many countries.
In the latter case, the store is presumed to be an agent of the customer’s financial service provider, and the store cannot offer the CICO service without an agency contract from that provider. But why? The cash that was offered was the store’s as is the account that would receive the electronic payment, and the transaction would have occurred entirely through a secure, real-time technology platform that banks offer all their clients.
A Regulatory Fix
Of course, purely financial transactions are usually held to higher consumer protection standards than normal commercial transactions. My proposal is not to deregulate CICO, but to create a new license type for CICO network managers. Holders of this license would carry certain consumer protection obligations (such as ensuring that tariffs are explicitly posted at all CICO outlets, and that they have a call center to handle any complaints that customers may have on individual CICO outlets) – entirely reasonable expectations for retailers, even if we normally don´t ask them of rice sellers.
But once you have a CICO license, then you could sign up any store you wanted and crucially, offer CICO services on the platform of any financial institution in which you have an account. In other words, you wouldn’t have to beg the incumbent to give you a special agent contract. All you would need to do is to open a normal customer account with them, which the incumbent couldn´t deny you.
This one little change would completely shift the competitive dynamics of digital financial services. Under the current direct agency model, incumbent firms have no incentive to make it easier for competitors to create CICO outlets. Whereas under the independent CICO network manager model, all licensed CICO networks would have the incentive to offer CICO services for all providers, no matter their size: with a full suite of available services, they will find it easier to sign up stores to work for them, and these stores will find it easier to convince more users to walk into their stores.
Incumbents would still be free to establish their own proprietary CICO networks, as today. But they would have to compete with independent CICO networks that are now able to aggregate business from all financial service providers, creating true competition.
All players could then claim a comparable physical presence as the incumbent. They would all benefit from the same branded competition between CICO networks. They could compete strictly on the basis of the quality of their digital financial services offering.
Unbundling the regulatory treatment of digital financial services would help competition reach every segment of the business; the current integrated model only serves the interests of the largest telecommunication companies and banks in the land.
[From NexBillion blog, 13 January 2015, with David Porteous]
In Spent: Looking for Change, the recent documentary about financial exclusion in the United States (embedded below), there is a moving segment about a young man named Justin who is determinedly rebuilding his life after having obliterated his credit rating by failing to repay his credit card debt. He says (from the 16:00 mark): “People often judge me on the choices I've made, not knowing the options that I had.” Maybe if we knew the limited options Justin had when he decided not to repay his debt, we would agree with him that he had taken the most appropriate, even responsible, action by not repaying. If that were the case, wouldn´t that make us want to trust him more rather than less? Years later, when his situation and options had changed, we would likely feel positive about offering him a new loan for a new beginning.
Economists say that credit bureaus are about solving information asymmetries between creditors and borrowers. But which asymmetries are the economists talking about, exactly? No credit bureau helps Justin explain to financial institutions that he was forced to scratch out a living entirely on his own from age 16, that his earnings didn´t always last to the end of the month, but that those days are now behind him. All the credit bureaus do is to propagate information on his past non-repayment.
As David Graeber argues compellingly in his sweeping history of money Debt: The First 5000 Years, we now take for granted that all loans must be repaid, fully and on time, as if that was a natural societal imperative - but that has not traditionally been the case. Debt moratoria, renegotiations, substitution for tokens of assets or labor, even wholesale cancellation of debts, are recurring themes everywhere. It is only fairly recently that debt repayments have become an absolute test of character, often summarized in a three digit score. Credit scoring has become something about you, disconnected from your circumstances and options.
With the digitization of finance, we face the daunting prospect of “the system” having an unforgiving and unforgetting memory of poor people´s formal debt repayments while knowing little else of any real significance about them. Credit providers will build a profile of you based on disjointed circumstantial evidence, slowly and painfully crossing datasets, almost accidentally – you have $20 in your savings account, a mobile ARPU of $1.70 per month, demonstrated successful repayment of a $10 instant mobile loan – but you can blow whatever positive attributes you’ve demonstrated all on one unpaid debt. One strike, you´re out. Big Data can become the basis for a new exclusion.
Exclusion is often the result of ignorance. Ignorance creates prejudice: in the absence of concrete information, you generalize. And big data is fundamentally about drawing big generalizations - excuse me, correlations. I can easily believe that these correlations will work increasingly well on average, giving new financial opportunities to many. But in the process, many Justins will be pushed further into financial untouchability, silent casualties of the unfathomable wisdom of the machine. Something that demonstrably works on average may be commercially satisfactory, even socially impactful, but may fall short on inclusiveness – in the sense of helping those who need help the most.
A way to prevent these unfortunate side effects is for financial service providers to avoid single prescriptions and actively work towards enabling multiple avenues for credit, so that people like Justin can have several shots at getting the credit they need. Providers should recognize that they might never get close enough to poor customers living in marginalized communities to fully understand their evolving circumstances and options.
So in addition to seeking ways of collecting more data on prospective borrowers with which to make individual credit decisions, lenders should also seek ways to build more trusted relationships with local players in each community, who they can count on to make credit recommendations and help channel credit within their network of friends, customers, savings circles, and business associates. Justin´s credit need not come directly from a bank, it could come from a local store that understands the true nature of his troubles, or a neighbor who knows how he´s turned himself around.
In a recent paper, we develop this theme by exploring three different Pathways to Smarter Financial Inclusion: by serving poor people directly, reaching poor people indirectly through the businesses within their communities where they work and buy things, or using social networks as (informal) distribution channels. Note that all three pathways rely heavily on clever analytics, but the objective of each is subtly different: the first draws credit inferences from the little data that is specifically available on poor customers, the second creates sufficient business intelligence and data flow to be able to underwrite local traders´ and entrepreneurs´ credit decisions, and the third creates incentive structures for peer screening and monitoring.
Finance will reduce people´s sense of vulnerability only if it creates a hierarchy of options in people´s minds. It´s not necessarily the case that people want more credit, they just want more options. By moving beyond traditional methods of collecting and interpreting borrower data, financial service providers can help provide those options to people who are often excluded.
[From Brookings Institution´s Tech Tank blog, 2 December 2014 (with David Porteous)]
It’s easy to imagine a future in a decade or less when most people will have a smartphone. In our recent paper Pathways to Smarter Digital Financial Inclusion, we explore the benefits of extending financial services to the mass of lower-income people in developing countries who are currently dubious of the value that financial services can bring to them, distrustful of formal financial institutions, or uncomfortable with the treatment they expect to receive.
The report analyzes six inherent characteristics of smartphones that have the potential to change market dynamics relative to the status quo of simple mobile phones and cards.
1. The graphical
Service Provider Changes:
4. Greater freedom
to program services without requiring the acquiescence or active participation
of the telco.
Taken together, these changes may lower the costs of designing for lower-income people dramatically, and the designs ought to take advantage of continuous feedback from users. This should give low-end customers a stronger sense of choice over the services that are relevant to them, and voice over how they wish to be served and treated.
Traditionally poor people have been invisible to service providers because so little was known about their preferences that it was not possible build a service proposition or business case around them. The paper describes three pathways that will allow providers to design services on smartphones that will enable an increasingly granular understanding of their customers. Each of the three pathways offers providers a different approach to discover what they need to know about prospective customers in order to begin engaging with them.
Pathway One: Through Big Data
Providers will piece together information on potential low-income customers directly, by assembling available data from disparate sources (e.g. history of airtime top-ups and bill payment, activity on online social networks, neighborhood or village-level socio-demographic data, etc.) and by accelerating data acquisition cycles (e.g. inferring behavior from granting of small loans in rapid succession, administering selected psychometric questions, or conducting A/B tests with special offers). There is a growing number of data analytics companies that are applying big data in this way to benefit the poor.
Pathway Two: Through local Businesses
Smartphones will have a special impact on micro and small enterprises, which will see increasing business benefits from recording and transacting more of their business digitally. As their business data becomes more visible to financial institutions, local firms will increasingly channel financial services, and particularly credit, to their customers, employees, and suppliers. Financial institutions will backstop their credit, which in effect turns smaller businesses into front-line distribution partners into local communities.
Pathway Three: Through Socio-Financial Networks
Firms view individuals primarily as managers of a web of socio-financial relationships that may or may not allow them access to formal financial services. Beyond providing loans to “creditworthy” people, financial institutions can provide transactional engines, similar to the crowdfunding platforms that enable all people to locate potential funding sources within their existing social networks. A provider equipped with appropriate network analysis tools could then promote rather than displace people´s own funding relationships and activities. This would provide financial service firms valuable insight into how people manage their financial needs.
The pathways are intended as an exploration of how smartphones could support the development of a healthier and more inclusive digital financial service ecosystem, by addressing the two critical deficiencies of the current mass-market digital finance systems. Smartphones could enable stronger customer value propositions, leading to much higher levels of customer engagement, leading to more revelation of customer data and more robust business cases for the providers involved. Mobile technology could also lead to a broader diversity of players coming into the space, each playing to their specific interests and contributing their specific set of skills, but together delivering customer value through the right combination of collaboration and competition.