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November 9, 2020
The Importance of Digital Payments to Financial Inclusion
Editor's note: In December, macroblog will become part of the Atlanta Fed's Policy Hub publication.
A recent Atlanta Fed white paper titled "Shifting the Focus: Digital Payments and the Path to Financial Inclusion" calls for a concerted effort to bring underbanked consumers into the digital payments economy. The paper—by Atlanta Fed president Raphael Bostic, payments experts Shari Bower and Jessica Washington, and economists Oz Shy and Larry Wall—acknowledges the importance of longstanding efforts to bring the full range of banking services to unbanked and underbanked consumers. (For another take on the white paper and its relationship to the Atlanta Fed's mission, you can read here.) However, the white paper observes, progress towards this goal has been slow. It further notes the growing importance of digital payments for a wide variety of economic activities. It concludes by highlighting a number of potential policies that could expand inclusion in the digital payments economy for policymakers to consider.
The 2017 Federal Deposit Insurance Corporation (FDIC) National Survey of Unbanked and Underbanked Households found that 6.5 percent of U.S. households are unbanked and an additional 18.7 percent underbanked. In this survey, a household is considered underbanked if it has a bank account but has obtained some financial services from higher-cost alternative service providers such as payday lenders. The proportions are even higher in some minority communities, with an unbanked rate for Black households at 16.9 percent. These figures were down modestly from earlier FDIC surveys, but progress remains inadequate.
The white paper retains full inclusion as the ultimate goal but argues we should not let the difficulties of achieving full inclusion deter us from moving aggressively to spread the benefits of digital payments. Such digital payments in the United States are typically made using (or funded by) a debit or credit card. Yet a recent paper by Oz Shy (one of the coauthors of this post) finds that over 4.8 percent of adults in a recent survey lack access to either card. Moreover, those lacking a card tend to be disproportionately concentrated in low-income households, with almost 20 percent of households earning under $10,000 annually and over 14 percent of those earning under $20,000 a year having neither card. These numbers also vary by ethnic groups: 4.8 percent of white and 10.2 percent of Black surveyed consumers.
The lack of access to digital payments has long been a costly inconvenience, but recent developments are moving digital payments from the "nice-to-have" category toward the "must-have" category. Card payments are increasing at an annual rate of 8.9 percent by number in recent years. While cash remains popular, debit cards have overtaken cash for the most popular in-person type of payments. Moreover, the use of cards in remote payments where cash is not an option nearly equals their use for in-person transactions. Most recently, COVID-19 has accelerated this move toward cards, with a 44.4 percent year-over-year increase in e-commerce sales in the second quarter of 2020.
These trends in card usage relative to cash usage pose several problems for consumers who lack access to digital payments. First, some retailers are starting to adopt a policy of refusing cash. Second, many governments are deploying no-cash parking meters, along with highway toll readers and mass transit fare machines that do not accept cash. Third, the growth of online shopping is being accompanied by a decrease in the number of physical stores, resulting in reduced access for those lacking cards.
The last part of the white paper discusses a number of not mutually exclusive ways of keeping the shift from paper-based payments (cash and checks) to digital payments from adversely affecting those lacking a bank account. A simple, short-term fix is to preserve an individual's ability to obtain cash and use it at physical stores. No federal law currently prevents businesses from going cashless, but some states and localities have mandated the acceptance of cash.
However, merely forcing businesses to accept cash does not solve the e-commerce problem, nor does it promote the development of faster, cheaper, safer, and more convenient payment systems, so considering alternatives takes on greater importance. One option the paper discusses is that of cash-in/cash-out networks that allow consumers to convert their physical cash to digital money (and vice versa). Examples of this in the United States include ATMs and prepaid debit cards, as well as prepaid services such as mass transit cards that can be purchased for cash in physical locations.
Another option is public banking. One version of this that has been proposed is a postal banking system like the ones operating in 51 countries outside the United States and the one that was once available here. Another public banking possibility would provide consumers with basic transaction accounts that allow digital payments services. The government or private firms (such as banks, credit unions, or some types of fintech firms) could administer such services.
The paper concludes with a discussion of some important challenges inherent in moving toward a completely cashless economy accessible to everyone. One such consideration is access to mobile and broadband. This issue has a financial dimension, that of being able to afford internet access. It also has a geographic dimension in that many rural areas lack both high-speed internet and fast cellphone networks. Another dimension is that of providing a faster payment service that would allow people to obtain earlier access to their incoming funds, and result in bank balances more accurately reflecting outgoing payments. Finally, the white paper raises the potential for central bank digital currency to expand access to digital payments. However, central bank digital currency raises a large number of issues that the federal government and Federal Reserve would need to work through before it could be a viable option.
November 21, 2019
Private and Central Bank Digital Currencies
The Atlanta Fed recently hosted a workshop, "Financial System of the Future," which was cosponsored by the Center for the Economic Analysis of Risk at Georgia State University. This macroblog post discusses the workshops discussion of digital currency, including Bitcoin, Libra, and central bank digital currency (CBDC). A companion Notes from the Vault post provides some highlights from the rest of the workshop.
The introduction of Bitcoin has sparked considerable interest in cryptocurrencies since its introduction in the 2008 paper "Bitcoin: A Peer-to-Peer Electronic Cash System" by Satoshi Nakamoto. However, for all its success, Bitcoin is not close to becoming a widely accepted electronic cash system. Why it has yet to achieve its original goals is the topic of a paper by New York University professors Franz Hinzen and Kose John, along with McGill University professor Fahad Saleh titled "Bitcoin's Fatal Flaw: The Limited Adoption Problem."
Their paper suggests that the inability of Bitcoin to achieve wider adoption is the result of the interaction of three features: the need for agreement on ledger contents (in blockchain terminology, "consensus"), free entry for creating new blocks (permissionless or decentralized), and an artificial supply constraint. The supply constraint means that an increase in demand leads to higher Bitcoin prices. Such a valuation increase expands the network seeking to create new blocks (that is, increases the number of Bitcoin "miners"). But an increase in the network size slows the consensus process as it takes time for newly created blocks to reach all of the miners across the internet. The end result is an increase in the time needed to make a payment, reducing the value of Bitcoin as a means of payment—a significant consideration, obviously, for any type of currency.
As an alternative to the Bitcoin consensus protocol, they suggest a public, permissioned blockchain that results in faster transactions because it imposes limits on who can create new blocks. In their system, new blocks would be selected based on a weighted vote based on the blockchain's cyptocurrency held by validators (in other words, approved block creators). If validators were to approve new and malicious blocks, that would erode the value of the validator's existing cryptocurrency holdings and thus provide an incentive to behave honestly.
Federal Reserve Bank of Atlanta visiting economist Warren Weber presented some work with me on Libra, the new digital coin proposed by Facebook. Weber began by pointing to another problem with using Bitcoin in payments: the cryptocurrency's volatile value. Libra solves this problem by proposing to hold a portfolio of assets denominated in sovereign currencies, such as the U.S. dollar, that will provide one-for-one backing of the value of Libra. This approach is similar to that taken by some other "stablecoins," with the exception that Libra proposes to be stable relative to an index of several currencies whereas other stablecoins are designed to be stable with respect to only one sovereign currency.
Drawing on his background in economic history, Weber observes that introducing a new private currency is hard, but not impossible. For example, he pointed to the Stockholm Bank notes issued in Sweden in the 1660s. These notes worked because they were more convenient than the alternatives used in that country. The fact that other U.S. payments systems are heavily bank-based might afford an advantage to Libra.
Although no one is certain of the public's interest in using Libra, policymakers around the world have taken considerable interest in the potential implications of Libra for monetary policy and financial regulation. Could Libra significantly reduce the use of the domestic sovereign currencies in some countries, thus reducing the effectiveness of monetary policy? How might financial institutions providing Libra-based services be regulated?
One of the other possible policy responses to Libra is central banks' introduction of digital currency. Economists Itai Agur, Anil Ari, and Giovanni Dell'Ariccia from the International Monetary Fund consider some of the issues in developing a CBDC in their paper "Designing Central Bank Digital Currencies." They start by observing some important differences between cash and bank deposits. Cash is completely anonymous in that it reveals nothing about the identity of the payer. However, lost or stolen cash can't be recovered, so it lacks security. Deposits have the opposite properties—they are not anonymous, but there is a mechanism to recover lost or stolen funds.
The paper develops a model in which CBDC can be designed to operate at multiple points on a continuum between deposits and cash. The key concern from a public policy perspective is that the more CBDC operates like bank deposits, the more it will depress bank credit and output. However, if the CBDC operates too much like paper currency, then it could supplant paper currency and eliminate a payments method that some individuals prefer. The paper proposes that CBDC be designed to look more like currency to minimize the extent to which CBDC replaces bank deposits. The problem then becomes how to avoid CBDC reducing the usage of cash to the point where cash is no longer viable. (For example, merchants could decide to stop accepting cash because they find that the few transactions using cash do not justify the costs of accepting it.) The way the paper proposes to keep CBDC from being too attractive relative to cash by applying a negative interest rate to the CBDC. The result would be that those who most highly value CBDC will use it, but the negative rate will likely deter enough people so that cash remains a viable payments mechanism.
January 4, 2018
Financial Regulation: Fit for New Technologies?
In a recent interview, the computer scientist Andrew Ng said, "Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI [artificial intelligence] will transform in the next several years." Whether AI effects such widespread change so soon remains to be seen, but the financial services industry is clearly in the early stages of being transformed—with implications not only for market participants but also for financial supervision.
Some of the implications of this transformation were discussed in a panel at a recent workshop titled "Financial Regulation: Fit for the Future?" The event was hosted by the Atlanta Fed and cosponsored by the Center for the Economic Analysis of Risk at Georgia State University (you can see more on the workshop here and here). The presentations included an overview of some of AI's implications for financial supervision and regulation, a discussion of some AI-related issues from a supervisory perspective, and some discussion of the application of AI to loan evaluation.
As a part of the panel titled "Financial Regulation: Fit for New Technologies?," I gave a presentation based on a paper I wrote that explains AI and discusses some of its implications for bank supervision and regulation. In the paper, I point out that AI is capable of very good pattern recognition—one of its major strengths. The ability to recognize patterns has a variety of applications including credit risk measurement, fraud detection, investment decisions and order execution, and regulatory compliance.
Conversely, I observed that machine learning (ML), the more popular part of AI, has some important weaknesses. In particular, ML can be considered a form of statistics and thus suffers from the same limitations as statistics. For example, ML can provide information only about phenomena already present in the data. Another limitation is that although machine learning can identify correlations in the data, it cannot prove the existence of causality.
This combination of strengths and weaknesses implies that ML might provide new insights about the working of the financial system to supervisors, who can use other information to evaluate these insights. However, ML's inability to attribute causality suggests that machine learning cannot be naively applied to the writing of binding regulations.
John O'Keefe from the Federal Deposit Insurance Corporation (FDIC) focused on some particular challenges and opportunities raised by AI for banking supervision. Among the challenges O'Keefe discussed is how supervisors should give guidance on and evaluate the application of ML models by banks, given the speed of developments in this area.
On the other hand, O'Keefe observed that ML could assist supervisors in performing certain tasks, such as off-site identification of insider abuse and bank fraud, a topic he explores in a paper with Chiwon Yom, also at the FDIC. The paper explores two ML techniques: neural networks and Benford's Digit Analysis. The premise underlying Benford's Digit Analysis is that the digits resulting from a nonrandom number selection may differ significantly from expected frequency distributions. Thus, if a bank is committing fraud, the accounting numbers it reports may deviate significantly from what would otherwise be expected. Their preliminary analysis found that Benford's Digit Analysis could help bank supervisors identify fraudulent banks.
Financial firms have been increasingly employing ML in their business areas, including consumer lending, according to the third participant in the panel, Julapa Jagtiani from the Philadelphia Fed. One consequence of this use of ML is that it has allowed both traditional banks and nonbank fintech firms to become important providers of loans to both consumers and small businesses in markets in which they do not have a physical presence.
Potentially, ML also more effectively measures a borrower's credit risk than a consumer credit rating (such as a FICO score) alone allows. In a paper with Catharine Lemieux from the Chicago Fed, Jagtiani explores the credit ratings produced by the Lending Club, an online lender that that has become the largest lender for personal unsecured installment loans in the United States. They find that the correlation between FICO scores and Lending Club rating grades has steadily declined from around 80 percent in 2007 to a little over 35 percent in 2015.
It appears that the Lending Club is increasingly taking advantage of alternative data sources and ML algorithms to evaluate credit risk. As a result, the Lending Club can more accurately price a loan's risk than a simple FICO score-based model would allow. Taken together, the presenters made clear that AI is likely to also transform many aspects of the financial sector.
January 3, 2018
Is Macroprudential Supervision Ready for the Future?
Virtually everyone agrees that systemic financial crises are bad not only for the financial system but even more importantly for the real economy. Where the disagreements arise is how best to reduce the risk and costliness of future crises. One important area of disagreement is whether macroprudential supervision alone is sufficient to maintain financial stability or whether monetary policy should also play an important role.
In an earlier Notes from the Vault post, I discussed some of the reasons why many monetary policymakers would rather not take on the added responsibility. For example, policymakers would have to determine the appropriate measure of the risk of financial instability and how a change in monetary policy would affect that risk. However, I also noted that many of the same problems also plague the implementation of macroprudential policies.
Since that September 2014 post, additional work has been done on macroprudential supervision. Some of that work was the topic of a recent workshop, "Financial Regulation: Fit for the Future?," hosted by the Atlanta Fed and cosponsored by the Center for the Economic Analysis of Risk at Georgia State University. In particular, the workshop looked at three important issues related to macroprudential supervision: governance of macroprudential tools, measures of when to deploy macroprudential tools, and the effectiveness of macroprudential supervision. This macroblog post discusses some of the contributions of three presentations at the conference.
The question of how to determine when to deploy a macroprudential tool is the subject of a paper by economists Scott Brave (from the Chicago Fed) and José A. Lopez (from the San Francisco Fed). The tool they consider is countercyclical capital buffers, which are supplements to normal capital requirements that are put into place during boom periods to dampen excessive credit growth and provide banks with larger buffers to absorb losses during a downturn.
Brave and Lopez start with existing financial conditions indices and use these to estimate the probability that the economy will transition from economic growth to falling gross domestic product (GDP) (and vice versa), using the indices to predict a transition from a recession to growth. Their model predicted a very high probability of transition to a path of falling GDP in the fourth quarter of 2007, a low probability of transitioning to a falling path in the fourth quarter of 2011, and a low but slightly higher probability in the fourth quarter of 2015.
Brave and Lopez then put these probabilities into a model of the costs and benefits associated with countercyclical capital buffers. Looking back at the fourth quarter of 2007, their results suggest that supervisors should immediately adopt an increase in capital requirements of 25 basis points. In contrast, in the fourth quarters of both 2011 and 2015, their results indicated that no immediate change was needed but that an increase in capital requirements of 25 basis points might be need to be adopted within the next six or seven quarters.
The related question—who should determine when to deploy countercyclical capital buffers—was the subject of a paper by Nellie Liang, an economist at the Brookings Institution and former head of the Federal Reserve Board's Division of Financial Stability, and Federal Reserve Board economist Rochelle M. Edge. They find that most countries have a financial stability committee, which has an average of four or more members and is primarily responsible for developing macroprudential policies. Moreover, these committees rarely have the ability to adopt countercyclical macroprudential policies on their own. Indeed, in most cases, all the financial stability committee can do is recommend policies. The committee cannot even compel the competent regulatory authority in its country to either take action or explain why it chose not to act.
Implicit in the two aforementioned papers is the belief that countercyclical macroprudential tools will effectively reduce risks. Federal Reserve Board economist Matteo Crosignani presented a paper he coauthored looking at the recent effectiveness of two such tools in Ireland.
In February 2015, the Irish government watched as housing prices climbed from their postcrisis lows at a potentially unsafe rate. In an attempt to limit the flow of funds into risky mortgage loans, the government imposed limits on the maximum permissible loan-to-value (LTV) ratio and loan-to-income ratio (LTI) for new mortgages. These regulations became effective immediately upon their announcement and prevented the Irish banks from making loans that violated either the LTV or LTI requirements.
Crosignani and his coauthors were able to measure a large decline in loans that did not conform to the new requirements. However, they also find that a sharp increase in mortgage loans that conformed to the requirements largely offset this drop. Additionally, Crosignani and his coauthors find that the banks that were most exposed to the LTV and LTI requirements sought to recoup the lost income by making riskier commercial loans and buying greater quantities of risky securities. Their findings suggest that the regulations may have stopped higher-risk mortgage lending but that other changes in their portfolio at least partially undid the effect on banks' risk exposure.
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