Category Archives: Fintech

MAS Launches Global FinTech Hackcelerator for a Greener Financial Sector

The Monetary Authority of Singapore (MAS) announced the launch of the 6th edition of the Global FinTech Hackcelerator, with the theme “Harnessing Technology to Power Green Finance”. The competition, supported by Oliver Wyman, seeks to unlock the potential of FinTech in accelerating the development of green finance in Singapore and the region.

2.      FinTech firms and solution providers around the world are invited to submit innovative solutions to address over 50 problem statements that have been collected from financial institutions and green finance industry players. These problem statements focus on three key challenges: (i) Mobilising Capital; (ii) Monitoring Commitment; and (iii) Measuring Impact.

3.      Up to 15 finalists will be shortlisted for a virtual programme where they will be paired with a Corporate Champion [1] to develop customised prototypes on the API Exchange (APIX) [2] . Each finalist will also receive a S$20,000 cash stipend and be eligible for a fast-tracked application for the MAS Financial Sector Technology and Innovation Scheme Proof-of-Concept Grant of up to S$200,000.

4.      Finalists will pitch their solutions at the Demo Day held at this year’s Singapore FinTech Festival [3] . Up to three winners will be selected, with each receiving S$50,000 in prize money.

5.      Mr Sopnendu Mohanty, Chief FinTech Officer of MAS said, “Green FinTech can be an important enabler to accelerate Asia’s transition to a low carbon future. It can provide much needed innovative solutions, and develop the crucial technology stack, which can help promote green financial services, catalyse efficient allocation of green capital, and facilitate trust in the green data value chain. I encourage all innovators to make use of this platform and showcase their Green FinTech solutions to the world.”

6.      All FinTech firms and solution providers are encouraged to submit their applications for the MAS Global FinTech Hackcelerator here  by 11 June 2021.

***

  1. [1] Corporate Champions are teams from Singapore-based financial institutions or organisations that mentor finalists during the Hackcelerator, working with them to refine and contextualise the solution.
  1. [2] APIX (www.apixplatform.com ), a product of the ASEAN Financial Innovation Network, is a not-for-profit entity formed by the MAS, the International Finance Corporation and the ASEAN Bankers Association, with the objective of supporting financial innovation and inclusion around the world.
  1. [3] Singapore FinTech Festival is the world’s largest FinTech festival and a global platform for the FinTech community comprising FinTech players, technopreneurs, policy makers, financial industry leaders, investors including private equity players and venture capitalists and academics. It will be held on 8 to 12 November 2021.

DBS, J.P. Morgan and Temasek to establish platform to transform interbank value movements in a new digital era

Designed as an open platform to encourage broad participation by banks globally

Platform will leverage blockchain technology and digitise M11 commercial bank money to reduce current frictions and latency for cross-border payments, trade transactions and foreign exchange settlements

Acknowledging that the future of global payments is on the cusp of a fundamental shift, DBS, J.P. Morgan (NYSE: JPM) and Temasek today announced plans to develop an open industry platform to reimagine and accelerate value movements for payments, trade and foreign exchange settlement in a new digital era, through a newly-established technology company.

The company, Partior2, aims to disrupt the traditional cross-border payments ‘hub and spoke’ model, that has resulted in common pain points, including multiple validations on payment details by banks, which translate to costly and onerous post transaction exception handling and reconciliation activities. Partior recognises the need for more efficient digital clearing and settlement solutions across the banking industry, and targets to address these challenges through the use of blockchain solutions to enable next generation, programmable value transfer for participating banks and their clients in real-time across a common and open platform.

The Partior platform has also set its sights on developing wholesale payments rails based on digitised commercial bank money to enable “atomic” or instantaneous settlement of payments for various types of financial transactions. Such functionality would help banks overcome challenges presented by the current standard sequential method of processing global payments.

Piyush Gupta, Chief Executive Officer, DBS Bank, said: “The current hub and spoke arrangement in global payments often results in delays as confirmations from various intermediaries are needed before a settlement is treated as final. This in turn has a knock-on effect and creates inefficiencies in the final settlement of other assets. By harnessing the benefits of blockchain and smart contracts technology, the Partior platform will address current points of friction. The open platform will enable banks around the world to provide real-time cross-border multi-currency payments, trade finance, foreign exchange and DVP securities settlements on a world-class platform, with programmability, immutability, traceability built into its suite of services.”

Takis Georgakopoulos, Global Head of Wholesale Payments, J.P. Morgan, said: “Our newly formed business unit, Onyx by J.P. Morgan, is focused on providing clients with the best-in-class platforms as their business models and banking needs evolve over time. We believe a shared ledger infrastructure such as the Partior platform will change the way payments are cleared and settled, through this first-of-its-kind, wholesale payments rail based on digitised commercial bank money. After five years of being a partner in Project Ubin, we are thrilled by the launch of Partior as it marks yet another milestone for J.P. Morgan and the industry – blockchain-based wholesale payments infrastructure where information and value can change hands around the world in a 24/7, frictionless way. J.P. Morgan is committed to being a leader in this space as our clients transition towards multiple bank platforms, de-centralised networks and programmable money.”

Chia Song Hwee, Deputy CEO, Temasek, said: “We are pleased to work alongside DBS and J.P. Morgan to create a global platform that will have tangible impact on global payments. Partnerships such as this are important in galvanising fundamental changes. Finding the right approach to payments transformation using new technologies should be a priority as we take our existing infrastructure into the next stage of digitalisation and connectivity.

“We’re also heartened by the interest from other banks and partners, and look forward to welcoming them on board as this new platform builds out,” Mr Chia added.

Sopnendu Mohanty, Chief FinTech Officer, Monetary Authority of Singapore, said: “The launch of Partior is a global watershed moment for digital currencies, marking a move from pilots and experimentations towards commercialisation and live adoption. With its genesis from Project Ubin, a public-private partnership on blockchain and CBDC experimentation, Partior is a pioneering step towards providing foundational global infrastructure for transacting with digital currencies in a trusted environment, spurring a wide range of use-cases in the blockchain ecosystem.”

The operation of Partior by DBS, J.P. Morgan and Temasek and the completion of development, launch and availability of services on the proposed platform are subject to obtaining any required regulatory consents and approvals.

When complete, the platform aims to provide 24/7 infrastructure that will enable financial institutions and developers to co-create applications that support use cases such as FX Payment Versus Payment (PVP), Delivery Versus Payment (DVP) and Peer-to-Peer escrows to complement and value-add to global financial ecosystems.

To encourage broad participation across the banking industry, Partior will be actively engaging leading banks to join the platform to establish the scale required to benefit the industry.

The platform will start with a focus on facilitating flows primarily between Singapore-based banks in both USD and SGD, with the intent to expand service offerings to other markets and in various currencies. Partior’s platform will also be designed to complement ongoing Central Bank Digital Currencies (CBDCs) initiatives and use cases.

These efforts by DBS, J.P. Morgan and Temasek build on their past work as part of Project Ubin3, an industry initiative by the Monetary Authority of Singapore to explore the application of blockchain technology involving multi-currency payments and settlements.

Philip Lowe: Opening remarks at the Melbourne Business Analytics Conference

Good morning and welcome to this year’s Business Analytics Conference.

I am very pleased to be able to join you, not least because of the theme of this year’s conference: Driving Recovery and Growth through Data Analytics. This theme brings together 2 issues that are very close to my heart – the recovery of the Australian economy from the pandemic and the critical role that investment in IT and data can play in sustaining that recovery. So I congratulate you on your choice of topic and I look forward to hearing your ideas.

The challenges facing us all are large. At the Reserve Bank, we are seeking to support the economic recovery and a stronger labour market that is consistent with achieving the inflation target. And most of you at this conference are seeking to find new ways of using data to help businesses and organisations innovate, compete and succeed.

These challenges are complementary. We will each be more successful if the other is successful. A stronger economy will provide businesses with the confidence and the resources to make the investments that are needed for our future. And conversely, our economy will be stronger because of your work, since the best decisions are those based on data, evidence and analysis. So our causes are linked.

I will come back to this idea, but first a few words about the economic recovery.

As a nation, we have responded very well to the pandemic. Australians have pulled together and been prepared to do what is necessary to contain the virus and support one another. Businesses have adapted quickly and innovated, with many making more progress on the digital front in a matter of months than they would have made in years. Governments also responded quickly and decisively, with extensive income support, increased spending on infrastructure and a large wage subsidy program. And monetary policy has also helped, reducing the cost of borrowing to historically low levels and supporting the supply of credit.

The result has been a quicker and stronger economic recovery than was expected. In the December quarter, GDP increased by 3.1 per cent and we are now within striking distance of the pre-pandemic level of GDP. The number of people in jobs has also almost returned to the level before the pandemic. Looking across the range of indicators, Australia is doing much better than most other advanced economies.

This, however, does not hide the fact that we still have a long way to go. The unemployment rate of 6.4 per cent is too high and the economy is operating well short of its capacity. Inflation and wages growth are also both lower than we would like. While we are expecting further progress to be made towards full employment and the inflation target, it is going to take some time before we reach our goals.

One piece of the recovery that is yet to click into gear is business investment. Understandably, last year many firms deferred their investment plans and sought to reduce risk on their balance sheets. Late in the year there was a welcome pick-up in investment in machinery and equipment, but there is still a long way to go to get back to the level of investment before the pandemic, which itself was low by historical standards. If we are to have a strong and durable recovery, it is important that the recovery in business investment continues and broadens.

Looking across the economy, there are investment needs and opportunities in many areas. The one I would like to focus on today is investment in IT, digitisation and data science. Investment in these areas is critical to lifting our nation’s productive capacity.

In many ways data is the new oil of the 21st century. Investing in data and our digital capability are critical to our future prosperity. These investments allow better decision making and a faster response to the changes in our economy and society. These investments are also crucial to organisations delivering the more personalised goods and services that many people are seeking.

There are opportunities for digital innovation in every sector of our economy. Almost every organisation needs a strong digital capability to perform well, to innovate and lift their productivity. Technology and data analysis also hold the keys to solving many of the great challenges of our times, including controlling the pandemic, dealing with climate change and responding to increasing cyber threats. This all means that the discussions you are having at this conference are really important.

If, as a nation, we are to capitalise on your work and the growing opportunities, we need to keep investing in the skills and knowledge of our people. This conference is a good example of this investment. Developing a strong digital workforce with skills in areas like predictive analytics, machine learning and artificial intelligence is just as important as investing in the hardware and software needed to support the digital economy. As part of our journey we also need to think about how our organisations function and make decisions, so that our people can work in more agile and flexible ways as they grapple with complex problems. It is by investing in both physical and human capital that we can boost our productivity, create employment and drive Australia’s future prosperity.

The importance of investing in the digital economy has been recognised by our governments. The Australian Government has a strong focus on this and is making additional investments in skills and training, streamlining regulatory processes and strengthening the nation’s cyber security. The consumer data right, to give consumers greater access to and control over their data, will also help. This access has started with open banking, which will make it easier for Australians to switch between financial institutions and access financial products that better suit their needs. In time, Australians will benefit from this being extended to other areas.

At the RBA, we are also investing significantly in digital infrastructure and data. The importance of this to us is reflected in the decision to make ‘harnessing the power of data’ one of our internal strategic focus areas for the next few years.

We view data as a strategic asset, and are investing in the processes, technology and people to enhance the value we get from data. We have established an enterprise data office with responsibility for data management, for ensuring that our staff have the right skills and that we are using leading data technologies and methods in our analysis. This includes the use of machine learning and ‘big data’.

We are seeing the benefits from this focus on data in our analysis of the economy and financial system. For example, the Bank’s staff use loan-level large datasets from securitisations to better understand developments in the market for housing loans and use detailed settlement data to measure bond market liquidity. They also use machine learning techniques to extract measures of sentiment from news articles as an economic indicator.[1] And during the pandemic, we have been able to access and analyse a broader range of data to obtain real-time readings of economic conditions in a way that wasn’t possible in the past.

At the RBA, we also see the power of new technologies and data in our central banking operations. The RBA has played a significant role in building the New Payments Platform (NPP), a critical piece of national infrastructure, which enables us all to make fast payments on a 24/7 basis. As a provider of banking services to the Australian Government, the RBA has been working with its government banking clients as they modernise their payment systems using the NPP. As an example, Services Australia now routinely uses the NPP to make emergency welfare and disaster relief payments in real time to Australians in need. Payment messages through the NPP can also carry richer data, opening up opportunities for more efficient business processes and new digital services in the future. As an example of this, NPP will be able to support the adoption of e-invoicing, which will lower the cost of doing business.

Another example where technology and data are opening up new possibilities is in the area of digital currencies. The RBA is conducting research on the technologies and policy implications of a potential wholesale central bank digital currency. This could use distributed ledger technology to support the settlement of transactions in the interbank payment system. Some of this work is taking place in the RBA’s in-house Innovation Lab, where we are collaborating with external parties on a proof-of-concept. We look forward to sharing more details in due course.

The Bank and the Payments System Board are also strongly supportive of forms of digital identity that can be used in both the public and private sector. An effective system of digital identity is important in promoting competition, security and innovation in the digital economy. The Australian Government is also supporting digital identity services for conveniently and securely accessing government services online.

I would like to conclude by returning to the idea that the challenges facing the RBA and those of you attending this conference are complementary.

The RBA is doing what it can to support the recovery from the pandemic and will maintain that support until we have achieved our goals for full employment and inflation. A strong economy will make for a more conducive environment for investments in data and technology. Similarly, your investments in data, technology and human capital will help make the economy stronger and more dynamic. We need these investments to develop the industries of the future and to equip Australians with the skills needed for that future. Australia needs your ideas, your ingenuity and your energy so that organisations across our country can seize the opportunities that will help deliver our future prosperity.

I wish you the best for the conference and look forward to your insights on how we can best drive the recovery and growth through investment in data analytics.

Thank you.

Vitas Vasiliauskas: Introductory remarks – Cyprus Annual Banking Conference and FinTech EXPO

Introductory remarks by Mr Vitas Vasiliauskas, Chairman of the Board of the Bank of Lithuania, at the Cyprus Annual Banking Conference and FinTech EXPO, 15 January 2021.

Good morning, dear listeners, fellow Governors,

I would like to thank the Governors for their insights in this panel, and also the organisers of the conference for inviting me.

Discussing the role of banks in the time of COVID-19 is indeed vital. Banking is as important to the economy as the heart is to the human body.

And just like cardiovascular diseases, which are the leading cause of mortality in Europe, diseases of the banking sector often stand behind the deepest crises in our history. In 1930s, the Great Depression was significantly amplified by bank runs. In 2008, subprime mortgage loans extended by banks created the US housing bubble that brought down the entire global financial system. In Europe, a heavily bank-based system, the infamous sovereign-bank nexus resulted in a prolonged European sovereign debt crisis.

The COVID-19 crisis was not, of course, caused by the banking system. But it could have greatly amplified the shock if it was not resilient enough. Moreover, a weak banking system would diminish prospects of a sustained recovery.

This is why the role of banks is so critical in the current context. In this light, I would like to make three points in my brief intervention:

· First, we learnt our lessons from the previous crisis – and this contributed to banks’ resilience during the current one.

· Second, swift regulatory responses to such shocks as COVID-19 can further help the banking system support the shaken economy.

· And third, a truly sustainable long-term economic recovery after this crisis depends on solving equally long-term issues in the European banking system.

Let us begin with the resilience of banks which has so far prevented a health crisis from turning into a full-blown systemic financial crisis. The Basel III reforms made sure that the global banking system was significantly better capitalised than at the onset of the global financial crisis. The Common Equity Tier 1 (CET1) ratio in the EU banking sector – a key indicator of financial soundness – rose from 9% in 2009 to nearly 15% in the fourth quarter of 2019, before the COVID-19 crisis hit. With this amount of capital, banks can generally continue their lending to the economy even if truly adverse scenarios materialise, as shown by the ECB’s vulnerability analysis published in July 2020.

This shows that in the field of banking regulation, we did not waste the previous crisis and learnt our lessons well.

My second point relates to regulatory response. The relief package that the ECB Banking Supervision announced in March was designed to ensure that banks can keep lending to the contracting economy. For instance, banks were allowed to temporarily operate below the level of capital defined by the bank-specific Pillar 2 capital requirements, namely Pillar II guidance.

In Lithuania, macroprudential policy was a key domain of the regulatory response package. Prior to 2020, critics would say that our macroprudential set-up was perhaps too wide-ranging. But this crisis showed, I believe, that our policy stance was the right one.

First, it helped prevent a deterioration in lending standards and a build-up of systemic risk in the run up to the COVID-19 shock. And second, when the time came, we implemented counter-cyclical policy decisions in line with the intended functioning of the framework. For instance, in mid-March, the Bank of Lithuania fully released the counter-cyclical capital buffer from 1% to 0%.

Overall, the relaxation of various requirements has increased the lending potential of banks in Lithuania by €2 billion, or by a third, compared to 2019. Of course, we are talking about potential here, not the real world. But the real-world data has been encouraging, at least in terms of lending to households, which has broadly returned to the pre-pandemic levels.

Going forward, policymakers should not “waste a good crisis” this time as well, and take a fresh look at the existing macroprudential framework. We could even consider novel ways of applying macroprudential tools – such as the application of borrower-based measures during the cycle, e.g. relaxing the loan-to-value or debt-service-to-income requirements in times of crisis.

Finally, I would like to make a point on the long-term issues of the European banking sector. The first issue here is inadequate bank profitability. In this regard, there is a need for consolidation via mergers and acquisitions to make the European banking sector leaner. Completing the Banking Union by creating a European Deposit Insurance Scheme (EDIS) would open doors for true cross border consolidation in Europe.

The second issue is non-performing loans (NPLs) – a long-standing problem in the European banking sector. To tackle the potential rise in NPLs, the next round of government support should put more emphasis on solvency rather than liquidity support. In my opinion, the EU should return to the idea of establishing a European Solvency Support Instrument. There is also a great need of convergence of various insolvency frameworks across the Member States.

Tackling these issues would allow banks to keep lending for a sustained recovery.

Joachim Wuermeling: Combining stability and innovation Bundesbank and fintech players in the digital financial ecosystem

Financial technology. Keynote speech by Prof Joachim Wuermeling, Member of the Executive Board of the Deutsche Bundesbank, at the Plug and Play Fintech Europe Expo 2021, 28 January 2021.

1 Introduction

Ladies and gentlemen, dear colleagues,

Good afternoon, and a very warm welcome from the Deutsche Bundesbank. I am delighted to be taking part in the Plug and Play “Fintech Europe Expo” today. As a central banker, this event offers me a unique opportunity to liaise with you and is certainly my first highlight of 2021. I look forward to a pleasant afternoon and an inspiring conference!

An innovative spirit has made financial technology the driving force transforming the world of finance. This technology has now become an essential part of banking, and therefore also central banking.

Looking at our virtual audience today, I see start-ups, venture capitalists, and consultants. To be honest, when you think of central banks, “innovation” might not exactly be the first word that springs to mind. Instead, your first thought might – hopefully – be “stability”.

But take it from me: stability and innovation are not mutually exclusive. In fact, they go hand in hand. Technological progress has always shaped the tasks and activities of central banks.

Although fintechs and central banks cooperate already, I believe they should do so on a much closer basis in the future. And that is why I am so happy to have the chance to speak to you here today.

I would therefore encourage you to keep an eye out for the digital activities of central banks! Here are three reasons why you should:

2 Central banks are relevant actors for digital innovation in finance

All fintech innovation is connected to central banks’ activities in some way or another.

For centuries, central banks have been the backbone of the financial system, issuing currency, providing payment infrastructures, and ensuring that banks are stable.

Digital transformation is relevant to us in two regards. The first is that we need to understand the technical progress in the industry when using our tools, whether it be in the area of monetary policy or in banking supervision. The second is that we make use of new technologies ourselves, be it in analytics, processes or products.

The actions we take might be highly relevant to you, your products, your business cases, or your ideas: Would it matter to you if we issued a digital currency, based gross settlement on a ledger, or if, as banking supervisors, we were to restrict the use of machine learning?

The impact central banks have on innovation is particularly relevant to the euro area, a major currency area which currently covers 19 countries and 350 million people. The Bundesbank, as the biggest central bank in the block, focuses on its role as an integral part of the Eurosystem.

3 Digital innovation can foster financial stability

Besides ensuring the stability of the currency, central banks’ main task is to safeguard the stability of the financial system as a whole. I am sure you will all agree when I say that new technology can simultaneously promote stability and bring about new risks.

As a central bank, we have to play a dual role. On the one hand, we want to enable digital innovation, harnessing its potential for financial stability. On the other, we have to keep an eye on potential risks arising from digital transformation. It is our job to supervise all risks to financial stability.

Be assured that central banks are looking out for this. If I could make one wish here, it would be for digital innovation to factor in financial stability by design.

The COVID-19 crisis is certainly providing an additional boost to digitalisation, particularly in the financial sector. But let me be clear: increasing use of online banking, digital payments and video chats with clients are by no means the digital transformation of the future. This is no more than the adoption of technologies invented in the last century.

But the pandemic might encourage all players in the financial system to pursue innovation more quickly and more radically. Indeed, the digital revolution is still to come in finance as well as in other industries.

Artificial intelligence, machine learning, cloud technology, distributed ledger, quantum computing, an ever-growing volume of data – whatever buzzword you name: all of these have the potential to disrupt each and every business model. And, as we learned recently, including that of central banks.

I believe that we are at the beginning rather than at the end of digitalising the financial system. The magic moment for financial technology and fintech companies may still be to come.

New technologies have the potential to make the whole financial system more resilient. For instance, financial institutions can use AI to better detect and ward off cyber-attacks. Early warning systems for loan defaults based on automatically evaluated economic news could improve risk management.

Digital innovation can enhance the stability of individual institutions and of the entire financial system. If we manage to strengthen financial stability through digital innovation, this could make supervision and central banking more effective and more efficient.

4 Central banks’ digital innovation activities

Central banks are themselves innovators in the financial system – but we need your imagination. A digital approach to finance is not merely limited to private actors. It extends to the entire financial ecosystem. And central banks are joining in, too.

All around the world, we are exploring the risks and benefits of issuing central bank digital currency. The Eurosystem is looking into different concepts for a “digital euro”.

Real-time monitoring of the financial system is a fascinating vision for central banks. Just recently, at the “Bundesbank Innovation Challenge”, 10 start-ups pitched for the most innovative solution for risk monitoring at our institution.

Innovation needs space – mental space, certainly, but also physical space. We at the Bundesbank are currently building InnoWerk, a collaborative workspace in the city centre of Frankfurt not only for our staff, but also for the international central bank community. Together with Banque de France and the European Central Bank, we are establishing one of nine global BIS Innovation Hubs around the globe.

But InnoWerk will not be limited to the central bank community. We wish to involve the broader ecosystem – with you, players from the start-up scene, experts from academia and any other creative minds. We want to make this a win-win situation in which the Bundesbank benefits from technology and creativity from outside, while start-ups become familiar with the world of central banking.

It is safe to say that we are not and will not become a fintech. But we are constantly reaching out to the digital finance community.

5 Conclusion

Now, you might be wondering: “what’s in it for me?” Yes, central banks are relevant to digital innovation in finance – my first point. Yes, digital innovation can foster financial stability – my second point. And yes, central banks are innovators themselves – my third point.

Beyond this, I hope you all agree that digitalisation is not just about making money; it is about making the world a better place, too. For me as a central banker, a better world means a financial system which is sufficiently and sustainably stable.

I am convinced that digital innovation will make us better able contain the threats and vulnerabilities in the financial system. All of you can contribute to this – and I very much hope you will!

The transformation of the financial system may not only make finance more digital, but also more stable and more resilient.

To that end, cooperation between central banks as Bundesbank and the fintech community is key. We are interested in establishing close ties to the digital financial ecosystem.

We might come from different worlds. But we can – and should – learn from each other and work together. Let us bring together stability and innovation!

I am looking forward to an inspiring event today, with insightful panel discussions and many shared ideas. And I hope to see some of you at InnoWerk soon!

New Gemini Credit Card with Crypto Rewards

Gemini, a crypto exchange and custodian, today announced that it will launch the Gemini Credit Card, a credit card with cryptocurrency rewards. This effort has been accelerated by the acquisition of Blockrize, a fintech startup that has been building a credit card with cryptocurrency rewards. In preparation for launch later this year, Gemini has opened the Gemini Card waitlist — providing Gemini customers, and those already on the Blockrize waitlist, with early access.

By combining Gemini’s simple, reliable, and safe platform with Blockrize’s rewards program, card holders will be able to seamlessly earn up to 3 percent back in bitcoin, or other cryptos, on every purchase they make with the Gemini Credit Card.

“The Gemini Credit Card will make it easier for any consumer to invest in bitcoin and other cryptos without changing their existing behavior, ” said Tyler Winklevoss, CEO of Gemini. “Rather than deciding how and when to buy crypto, customers can do so when making their everyday purchases. We’re excited to welcome the Blockrize team to Gemini and work together to continue to mainstream crypto.”

Those who join the waitlist, and the more than 10,000 people already on the Blockrize waitlist, will get early access. The Gemini Card will work like a traditional credit card. It will be available to U.S. residents in every state and will be widely accepted wherever major cards are accepted. Rewards will be automatically deposited into a cardholder’s Gemini account.

For Gemini users or others interested in signing up to the waitlist, please visit: https://gemini.com/credit-card/waitlist. To sign up for a Gemini account visit: https://exchange.gemini.com/register.

This is Gemini’s second acquisition, following its acquisition of Nifty Gateway in November of 2019. Gemini continues to look for companies that align with its values and mission to empower the individual through crypto.

About Gemini

Gemini Trust Company, LLC (Gemini) is a cryptocurrency exchange and custodian that allows customers to buy, sell, and store more than 30 cryptocurrencies like bitcoin, bitcoin cash, ether, litecoin, and Zcash. Gemini is a New York trust company that is subject to the capital reserve requirements, cybersecurity requirements, and banking compliance standards set forth by the New York State Department of Financial Services and the New York Banking Law. Gemini was founded in 2014 by twin brothers Cameron and Tyler Winklevoss to empower the individual through crypto.

Reserve Bank of New Zealand committed to action as it responds to data breach

The Governor of the Reserve Bank of New Zealand, Adrian Orr, says the recent malicious and illegal breach of a file sharing application used by the Bank is significant, and has our full attention.

Mr Orr says New Zealand’s financial system and institutions remain sound, and Te Pūtea Matua is open for business. The standalone File Transfer Application system that was breached has been secured and closed.

“We apologise unreservedly to all of those impacted by the breach. Personally, I own this issue and I am disappointed and sorry,” Mr Orr says.

“Our investigation makes it clear we are dealing with a significant data breach. While a malicious third party has committed the crime, and we believe service provisions have fallen short of our agreement, the Bank has also fallen short of the standards expected by our stakeholders.”

A detailed forensic cyber investigation is underway and RBNZ is working directly with affected stakeholders whose information may have been breached.

“We recognise the public interest in this incident and we acknowledge there are serious questions that need to be answered about how this incident occurred and how to strengthen our systems and processes,” says Mr Orr.

“In addition to the forensic cyber investigation currently underway, we have appointed an independent third party to undertake a comprehensive general review of this incident. We will be as transparent and clear as possible as this progresses, and will release the review’s terms of reference shortly.”

“Our immediate focus is on working directly with system users and those who may have had their information compromised. It is a complex process and accuracy and security are important. As our investigations progress, we are prioritising direct engagement with institutions and individuals affected. We thank stakeholders for their patience and understanding.

“Be assured, we are taking action. We are working closely with public authorities and utilising international experts as we respond. We are doing so in a whole of Government framework, utilising the National Security System.”

“We are not in a position to provide further details on the investigation at this time as it could adversely affect the investigation and the steps being taken to mitigate the breach,” says Mr Orr.

Ongoing updates on the investigation process will be provided via the Reserve Bank Data Breach Response page, and email service.

Brian P. Brooks to Step Down, Blake Paulson to Become Acting Comptroller of the Currency on Jan 14, 2021

Acting Comptroller of the Currency Brian P. Brooks today announced he will step down on January 14, 2021, and pursuant to 12 USC 4, Chief Operating Officer Blake Paulson will become Acting Comptroller of the Currency.

“It has been a great honor to serve the United States as Acting Comptroller of the Currency,” Acting Comptroller Brooks said. “The Office of the Comptroller of the Currency (OCC) is the most extraordinary of federal agencies filled with the most dedicated, professional, and gifted staff any executive can hope to have. I am extremely proud of what we have accomplished together through what have been extraordinary times by any measure.”

During his eight months as Acting Comptroller, the OCC acted swiftly to provide relief and support to national banks and federal savings associations so they could use their strength to help consumers, businesses, and communities through the COVID-19 pandemic. It promoted greater financial access and economic opportunity by eliminating regulatory uncertainty regarding valid-when-made and true-lender rules. The agency also continued to implement its new Community Reinvestment Act rule to promote more investment, lending, and services where they are needed most.

In addition, the agency enhanced the relevance and value of the federal charter and helped ensure the federal banking system can evolve to meet the changing demands of consumers and markets by clarifying bank and thrift authorities regarding certain activities related to crypto assets and continuing to defend our authority to charter companies engaged in the business of banking with business models that focus on serving customers in new and specific ways.

“The actions we took as a team will help ensure the federal banking system operates in a safer, sounder, and fairer manner for decades to come,” Mr. Brooks said.

Chief among the initiatives launched during Acting Comptroller Brooks’ tenure is Project REACh. Project REACh includes active participation of bankers, civil rights leaders, and technologists working at national and regional levels to identify and reduce barriers that prevent underserved and minority populations from participating fully and fairly in our economy. “The movement demonstrates the good the agency can do by convening hearts and minds and aligning them to a common cause,” Brooks said. “I applaud the agency, the industry, and stakeholders for coming together for this important project—all the more so since, as the events of January 6 demonstrate, the country’s need to come together has never been greater.”

“The nation and the federal banking system are fortunate to have such a stable, capable hand like Blake Paulson to step in and guide the agency with the other Executive Committee members until the next Comptroller is nominated and confirmed,” Brooks added.

Mr. Paulson is a career bank examiner and has served as Chief Operating Officer since June 2020. In this role, Mr. Paulson oversaw OCC bank supervision and OCC management operations, as well as staff responsible for Systemic Risk Identification Support and Specialty Supervision, and Supervision System and Analytical Support.

Prior to this role, Mr. Paulson was responsible for supervising nearly 1,100 national banks and federal savings associations, as well as nearly 1,600 OCC employees as Senior Deputy Comptroller for Midsize and Community Bank Supervision. During his long career at the OCC, he has held a variety of other bank supervision and leadership roles involving banks of all size.

Mr. Blake Paulson joined the OCC in 1986 in Sioux Falls, South Dakota and is a graduate of the University of South Dakota.

Supporting Responsible Use of AI and Equitable Outcomes in Financial Services

Governor Lael Brainard

At the AI Academic Symposium hosted by the Board of Governors of the Federal Reserve System, Washington, D.C. (Virtual Event)

Today’s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve’s broader effort to understand AI’s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1

The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task for which the AI field of machine learning is particularly well-suited.2 The journal Nature recently reported the development of an AI network which could “vastly accelerate efforts to understand the building blocks of cells and enable quicker and more advanced drug discovery” by accurately predicting a protein’s 3-D shape from its amino acid sequence.3

Application of AI in Financial Services

In November 2018, I shared some early observations on the use of AI in financial services.4 Since then, the technology has advanced rapidly, and its potential implications have come into sharper focus. Financial firms are using or starting to use AI for operational risk management as well as for customer-facing applications. Interest is growing in AI to prevent fraud and increase security. Every year, consumers bear significant losses from frauds such as identity theft and imposter scams. According to the Federal Trade Commission, in 2019 alone, “people reported losing more than $1.9 billion to fraud,” which represents a mere fraction of all fraudulent activity banks encounter.5 AI-based tools may play an important role in monitoring, detecting, and preventing such fraud, particularly as financial services become more digitized and shift to web-based platforms. Machine learning-based fraud detection tools have the potential to parse through troves of data—both structured and unstructured—to identify suspicious activity with greater accuracy and speed, and potentially enable firms to respond in real time.

Machine learning models are being used to analyze traditional and alternative data in the areas of credit decisionmaking and credit risk analysis, in order to gain insights that may not be available from traditional credit assessment methods and to evaluate the creditworthiness of consumers who may lack traditional credit histories.6 The Consumer Financial Protection Bureau has found that approximately 26 million Americans are credit invisible, which means that they do not have a credit record, and another 19.4 million do not have sufficient recent credit data to generate a credit score. Black and Hispanic consumers are notably more likely to be credit invisible or to have an unscored record than White consumers.7 The Federal Reserve’s Federal Advisory Council, which includes a range of banking institutions from across the country, recently noted that nontraditional data and the application of AI have the potential “to improve the accuracy and fairness of credit decisions while also improving overall credit availability.”8

To harness the promise of machine learning to expand access to credit, especially to underserved consumers and businesses that may lack traditional credit histories, it is important to be keenly alert to potential risks around bias and inequitable outcomes. For example, if AI models are built on historical data that reflect racial bias or are optimized to replicate past decisions that may reflect bias, the models may amplify rather than ameliorate racial gaps in access to credit. Along those same lines, the opaque and complex data interactions relied upon by AI could result in discrimination by race, or even lead to digital redlining, if not intentionally designed to address this risk. It is our collective responsibility to ensure that as we innovate, we build appropriate guardrails and protections to prevent such bias and ensure that AI is designed to promote equitable outcomes. As Rayid Ghani notes, “…[A]ny AI (or otherwise developed) system that is affecting people’s lives has to be explicitly built to focus on increasing equity and not just optimizing for efficiency…[W]e need to make sure that we put guidelines in place to maximize the chances of the positive impact while protecting people who have been traditionally marginalized in society and may be affected negatively by the new AI systems.”9

Black Box Problems

Recognizing the potential and the pitfalls of AI, let us turn to one of the central challenges to using AI in financial services—the lack of model transparency. Some of the more complex machine learning models, such as certain neural networks, operate at a level of complexity that offers limited or no insight into how the model works. This is often referred to as the “black box problem,” because we can observe the inputs the models take in, and examine the predictions or classifications the model makes based on those inputs, but the process for getting from inputs to outputs is obscured from view or very hard to understand.

There are generally two reasons machine learning models tend toward opacity. The first is that an algorithm rather than a human being “builds” the model. Developers write the initial algorithm and feed it with the relevant data, but do not specify how to solve the problem at hand. The algorithm uses the input data to estimate a potentially complex model specification, which in turn make predictions or classifications. As Michael Tyka puts it, “[t]he problem is that the knowledge gets baked into the network, rather than into us. Have we really understood anything? Not really—the network has.”10 This is somewhat different from traditional econometric or other statistical models, which are designed and specified by humans.

The second is that some machine learning models can take into account more complex nonlinear interactions than most traditional models in ways that human beings would likely not be able to identify on their own.11 The ability to identify subtle and complex patterns is what makes machine learning such a powerful tool, but that complexity often makes the model inscrutable and unintuitive. Hod Lipson likens it to “meeting an intelligent species whose eyes have receptors [not] just for the primary colors red, green, and blue, but also for a fourth color. It would be very difficult for humans to understand how the alien sees the world, and for the alien to explain it to us.”12

The Importance of Context

While the black box problem is formidable, it is not, in many cases, insurmountable. The AI research community has made notable strides in explaining complex machine learning models—indeed, some of our symposium panelists have made major contributions to that effort. One important conclusion of that work is that there need not be a single principle or one-size-fits-all approach for explaining machine learning models. Explanations serve a variety of purposes, and what makes a good explanation depends on the context. In particular, for an explanation to “solve” the black box problem, it must take into account who is asking the question and what the model is predicting.

So what do banks need from machine learning explanations? The requisite level and type of explainability will depend, in part, on the role of the individual using the model. The bank employees that interact with machine learning models will naturally have varying roles and varying levels of technical knowledge. An explanation that requires the knowledge of a PhD in math or computer science may be suitable for model developers, but may be of little use to a compliance officer, who is responsible for overseeing risk management across a wide swath of bank operations.

The level and type of explainability also depends on the model’s use. In the consumer protection context, consumers’ needs and fairness may define the parameters of the explanation. Importantly, consumer protection laws require lenders who decline to offer a consumer credit—or offer credit on materially worse terms than offered to others—to provide the consumer with an explanation of the reasons for the decision. That explanation serves the important purposes of helping the consumer to understand the basis of the determination as well as the steps the consumer could take to improve his or her credit profile.13

Additionally, to ensure that the model comports with fair lending laws that prohibit discrimination, as well as the prohibition against unfair or deceptive practices, firms need to understand the basis on which a machine learning model determines creditworthiness. Unfortunately, we have seen the potential for AI models to operate in unanticipated ways and reflect or amplify bias in society. There have been several reported instances of AI models perpetuating biases in areas ranging from lending and hiring to facial recognition and even healthcare. For example, a 2019 study by Science revealed that an AI risk-prediction model used by the U.S. healthcare system was fraught with racial bias. The model, designed to identify patients that would likely need high-risk care management in the future, used patients’ historical medical spending to determine future levels of medical needs. However, the historical spending data did not serve as a fair proxy, because “less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients.”14 Thus, it is critical to be vigilant for the racial and other biases that may be embedded in data sources.

It is also possible for the complex data interactions that are emblematic of AI—a key strength when properly managed—to create proxies for race or other protected characteristics, leading to biased algorithms that discriminate. For example, when consumers obtain information about credit products online, the complex algorithms that target ads based on vast amounts of data, such as where one went to school, consumer likes, and online browsing habits, may be combined in ways that indicate race, gender, and other protected characteristics.15 Even after one online platform implemented new safeguards pursuant to a settlement to address the potential exclusion of consumers from seeing ads for credit products based on race, gender, or other protected characteristics, Professor Alan Mislove and his collaborators have found that the complex algorithms may still result in bias and exclusion.16 Therefore, it is important to understand how complex data interactions may skew the outcomes of algorithms in ways that undermine fairness and transparency.

Makada Henry-Nickie, notes that “…[I]t is of paramount importance that policymakers, regulators, financial institutions, and technologists critically examine the benefits, risks, and limitations of AI and proactively design safeguards against algorithmic harm, in keeping with societal standards, expectations, and legal protections.”17 I am pleased that the symposium includes talks from scholars who are studying how we can design AI models that avoid bias and promote financial inclusion. No doubt everyone here today who is exploring AI wants to promote financial inclusion and more equitable outcomes and ensure that it complies with fair lending and other laws designed to protect consumers.

In the safety and soundness context, bank management needs to be able to rely on models’ predictions and classifications to manage risk. They need to have confidence that a model used for crucial tasks such as anticipating liquidity needs or trading opportunities is robust and will not suddenly become erratic. For example, they need to be sure that the model would not make grossly inaccurate predictions when it confronts inputs from the real world either that differ in some subtle way from the training data or that are based on a highly complex interaction of the data features. In short, they need to be able to have confidence that their models are robust. Explanations can be an important tool in providing that confidence.

Not all contexts require the same level of understanding of how machine learning models work. Users may, for example, have a much greater tolerance for opacity in a model that is used as a “challenger” to existing models and simply prompts additional questions for a bank employee to consider relative to a model that automatically triggers bank decisions. For instance, in liquidity or credit risk management, where AI may be used to test the outcomes of a traditional model, banks may appropriately opt to use less transparent machine learning systems.

Forms of Explanations

Researchers have developed various approaches to explaining machine learning models. Often, these approaches vary in terms of the type of information they can provide about a model. As banks contemplate using these tools, they should consider what they need to understand about their models relative to the context, in order to determine whether there is sufficient transparency in how the model works to properly manage the risk at issue.

Not all machine learning models are a black box. In fact, some machine learning models are fully “interpretable” and therefore may lend themselves to a broader array of use cases. By “interpretable” I mean that developers can “look under the hood” to see how those models make their predictions or classifications, similar to traditional models. They can examine how much weight the model gives to each data feature, and how it plays into a given result. Interpretable machine learning models are intrinsically explainable.

In the case of machine learning models that are opaque, and not directly interpretable, researchers have developed techniques to probe these models’ decisions based on how they behave. These techniques are often referred to as model agnostic methods, because they can be used on anymodel, regardless of the level of explainability. Model agnostic methods do not access the inner workings of the AI model being explained. Instead, they derive their explanations post hoc based on the model’s behavior: essentially, they vary inputs to the AI model, and analyze how the changes affect the AI model’s outputs.18 In effect, a model agnostic method uses this testing as data to create a model of the AI model.19

While post hoc explanations generated by model agnostic methods can allow inferences to be drawn in certain circumstances, they may not always be accurate or reliable, unlike intrinsic explanations offered by interpretable models. Basing an explanation on a model’s behavior rather than its underlying logic in this way may raise questions about the explanation’s accuracy, as compared to the explanations of interpretable models. Still, such explanations may be suitable in certain contexts. Thus, one of the key questions banks will face is when a post hoc explanation of “black box” model is acceptable versus when an interpretable model is necessary.

To be sure, having an accurate explanation for how a machine learning model works does not by itself guarantee that the model is reliable or fosters financial inclusion. Time and experience are also significant factors in determining whether models are fit to be used. The boom-bust cycle that has defined finance for centuries should make us cautious in relying fully for highly consequential decisions on any models that have not been tested over time or on source data with limited history, even if in the age of big data, these data sets are broad in scope.

Expectations for Banks

Recognizing that AI presents promise and pitfalls, as a banking regulator, the Federal Reserve is committed to supporting banks’ efforts to develop and use AI responsibly to promote a safe, fair, and transparent financial services marketplace. As regulators, we are also exploring and understanding the use of AI and machine learning for supervisory purposes, and therefore, we too need to understand the different forms of explainability tools that are available and their implications. To ensure that society benefits from the application of AI to financial services, we must understand the potential benefits and risks, and make clear our expectations for how the risks can be managed effectively by banks. Regulators must provide appropriate expectations and adjust those expectations as the use of AI in financial services and our understanding of its potential and risks evolve.20

To that end, we are exploring whether additional supervisory clarity is needed to facilitate responsible adoption of AI. It is important that we hear from a wide range of stakeholders—including financial services firms, technology companies, consumer advocates, civil rights groups, merchants and other businesses, and the public. The Federal Reserve has been working with the other banking agencies on a possible interagency request for information on the risk management of AI applications in financial services. Today’s symposium serves to introduce a period of seeking input and hearing feedback from a range of external stakeholders on this topic. It is appropriate to be starting with the academic community that has played a central role in developing and scrutinizing AI technologies. I look forward to hearing our distinguished speakers’ insights on how banks and regulators should think about the opportunities and challenges posed by AI.


1. I am grateful to Kavita Jain, Jeff Ernst, Carol Evans, and Molly Mahar of the Federal Reserve Board for their assistance in preparing this text. These remarks represent my own views, which do not necessarily represent those of the Federal Reserve Board or the Federal Open Market Committee. Return to text

2. David Rolnick, et al., “Tackling Climate Change with Machine Learning (PDF),” ; Sarah Castellanos, “Climate Researchers Enlist Big Cloud Providers for Big Data Challenges,” The Wall Street Journal, November 25, 2020, https://www.wsj.com/articles/climate-researchers-enlist-big-cloud-providers-for-big-data-challenges-11606300202. Return to text

3. Ewen Callaway, “‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures,” Nature 588 (November 30, 2020): 203–204, https://www.nature.com/articles/d41586-020-03348-4. Return to text

4. Lael Brainard, “What Are We Learning about Artificial Intelligence in Financial Services? (remarks at Fintech and the New Financial Landscape, Philadelphia, Pennsylvania, November 13, 2018). Return to text

5. Federal Trade Commission, Consumer Sentinel Network, Data Book 2019 (PDF), (Washington: Federal Trade Commission, January 2019). Return to text

6. See Board of Governors of the Federal Reserve System et al., “Interagency Statement on the Use of Alternative Data in Credit Underwriting (PDF).” Return to text

7. Kenneth P. Brevoort, Philipp Grimm, and Michelle Kambara, Data Point: Credit Invisibles (PDF) (Washington: Consumer Financial Protection Bureau, May 2015). Return to text

8. Federal Advisory Council (FAC) Record of Meeting, (December 3, 2020) (PDF)Return to text

9. Rayid Ghani, “Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services (PDF)” (testimony before the House Committee on Financial Services Task Force on Artificial Intelligence Hearing on February 12, 2020). Return to text

10. Davide Castelvevchi, “Can we open the black box of AI?” Nature 538(October 5, 2016): 20–23, https://www.nature.com/news/can-we-open-the-black-box-of-ai-1.20731. Return to text

11. Cynthia Rudin, “Please Stop Explaining Black Box Models for High-Stakes Decisions (PDF)” (paper presented at 32nd Conference on Neural Information Processing Systems, Montreal, Canada, November 2018). Return to text

12. Castelvevchi, “Can we open,” 20–23. Return to text

13. Among other things, the explanation can also make consumers aware of any erroneous information that drove the denial of credit. Return to text

14. Ziad Obermeyer et al., “Dissecting racial bias in an algorithm used to manage the health of populations,” Science 366 (October 25, 2019): 447–453, https://science.sciencemag.org/content/366/6464/447. Return to text

15. Carol A. Evans and Westra Miller, “From Catalogs to Clicks: The Fair Lending Implications of Targeted, Internet Marketing,” Consumer Compliance Outlook, third issue, 2019. Return to text

16. Piotr Szapiezynski et al., “Algorithms That ‘Don’t See Color’: Comparing Biases in Lookalike and Special Ad Audiences,” (2019), https://sapiezynski.com/papers/sapiezynski2019algorithms.pdf; Till Speicher, et al., “Potential for Discrimination in Online Targeted Advertising (PDF),” Proceedings of Machine Learning Research 81:1–15, 2018 Conference on Fairness, Accountability, and Transparency. Return to text

17. Makada Henry-Nickie, “Equitable Algorithms: Examining Ways to Reduce AI Bias in Financial Services (PDF)” (testimony before the House Committee on Financial Services Task Force on Artificial Intelligence Hearing on February 12, 2020). Return to text

18. See Marco Tulio Ribeiro et al., “Model-Agnostic Interpretability of Machine Learning” (presented at 2016ICML Workshop on Human Interpretability in Machine Learning, New York, New York, 2016); Zachary C. Lipton, “The Mythos of Interpretability” (presented at 2016 ICML Workshop on Human Interpretability in Machine Learning, New York, New York, 2016). Return to text

19. See Cynthia Rudin, “Please Stop Explaining” and Christoph Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (Christoph Molnar). Return to text

20. The Federal Reserve’s Model Risk Management guidance (SR 11-7) establishes an expectation that models used in banking are conceptually sound or “fit for purpose.” SR 11-7 instructs that when evaluating a model, supervised institutions should consider the “[t]he design, theory, and logic underlying the model.” The Model Risk Management guidance discusses in detail the tools banks rely on to help establish the soundness of their models, such as back-testing and benchmarking and other outcomes-based tests. Return to text

RBNZ response to illegal breach of data system

The Reserve Bank of New Zealand (RBNZ) – Te Pūtea Matua continues to respond with urgency to a breach of a third party file sharing service used to share information with external stakeholders.

Governor Adrian Orr says the breach is contained, but it will take time to determine the impact. The analysis of the potentially affected information is being done with pace and care.

“We are actively working with domestic and international cyber security experts and other relevant authorities as part of our investigation. This includes the GCSB’s National Cyber Security Centre which has been notified and is providing guidance and advice.”

“We have been advised by the third party provider that this wasn’t a specific attack on the Reserve Bank, and other users of the file sharing application were also compromised.”

“We recognise the public interest in this incident however we are not in a position to provide further details at this time.”

Providing any further details at this early stage could adversely affect the investigation and the steps being taken to mitigate the breach. The Reserve Bank will continue to work with affected stakeholders directly.

“Our core functions and New Zealand’s financial system remain sound, and Te Pūtea Matua is open for business. This includes our markets operations and management of the cash and payments systems.”

We will provide further facts when it is appropriate to do so.

Key details of incident to date:

  • A third party file sharing service provided by Accellion called FTA (File Transfer Application), used by the Bank to share and store some sensitive information, was illegally accessed.
  • The system has been secured and taken offline while investigations are underway.
  • The Bank is communicating with system users about alternative ways to securely share data.
  • Work is continuing to confirm the nature and extent of information that has been potentially accessed. The compromised data may include some commercially and personally sensitive information.