This is the final in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1. Operational resiliency and third party due diligence have […]
This is the 4th in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1. Better can mean different things to different organizations […]
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This is the third in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1. Many financial institutions have the dueling mandates to […]
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John O’Neill is the SVP of Regional Sales, Western US and Canada for Silent Eight. Although he is now in sales, John began as an engineer, for example working on some of the early browser technology in Champaign Illinois and later at Motorola. We are publishing a 5 part series on AI in Banking by […]
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This is the second in a 5 part series (published weekly) written by guest author Amber Sutherland a banker who understands technology who currently works for Silent Eight an AI-based name, entity and transaction adjudication solution provider to financial institutions. Click here for Index and Part 1. The underlying questions here, without detracting from the […]
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Being a last minute addition to two Digital Insurance Innovation Conference Africa 2020 panels has proven the Flat Earthers’ hypothesis- the earth is not a spherical barrier to virtual navigation, and taking the concept one step further, insurance innovation has loosed its bindings from its earthly coil and barriers raised by regulatory frontiers. Patrick […]
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This is a 5 part series (published weekly) written by guest author Amber Sutherland. AI in Banking is a massive subject that needs somebody who understands both subjects. We found that person in Amber Sutherland who has over fifteen years of experience in finance, business development, and regulatory technology. She worked for a large […]
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The conversation around the interaction between data, algorithms and humans is evolving.
Once again, A crisis like no other!
The COVID19 induced crisis of course, provides us with more data and experience on the topic of data, algorithms, investing etc.
Once again, A crisis like no other, is actually the title of an AQR article at the end of March. The 20+yrs old quant king has suffered after a very difficult 2018 (the so-called Red October) which led to large outflows and over 1000 layoffs- Investors pull billions from quant king AQR as performance slumps – Financial News.
There has never been a better time to bring up the conversation on whether Data (fundamental, conventional and alternative) made a difference during this crisis. Did algorithms help and in what way? And what about the interaction of humans with Data and algorithms?
Fundamental macro data is essential, and this will never change. What can and should change is that it time lag and that not all data sources are trustworthy. We need to build a real-time information system aggregating trustworthy data, that starts with fundamental data and extends to the currently coined `alternative data`. Eventually, we will get rid of the alternative data term, all together.
Lykke recently launched, the Open Initiative project which is a competition with a CHF50k funding award. One of the four different thematics of the competition is focused on building a Real-time information system. Lykke proposes a Wikipedia like system (in certain aspects) with revenue streams built on Blockchain and accessed via APIs. This vision merits a separate article.
In May, I reached out to experienced Quants in different positions and discussed with them about data, machines and humans through the crisis and in the new normal. The demand for alternative data, as expected, spiked during this crisis because everybody needed to access the situation in real-time and needed real-time measures. Investors, traders, portfolio managers, pensions, who were already using some kind of alternative data, needed more and relied heavily on high-quality real-time data. The reliance on such alternative data and on actionable techniques to access exposures and make intelligent predictions, skyrocketed. New entrants in the alternative data space, flocked as they needed to manage risks and exposures.
Those in the field managing risks, uniformly confirmed the increased need to manage thematic and narrative risks. In plain words, humans needed to understand their exposure to airlines or to China during the crisis and in the new normal. They needed to ask the data and the machines what the impact of COVID19 would be in real estate. And on and on….
Humans continue to be in charge of narrating the topics of interest or at stake. The machines need to be able to offer actionable insights and forecasts.
There is an increasing need for real-time and continuous re-assessment of this kind of complexity through lots of high-quality real-time data of all sorts. Machine learning and adaptive trading algorithms that reflect and retrain gave confidence to humans in certain asset classes (e.g. commodities) during this highly uncertain period.
Building trust between humans and machines has always been essential and will continue to be. The recent crisis was a painful but valuable experience that built more trust in humans as to what machines can currently do and with what inputs.
There has been an improvement in actionable techniques that allow humans to extract signals towards generating alpha by combining high-quality real-time data and adaptive algorithms. There has been a better and larger offering from providers, of data, insights, algorithms.
To write this article I looked into and spoke with people in the following companies.
Ravenpack is a leading data analytics platform headquartered in Spain. I was using their free Coronavirus dashboard during the lockdown which had lots of alternative indicators (panic, media hype, fake news etc). You can check out their research around sentiment impact and sentiment investment strategies – here. Ravenpack highlights that negative sentiment has more predictive power for asset prices than positive sentiment.
S&P Global market intelligence is a leading player in alternative data and actionable techniques. They measure people’s flows and money flows and other data ahead of delayed economic data reporting. During the crisis, their free Quantamental Research offered several insights. For example, reports on how COVID19, the weather temperature, and real estate prices (REITs) were connected. Two years ago, S&P Global acquired Kensho. One of my first articles on DailyFintech in April 2015, profiled Kensho: Warren is like Watson and Siri for analysts, investors and traders.
The Book of Alternative Data, by Wiley coauthored by Alexander Denev and Saeed Amen will be available in July. It is a book covering ways of leveraging alternative information sources in the context of investing and risk management.
Sentifi is a Swiss company that was focused on sentiment data and has now built an enterprise analytics platform using AI and sentiment data.
Macrosynergy is a UK macro hedge that is currently acting as a fiduciary quant house for long term institutional investors. They are combining fundamental data and insights with algorithmic trading.
Predictive Layer is a Swiss company focused on predictive Analytics and forecasting applications. Their algorithms adapted to this crisis and offered insights that only machine learning can produce in a timely fashion. Their value add was clear in the commodities space during this crisis.
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Flood insurance has been a wallflower at the coverage dance- an eager participant but not able to find a suitable partner. Innovation efforts have found suitable risk prediction partners for carriers- FloodMapp, Hazard Hub, and Previsco among others- but is the flood insurance market ready? Politics, inertia, customer preferences and regulation might keep the music […]
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These are interesting times with Brexit around the corner, an Indo-Pak (China) war looming, and a disastrous trade relationship between the two largest economies of the world.
This week I delivered a speech at Cass Business School on how and if AI could help in dealing with recessions. There is so much noise about the next recession, that I wonder, if people prefer a recession to cool down the economy a bit.
And the expectations on Constantinople is pushing up crypto prices again – although I don’t believe for a second that, the crypto market is big enough yet, to trigger recessions.
Assume you are driving a Ford Fiesta, can the speed indicator on your dashboard keep you from having an accident? Upgrading your car to a more sophisticated, intelligent one would definitely help. But that doesn’t prevent you from having an accident either. Even self driving cars could be hacked, or could have a bug that causes accidents.
AI/Machine learning or any variation of data driven intelligence, as we know them today, can provide us suggestions – and clever ones.
But if a market filled with irrational exuberance from humans have to be fixed by rational machines, it is a tall ask.
The dot com bubble burst and the subprime mortgage crash happened because of too much liquidity in the market leading to bad lending and spending decisions. And it only took a trigger like a policy change or a crash of Lehman Brothers to sap liquidity off the market. So, what are the signs now?
How can we get intelligent with the data around us and spot recessions? An analysis of Consumer data should provide us with a view on consumer behaviour, and almost predict where inflation would be heading. One of the firms I recently met, used open banking to collect consumers data, enrich it, and help them manage their finances. But the intelligence they gather from millions of transaction level data are used by their institutional clients to understand customer sentiments towards a brand.
Those insights combined with macro economic data should give these institutions the intelligence to choose their investments. The applications of open banking have largely been focused around selling services to customers in a personalized fashion. However, open banking data should help us understand where the economy is heading too.
Risk management functions in banks/FIs have been beefed up since the recession. About £5 Billion is spent in the UK alone on risk and regulatory projects every year. The ability to perform scalable simulations in a Quantum computing ready world will help banks provide near real time risk management solutions.
In capital markets, we model the risk of a position by applying several risk factors to it. Often these risk factors are correlated to each other. To be able to model the effect of a dozen or more correlated risk factors on a firm’s position is hard for conventional computers. And as the number of these correlated risk factors increase, the computational power required to calculate risks increase exponentially. This is one of the key issues of simulations (not just in financial services) that Quantum computers are capable of solving.
11 years ago, when the recession happened, regulators were ill-equipped to react due to the lack of real time insights. Today they have regular reports from banks on transactions, and better ways to understand consumers’ behaviour. That clubbed with macro economic data trends, should provide enough indicators for regulators to set policies. So, when there is a tax law that would trigger a collapse is being proposed, they should come up with strategies to bring the law into effect with minimal damage to the economy.
In the machine learning world, there are two different approaches – supervised and unsupervised models. If you understand the problem well, you typically go for the supervised model and see how the dependent variable is affected by the independent variables.
However, I believe, recessions often have the habit of hitting us from a blind spot. We don’t know what we don’t know.
It’s important for regulators and central banks to run exploratory analysis – unsupervised models, and assess the patterns and anomalies that the algorithms throw.
Data from consumer behaviour, geo-political events, macro economics and the market should give these algorithms enough to identify patterns that bring about recessions. This may not necessarily help us avoid a recession, but could definitely reduce the impact of a sudden recession, or help us engineer a controlled recession when we want a cool down of the economy.
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