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Dollar Doldrums Before the Surge

This is an original publication by Real Vision and was posted on Youtube at


This week Real Vision use Refinitiv’s best-in-class data to discuss the outlook for the US dollar and commodities with Tony Nash, CEO and Founder of Complete Intelligence, a forecasting company across currencies, commodities and equity indexes. Whilst many investors are expecting fireworks, Tony expects asset prices to remain subdued for now, before exploding back into life after the US election.


See the full series and access expert data-driven insights and news from Refinitiv:


Show Notes


RV: Many of us make sweeping statements about the direction that markets will take, but accurate forecasting across a wide range of assets is a rare entity and has been made particularly difficult today by the distortions of central bank activity. That’s a financial forecasting challenge. Forecasting company Complete Intelligence has joined forces with Refinitiv to provide companies and investors with an outlook on assets such as currencies and commodities. In this week’s Big Conversation, I talk to founder and CEO Tony Nash about the prospects for the US dollar, commodities and also trade relations between the US and China.

Tony, great to see you.


TN: Thank you.


RV: Thanks very much for coming on The Big Conversation. And today we got to talk about a lot of things. We’re gonna talk about the dollar, currencies in general and a bit of commodities. But before we get into that, for those who do know you, could you give me a little bit about your background, what you’ve been doing and what you’re currently doing?


TN: I’ve been in research for couple of decades, actually, and in the past I led global research for a UK based firm called The Economist, I led Asia Consulting for a firm called IHS Market, and I jumped out to start Complete Intelligence about four or five years ago. Initially we were based in Singapore and now we’re based in Houston, Texas.


So Complete Intelligence is an artificial intelligence platform or a globally integrated AI platform. We help companies make better cost purchasing and revenue decisions. As a part of that, of course we work with raw materials, currencies, futures, commodities and even equity indices. All of this works in a layered environment so that we understand the interdependencies of supply chains and revenues and sales.


We go live on Refinitiv this month in July, and Refinitiv is a very positive partner for us. They’re great to work with. Our forecasts will be distributed on the Refinitv platform for purchase by Refinitiv clients.


RV: A lot of these forecasts are completely wrong, but your forecasts have been relatively accurate. They are pretty accurate, which is why I wanted this discussion. Your views on these commodities and currencies will be quite interesting. So how do you do that? What is it? What are the main inputs into your product?


TN: We started Complete Intelligence because my clients in my previous firms told me they can’t get good forecasting, whether it’s internal to their own firms or external from off the shelf information services firms. It’s definitely a financial forecasting challenge. What we found is generally external forecasters, whether they’re economists or banks or industry experts, typically have double-digit error rates on an absolute percentage error basis. Our average error rate is about 4.6 percent on a MAPE basis. We do much better generally than either internal forecasters or say industry experts, consensus forecasts.


What we do is we use what’s called an ensemble approach. We have a number of core methodologies that we use that build and learn scenarios for every iteration of our forecasts that we do. We do our forecast twice a month on the first of the month and mid-month. So we’re looking at thousands of methodological configurations for every line item that we forecast. So for example the dollar, we’ll look at between five and ten thousand configurations of methodologies to forecast the dollar. So that’s millions of calculations just for the dollar. And we do that for every line item foot.


Off the shelf, we have about 800 different assets that we forecast across currencies, commodities, equities. We also do economics and trade. So together it’s about 1.3 million line items that we forecast every month.


Obviously there are charts, so you can see the directional change in the lines. But we disclose the top relationships, six months ago, three months ago and this month. So you can see how those things change over time as well. That’s really a key part of understanding the market changing. If you see those relationships changing dramatically, then that’s a real indication. So if we look at last December, we saw things change dramatically and we saw that sometime ahead of that forecast. So we expected that dramatic change. When we expect, say, a mild change in October or a dramatic change in Jan/Feb, those relationships really do start to iterate because the market starts to restructure with every change.


RV: Tony you mentioned the dollar, ultimately. I always think of it as the apex predator of the financial market. You get the dollar right, you’re probably going to get a vast amount of the rest of your portfolio correct. Recently, the dollar had a lot of volatility very early on, but it’s actually been in a fairly kind of narrow range for a long period of time. At the moment, it’s testing the bottom of that range.


Let’s talk about the maybe the dollar index, the DXY, which is 57% Euro. Let’s talk about that first. Where do you see that going over the rest of the year and what do you see as the big drivers and the reasons for your view on the dollar?


TN: We see the dollar weakness continuing until about September. After September, we see a bit of strength coming back. And then in Q1, we start to see more dollar strength coming back.  So obviously, monetary policy, economic questions, these sorts of things in the US are behind that, but also economic questions around other parts of the world. The dollar is doesn’t operate in a vacuum. So there are a number of inputs there, and we’re really worried about a lot. Big monetary policies in the US have been made and they’re working themselves out. But we’re worried about other parts of the world, specifically Europe and China. But we do see the dollar continue to weaken until about September, late August and September, after which we see a slight return to strength. And then once we hit Q1 of 2021, we start to see that as well.


RV: What are those key drivers? Because in some ways, people have been, I wouldn’t say caught offside, but actually the dollar has been weakening as the Fed’s been tightening its balance sheet. So as it’s been reducing a little bit of liquidity and the other central banks is still going for it. Which of the drivers is it? Is it liquidity driving? It is perception? Is it interest rate differentials? Is it real, real yields and real difference? What are the key drivers that mean that you think it’s going lower?


TN: I don’t know that there’s necessarily trust in the fact that the Fed is actually reducing its balance sheet. Is that temporary? I think there’s a belief that the Fed will do anything to keep markets up.


We all see this cynicism in the market every day, and so I’m not sure that there is a lot of trust right now of the Fed’s true intentions. We’re in a position where both the Fed and the Treasury will do anything to grow the U.S. economy through COVID. And once we get through COVID, different rules apply. But for now, they’ll do anything to get us through. That’s until we see some proof points about a policy that isn’t just kind of throw everything at it. We’ll start to see that in October. But for now, we’re still in that very skeptical position where the U.S. institutions, finance and monetary, isn’t really questioned at the moment because we’re in the middle of this unprecedented period.


RV: When people are looking at currencies, people say, OK, I can see all these negatives for the US, but then you think, “well hang on a minute, Europe has a lot of negatives.” When we talk about the dollar, what are the alternatives? So with the Euro, do you see therefore that because a big part of the dollar index is the euro, do you see the Euro going sort of 1-17 from where it is today, or do you see the Euro being challanged as well, and over what timeframe or not perhaps?


TN: The euro is challenged once they get through these meetings now. The issues with the Euro are many and I think we’ll see probably four to five months of difficulties for the Euro. After which, let’s say, the end of Q1 2021, I think we’ll start to see more strength in the Euro. But we just don’t see the justification for Euro strength right now, even on a relative basis with the dollar. We find it really challenging to see a bull case for the Euro until early next year.


So what are the alternatives? Things like Aussie dollar or things like Japanese yen, those are also alternatives, but again, it’s the ugly sisters right there. It’s difficult to pin down a winner.


RV: So when you’re talking about that dollar weakness, what are you really thinking here? Is It’s probably dollar weakness that’s commensurate with volatility in currencies remaining relatively subdued. It sounds like the alternative to, if you’ve got dollar weakness through to September, maybe beyond, but you haven’t really got Euro strength and maybe the strength comes in Yen and Aussie dollar. But overall, we really talking about grinding currencies and low volatility currencies, is that you think is the next few weeks, months?


TN: That’s right. And I don’t know that anybody is really confident to say currency A is the currency I’m going to place the next three months of my bets on. It’s all speculative, vol related trades, or at least that’s what we see. And until we start to get some good direction, typically when we see good direction, we see dollar strength. We don’t really see good direction coming back to markets until maybe December or Q1 of 2021.


RV: And do you think in terms of people who are looking for signs of things, that change is there a sequencing that you were looking for, for instance? Well, what I think we really saw last year was those very challenged emerging market currencies, in places like Turkey obviously Argentina, they tended to move first. Then you saw things like the Aussie dollar moving, sort of commodity based, slightly EM style, and then eventually that was shifted through. Do you think that’s still going to be the way to look at this? That if we want to, an early warning that currencies are on the move, do you think it’s going to be in the challenged currencies again first like maybe Brazil moving slowly through? Or do you see a different sequencing now with slightly different paradigm post-COVID?


TN: I think until the end of COVID, I think we’re looking at the same patterns. And again, I think part of that is COVID, part of that is the US election, part of that is what’s really going on with Chinese data. There are a number of different considerations, macro considerations that until we have a good idea of what the data actually mean. And let’s say what you know, what is the future of U.S. politics? I don’t think we’re really going to settle. And if you don’t know the future of U.S. economic policies, you really don’t know the future of Chinese economic policies. And so you have the two biggest economies in the world that have a big question mark around them for the next four or five months.


RV: When it comes to commodities, as I think commodities has been the first item on the Refinitiv platform, currencies coming at the end of this month. So as a sort of segway between one and the other, the Aussie dollar is often considered to be a very important part of the multi complex, even though it’s not a commodity itself. Is that one of the ones you think will have a bit of strength VS the Dollar over the shorter term as in the next couple of months? How do you feel the Aussie dollar is going to play out and what are the key players behind that?


TN: We do see strength in the Aussie dollar. I mean Aussie dollar had this amazing trip over the past five months right? We do see strength coming in, say, through the next two to three months in the Aussie dollar. Then we see it returning to the normal levels, kind of around 70 cents. So part of that is COVID related, part of that is obviously China related, as Australia and China re-figure out what their relationship is, their trading relationship and their diplomatic relationship.


There is a bit of risk because obviously, Australia exports a lot of commodities to China. And if that relationship isn’t there, then the underlying driver of their economy is in question. And so we do have some questions about the Aussie dollar and the sustainability of some of those exports for some short to medium term. But some of that quite frankly, is just diplomatic positioning more than reality. There’s a bit of volatility until we figure out exactly what that looks like, but we don’t expect a return to say, of the Fed March position and the volatility we saw there.


RV: When you look at the Aussie dollar, are you looking at real economy assets like copper and like oil? Because obviously these have had, we’ve seen oil, WTI’s closed its gap from the trade war, the oil war earlier in the year, copper is now back at a big, i think it’s the 10 year resistance level. How do you see these real economy assets performing over the next two, three months because it feels like we’re recovering, but we’re recovering from such a low place that it looks v shaped, but we’re not recovering, we’re not going to return to where we were. Doesn’t that put pressure on some of these currencies like the Aussie dollar, which rely on the real economy to get back to where it was? I think we’re back to where we were beginning of year with the Aussie dollar, but should not really be capping it?


TN: Yeah, it’s been kind of a foreshock of recovery. It’s not really an aftershock. It’s never really recovered yet, but we’ve started we’ve seen markets recover. So we do see, say the Brent and WTI really having strength over the next, say, to three to four months. After that I think there’s some questions around the sustainability of that. Short of a supply, more controls on supply I think we hit some levels where we’re we’re not quite sure where things will go and we may see those kind of pare some of their gains that we’ve seen since, say, the lows in April. Going into early twenty one, we may very well see some downside, not serious downside, but gradual downside to crude oil. We do believe that WTI has more legs than Brent going into Q4, but not much.


When we look at things like copper, which is very, very important to the Australian economy, that’s really looking strong until, say, December, Jan, after which again, twenty one, I think people really take stock of where markets have gone and start to question whether the value is really there, whether, say, manufacturing and transportation have caught up with the prices that we’ve hit. And if we don’t see things like consumer goods and consumer electronics hit their previous pace, if we don’t see airlines starting to hit they’re approaching their previous pace, going back online, I think we’re going to start to see some questions around that value. And that’s kind of our base case right now, is we’re not necessarily expecting those things to start to approach their previous levels, and what we’ve faced from the beginning of this is a demand problem. The demand problem that came as a result of government’s pulling the plug on their economies.


So when will that demand return? Is the big question. We do see it coming back, but not necessarily at the pace that markets have expected for the past couple of months. But that won’t necessarily hit investors for another three to four months, actually.


RV: How much of that is dependent on the furlough support scheme we see in place? The US went first, it went hard, it went in size, and it took Europe’s only just caught up about a month ago. Japan’s never really stopped, and China’s may been more reticent, but let’s say we get into a scenario where we see the furlough schemes running off at the end of this month in the US, and what if the U.S. decides not to come back too aggressively? But other markets where the current countries do or other regions do? Is that is that going to change the view materially or is this kind of a global context and kind of everyone lives and falls together as it where?


TN: Well, I think everyone lives and falls together. Look, it’s an election year in the U.S. of course, they’re gonna put out more money. I mean, it’s you can’t I don’t think you can in an election year say, oh, we’re going to be fiscally responsible no. There’s just no election works that way at all. So the U.S. will definitely come out with more support. And because the US is doing it, every other Treasury and finance ministry and central bank will say, well, the U.S. is doing it, so we’re gonna do it. So exactly what you say kind of they’ll all rise or fall together. Once the US election is over, that tail will kind of taper off and then we’ll see things really starting to fall to Earth again. We’re not saying anything dramatic, but we’ll start to see some of the steam come off post-election in the US.


RV: We’ve been focusing on the currencies and a little bit on the commodities, but in some ways what people worry about is that we’ve gone from this liquidity issue at the beginning of the second quarter of the year to potentially a solvency issue. So a real, real economy growth issue. And do you think that that is going to come to fruition? Because those that will have a very, very key impact on bond yields, and if you look at these major bonds particularly in the U.S., they’ve been struggling, I mean, that would merely making new all time lows in the U.S. fight it? Where do you see bond yields going? Because in some ways, the bond yield is the one that will tell us the true growth, the equity market told us, how much liquidity, where do you see bond yields going?


TN: I don’t think there’s any choice but for bonds to continue to fall until we see more solvency to the economy, that’s really it. And we’ve seen so many SME’s go out of business, we’ve seen a complete section of the U.S. economy just give up. And we are now on to kind of the medium term players who are keeping it together but maybe can’t in for three to four to five to six months if they don’t have more support from the central government in the US. Until we see the baton passed from government support to market support, which again, probably won’t happen until sometime in twenty one, you know, we’re going to have this question around solvency. Once the market takes over again, then I think we’ll be in a very good place, we’ll have cleared out a lot of fairly weak companies, we’ll see consolidation in sectors that weren’t really healthy, and then as we go into twenty one and the market takes over again, I think the path has really cleared for companies to do extraordinarily well.


RV: Something that you talked about in depth last year, generally, you sort of you talked about was the the impact or the underestimation of the impact of the trade war and relationships like that. How important do you think that will be? Because obviously the politics today is kind of quite visceral in this, you know, in the last couple of months. Do you think that that is more bark than bite or do you think that we’re going to go back to the worst of what we saw with the trade wars, which was almost also reflecting the difficult position that the Chinese economy was in prior to all this if we went back a year, 12 to 18 months?


TN: One view that I’m kind of moving toward is that potentially a trade war is actually over. So with COVID, at least North American companies have taken an assessment of their supply chains and said, hold on a minute, we have a highly centralized supply chain sitting in China and other parts of Asia. COVID’s come along and we haven’t really been able to get access to our goods.


We need to diversify our supply chain. Now, before the financial crisis in 2008, there was a strategy that manufacturing companies were pursuing called the China plus one, China plus two, China plus three strategy, where they would have part of their supply chain in China and part elsewhere in Asia. I think what we’re at now because after the financial crisis, people just double and tripled down on their China-centric supply chains because it was convenient and in their in their eyes at the time, less risky.


I think we’re in a position now where especially North American companies have said it’s very risky for us to have our North American and our European manufacturing based in China. We need to disaggregate, we need to have regional supply chains. We look at, for example, the amount of electronics supply chain that’s moving to Mexico, when we look at companies like TSMC, Taiwan Semiconductor, moving to the US, these are major generational movements of supply chains. That to me is a signal that the trade war is almost over, meaning both sides have said enough, we’re not going to do this.


That’s a very bad signal for China, and you could potentially be looking at kind of a Russia post-World War Two scenario where all the foreign investors who went into Russia in the nineteen thirties from the UK and the US and other guys, they gave up with World War Two and really never went back. And so China could potentially be looking at that type of scenario.


The big question mark is around kind of Angela Merkel and a bunch of European investors in China, what will they or leaders in China, what will their investors do? Will they regionalize in Europe, which is what was happening in the 90s? Or will they continue to double and triple down on China? If they do, the problem that Europe has is that China has to export even more deflation than they were exporting two or three years ago because they have the additional capacity that is not going to the US now. That is a serious risk for the hollowing out of European industry and European unemployment.


RV: By the sounds of it, the next few months therefore, across pretty much every asset should be relatively low volatility, so maybe still working out all the support that’s come into the system as it still moves its way through the global framework. But it sounds like at the end of this year, particular into Q1 of next year there could be some inflection points. How do people use your product to spot those inflection points? Because its those inflection points where people are going to really win or lose?


TN: The inflection points are really where the risk comes in. So in our partnership with Refinitiv, you know, people can use our product to understand, as you say, when are those inflection points, what’s the degree of those inflection points? With all of our outlooks, we have high base, low scenarios. And so, those clients can understand where we see things going and the range where we see those things going. Whether it’s a currency, commodity and equity market. And so, as you say, we see a larger inflection coming kind of mid Q1, but in the in the near term, we see kind of a small calibration coming in September, October.


RV: Whilst most people want to hear about fireworks, where prices are either going to break down or break out, the reality is that for most of the time, they tend to grind through ranges. For corporate planners and investors, accurate forecasts help to prepare for the unexpected without getting bogged down by sensationalism.


Complete Intelligence currently forecasts the commodity and currency volatility will remain suppressed, with the dollar drifting lower, helping push oil and copper prices higher. The first market wobble should appear in September and October, but the big inflection point is expected after the US election. Markets in the first quarter of 2021 are forecast to be challenged by a stronger U.S. dollar as the real economy impact of the COVID crisis emerges from beneath the flood of government support.


About Refinitiv: For new insights on artificial intelligence (AI), digitalization, big data, risk management, compliance, fighting financial crime and the future of trading and investing, visit our insights hub – Refinitiv is one of the world’s largest providers of financial markets data and infrastructure, serving over 40,000 institutions in approximately 190 countries. It provides leading data and insights, trading platforms, and open data and technology platforms that connect a thriving global financial markets community – driving performance in trading, investment, wealth management, regulatory compliance, market data management, enterprise risk and fighting financial crime.


About Real Vision™: Real Vision™ is the destination for the world’s most successful investors to share their thoughts about what’s happening in today’s markets. (Think of it like TED Talks for Finance.). Understand the complex world of finance, business and the global economy with real in-depth analysis from real experts.

News Articles

Top 12 AI Use Cases: Artificial Intelligence in FinTech

We’ve scoped out these real-world AI use cases so we could detail how artificial intelligence has been a game-changer for FinTech. Few verticals are such a perfect match for the improved capabilities brought by the AI revolution like the financial sector.


Traditional financial services have always struggled with massive volumes of records that need to be handled with maximum accuracy.


However, before the advent of AI and the rise of Fintech companies, very few giants of this industry had the bandwidth to deal with the inherently quantitative nature of this world. (Read Fintech’s Future: AI and Digital Assets in Financial Institutions.)


Banks alone are expected to spend $5.6 billion USD on AI and Machine Learning (ML) solutions in 2019 — just a fraction of what they’re expecting to earn since the profits generated may reach up to $250 billion USD in value.


From automating the most menial and repetitive tasks to free up the time to focus on higher-level objectives, to assisting with customer service management and reducing the risk of frauds, AI is employed from back-office tasks to the frontend with nimbleness and agility.



1. Fraud Detection and Compliance


According to the Alan Turing Institute, with $70 billion USD spent by banks on compliance each year just in the U.S., the amount of money spent on fraud is staggering. And when the number of reported cases of payments-related fraud has increased by 66% between 2015 and 2016 in the United Kingdom, it’s clear how this problem is much more than a momentary phenomenon.


AI is a groundbreaking technology in the battle against financial fraud. ML algorithms are able to analyze millions of data points in a matter of seconds to identify anomalous transactional patterns. Once these suspicious activities are isolated, it’s easy to determine whether they were just mistakes that somehow made it through the approval workflow or traces of a fraudulent activity.


Mastercard launched its newest Decision Intelligence (DI) technology to analyze historical payments data from each customer to detect and prevent credit card fraud in real time. Companies such as Data Advisor are employing AI to detect a new form of cybercrime based on exploiting the sign-up bonuses associated with new credit card accounts.


Even the Chinese giant Alibaba employed its own AI-based fraud detection system in the form of a customer chatbot — Alipay.



2. Improving Customer Support


Other than health, no other area is more sensitive than people’s financial well-being. A critical, but often overlooked, application of AI in the finance industry is customer service. Chatbots are already a dominating force in nearly all other verticals, and are already starting to gain some ground in the world of banking services, as well. (Read We Asked IT Pros How Enterprises Will Use Chatbots in the Future. Here’s What They Said.)


Companies like Kasisto, for example, built a new conversational AI that is specialized in answering customer questions about their current balance, past expenses, and personal savings. In 2017, Alibaba’s Ant Financial’s chatbot system reported to exceed human performance in customer satisfaction.


Alipay’s AI-based customer service handles 2 million to 3 million user queries per day. As of 2018, the system completed five rounds of queries in one second.


Other companies, such as Tryg, used conversational AI techs such as to provide the right resolutive answer to 97% of all internal chat queries. Tryg’s own conversational AI, Rosa, works as an incredibly efficient virtual agent that substitutes inexperienced employees with her expert advice.


Virtual agents are able to streamline internal operations by amplifying the capacity and quality of traditional outbound customer support. For example LogMeIn’s Bold360 was instrumental in reducing the burden of the Royal Bank of Scotland’s over 30,000 customer service agents customer service who had to ask between 650,000 and 700,000 questions every month.


The same company also developed the AI-powered tool AskPoli to answer all the challenging and complex questions asked by Fannie Mae’s customers.



3. Preventing Account Takeovers


As a huge portion of our private identity has now become somewhat public, in the last two decades cybercriminals have learned many new ways to use counterfeit or steal private data to access other people’s accounts.


Account Takeovers (ATOs) account for at least $4 billion USD in losses every year, with nearly 40% of all frauds occurred in 2018 in the e-commerce sector being due to identity thefts and false digital identities.


Smartphones appear to be the weakest link in the chain in terms of security, so the number of mobile phone ATO incidents rose by 180% from 2017 to 2018.


New AI-powered platforms have been created such as the DataVisor Global Intelligence Network (GIN) to prevent these cyber threats, ranging from social engineering, password spraying, and credential stuffing, to plain phone hijacking.


This platform is able to collect and aggregate enormous amounts of data including IP addresses, geographic locations, email domains, mobile device types, operating systems, browser agents, phone prefixes, and more collected from a global database of over 4 billion users.


Once digested, this massive dataset is analyzed to detect any suspicious activity, and then prevent or remediate account takeovers.


4. Next-gen Due Diligence Process


Mergers and acquisitions (M&A) due diligence is a cumbersome and intensive process, requiring a huge workload, enormous volumes of paper documents, and large physical rooms to store the data. Today the scope of due diligence is now even broader, encompassing IT, HR, intellectual property, tax information, regulatory issues, and much more.


AI and ML are revolutionizing it to overcome all these difficulties.


Merrill has recently implemented these smart technologies in its due diligence platform DatasiteOne to redact documents and halve the time required for this task. Data rooms have been virtualized, paper documents have been substituted with digital content libraries, and advanced analytics is saving dealmakers’ precious time by streamlining the whole process.



5. Fighting Against Money Laundering


Detecting previously unknown money laundering and terrorist financing schemes is one of the biggest challenges faced by banks across the world. The most sophisticated financial crime patterns are stealthy enough to get over the rigid conventional rules-based systems employed by many financial institutions.


The lack of public datasets that are large enough to make reliable predictions makes fighting against money laundering even more complicated, and the number of false positive results is unacceptably high.


Artificial neural networks (ANN) and ML algorithms consistently outperform any traditional statistic method in detecting suspicious events. The company ThetaRay used advanced unsupervised ML algorithms in tandem with big data analytics to analyze multiple data sources, such as current customer behavior vs. historical behavior.


Eventually, their technology was able to detect the most sophisticated money laundering and terrorist financing pattern, which included transfers from tax-havens countries, abnormal cash deposits in high risk countries, and multiple accounts controlled by common beneficiaries used to hide cash transfers.



6. Data-Driven Client Acquisition


Just like in any other sector where several players fight to sell their services to the same customer base, competition exists even among banks. Efficient marketing campaigns are vital to acquire new clients, and AI-powered tools may assist through behavioral intelligence to acquire new clients.


Continuously learning AI can digest new scientific research, news, and global information to ascertain public sentiment and understand drivers of churn and customer acquisition.


Companies such as SparkBeyond can classify customer wallets into micro-segments to establish finely-tuned marketing campaigns and provide AI-driven insights on the next best offers.


Others such as LelexPrime make full use of behavioral science technology to decode the fundamental laws that govern human behaviors. Then, the AI provide the advice required to make sure that a bank’s products, marketing and communications align best with their consumer base’s needs.



7. Computer Vision and Bank Surveillance


According to the FBI, in the United States Federal Reserve system banks alone are targeted by nearly 3,000 robberies every year. Computer vision-based applications can be used to enhance the security and surveillance systems implemented in all those places and vehicles where a lot of money is stashed (banks, credit unions, armored carriers, etc.).


One example is Chooch AI, which used to monitor sites, entries, exists, actions of people, and vehicles. Visual AI is better than human eye to capture small details such as license plates and is able to recognize human faces, intruders and animal entering the site.


It can even raise a red flag whenever unidentified people or vehicles are present for a suspicious time within a certain space.



8. Easing the Account Reconciliation Process


Account reconciliation is a major pain point in the financial close process. Virtually every business must face some level of account reconciliation challenge since it’s an overly tedious and complex process that must be handled via manual or Excel based processes.


Because of this, errors are way too common even when this problem is dealt with rule-based approaches. In fact, other than being extremely expensive to set up due to complicated system integration and coding, they tend to break when the data changes or new use cases are introduced and need on-going maintenance.


SigmaIQ developed its own reconciliation engine built on machine learning. The system is able to understand data at a much higher level, allowing for a greater degree of confidence in matching, and is able to learn from feedback.


As humans “teach” the system what is a match and what is not, the AI will learn and improve its performance over time, eliminating the need to pre-process data, add classifications, or update the system when data changes.



9. Automated Bookkeeping Systems


Small business owners are often distracted by the drudgery of the back-office — an endless series of chores which take away a lot of valuable business time. AI-powered automated bookkeeping solutions such as the ones created by ScaleFactor or Botkeeper are able to assist SMB owners in back-office tasks, from accounting to managing payrolls.


Using a combination of ML and custom rules, processes, and calculations, the system can combine various data sources to identify transaction patterns and categorize expenses automatically. JP Morgan Chase is also employing its own Robotic Process Automation (RPA) to automate all kind of repetitive tasks such as extracting data, capturing documents, comply with regulations, and speed up the cash management process.



10. Algorithmic Trading


Although the first “Automated Trading Systems” (ATSs) trace their history back to the 1970’s, algorithmic trading has now reached new heights thanks to the evolution of the newer AI systems.


In fact, other than just implementing a set of fixed rules to trade on the global markets, modern ATSs can learn data structure via machine learning and deep learning, and calibrate their future decisions accordingly.


Their predicting power is becoming more accurate each day, with most hedge funds and financial institutions such as Numerai and JP Morgan keeping their proprietary systems undisclosed for obvious reasons.


ATSs are used in high-frequency trading (HFT), a subset of algorithmic trading that generates millions of trades in a day. Sentient Technologies’ ATS, for example, is able to reduce 1,800 days of trading to just a few minutes. Other than for their speed, they are appreciated for their ability to perform trades at the best prices possible, and near-zero risk of committing the errors made by humans under psychological pressure.


Their presence on the global markets is pervasive to say the least. It has been estimated that nowadays, computers generate 50-70% of equity market trades, 60% of futures trades and 50% of Treasuries. Automated trading is also starting to move beyond HFT arbitrage and into more complex strategic investment methodologies.


For example, adaptive trading is used for rapid financial market analysis and reaction since machines can quickly elaborate financial data, establish a trading strategy and act upon the analysis in real-time.



11. Predictive Intelligence Analytics and the Future of Forecasting


Accurate cash forecasting are particularly important for treasury professionals to properly fund their distribution accounts, make timely decisions for borrowing or investing, maintain target balances, and satisfy all regulatory requirements. However, a 100% accurate forecasting is a mirage when data from internal ERPs is so complicate to standardize, centralize, and digitize — let alone extract some meaningful insight from it. It’s clearly a financial forecasting challenge.


Even the most skilled human professional can’t forecast outside factors and can hardly take into consideration the myriad of variables required for a perfect correlation and regression analysis.


Predictive intelligence analytics applies ML, data mining and modeling to historical and real-time quantitative techniques to predict future events and enhance cash forecast. AI is able to pick hidden patterns that humans can’t recognize, such as repetitions in the attributes of the payments that consist of just random sequences of numbers and letters.


The most advanced programs such as the ones employed by Actualize Consulting will use business trends to pull valuable insights, optimize business models, and forecast a company’s activity.


Others such as the one deployed by Complete Intelligence reduce error rate to less than 5-10% from 20–30%.



12. Detecting Signs of Discrimination and Harassment


Strongman and sexist power dynamics still exist in financial services, especially since it’s an industry dominated prevalently by males. While awareness has increased, 40% of people who filed discrimination complaints with the EEOC reported that they were retaliated against, meaning that the vast majority of those who are victimized are simply too scared to blow the whistle.


AI can provide a solution by understanding subtle patterns of condescending language, or other signals that suggest harassment, victimization, and intimidation within the communication flows of an organization.


Receptiviti is a new platform that can be integrated with a company’s email and messaging systems to analyze language that may contain traces of toxic behaviors. Algorithms have been instructed with decades of research into language and psychology that analyze how humans subconsciously leak information about their cognitive states, levels of stress, fatigue, and burnout.


A fully automated system, no human will ever read the data to preserve full anonymity and privacy.



Final Thoughts


In the financial sector, AI can serve a multitude of different purposes, including all those use cases we already mentioned in our paper about the insurance industry. AI and ML are incredibly helpful to ease many cumbersome operations, improve customer experience, and even help employees understand what a customer will most-likely be calling about prior to ever picking up the phone.


These technologies can either substitute many human professionals by automating the most menial and repetitive tasks, or assist them with forecasts and market predictions.


In any case, they are already spearheading innovation in this vertical with the trailblazing changes they keep bringing every day.



Written by Claudio Buttice

Dr. Claudio Butticè, Pharm.D., is a former clinical and hospital pharmacist who worked for several public hospitals in Italy, as well as for the humanitarian NGO Emergency. He is now an accomplished book author who has written on topics such as medicine, technology, world poverty, and science. His latest book is “Universal Health Care” (Greenwood Publishing, 2019).

A data analyst and freelance journalist as well, many of his articles have been published in magazines such as CrackedThe ElephantDigital JournalThe Ring of Fire, and Business Insider. Dr. Butticè also published pharmacology and psychology papers on several clinical journals, and works as a medical consultant and advisor for many companies across the globe.

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