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BUILDING THE TRADER OF TOMORROW WITH ARTIFICIAL INTELLIGENCE

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By: John Harding, Regional Director, UK & Ireland, NVIDIA

 

While stories about the seismic shift toward digital banking in the post-COVID era abound, another technology revolution is taking place in capital markets: the era of AI-powered trading. Recent market fluctuations and the impact of social media sentiment on stock prices have highlighted the need for active fund managers, traders and market makers to utilize AI in order to compete effectively in the future.

These trends are apparent in some of the findings from NVIDIA’s recent survey of financial services professionals from around the world. The “State of AI in Financial Services” survey consisted of questions covering a range of AI topics, such as deployment models, infrastructure spending, top use cases and biggest challenges. Respondents included C-suite leaders, managers, developers and IT architects from fintechs, investment firms and retail banks.

In fact, according to NVIDIA’s recently released “State of AI in Financial Services” survey report, 83% of global financial services professionals agreed with the statement that “AI is important to my company’s future success.”

Diving into that statement, the impact of AI on financial markets is real and measurable. According to our survey, 34 percent of respondents state that AI will increase their company’s annual revenue by 20 percent or more. Across the broader financial services landscape, survey respondents identified four key areas where AI is impacting their company today: yielding more accurate models, creating a competitive advantage, developing new products and improving operational efficiencies. AI is growing revenue and market share while shrinking costs across the industry.

Specifically, for investment firms, algorithmic trading and portfolio optimization were identified as the most common class of AI applications. Every trading decision — what to buy or sell, at what prices, when and where to execute trades — can benefit from either AI powered algorithms or from systems that augment human decision makers with AI-powered assistants.

 

Roadblocks to Achieving AI Goals

Given the significant impact of AI to investment firms, what is holding them back from achieving their AI objectives? The biggest challenges to achieving AI goals are too few data scientists (38 percent), insufficient technology infrastructure (35 percent) and a lack of data (35 percent). These challenges are all related, as it turns out.

 

Finding and retaining top talent is a challenge for any part of an organization and that’s certainly the case in AI. However, the C-suite can overcome these by infusing AI expertise across the organization. 60 percent of C-level executives responded that their largest focus moving forward is identifying additional AI use cases — driving the demand for even more data scientists. One in two respondents from the C-suite noted that their company also plans to hire more AI experts — directly trying to addressing the gap of too few data scientists.

The technical infrastructure for AI has never been more available, although the plethora of choices (and existing “shadow IT” investments) can sometimes make it feel like an overwhelming problem. Whether on-premise, in the cloud, or in a hybrid environment, container-based, GPU accelerated systems can be rapidly built and deployed. This too is a soluble problem.

Lack of data can sometimes be addressed with some creative thinking (how can I transform or buy data that would turn what I do have into something with more value). In other cases, it’s a timing problem — we don’t have enough data now, but if we started on an AI journey we could bootstrap ourselves onto a virtuous cycle where the more data we have, the more value our models deliver, which makes the return on investment in managing the additional data higher, and so on. Key to solving this problem is a combination of data scientists and infrastructure!

 

Putting it Together

Regardless of whether a trader is managing a portfolio of algorithmic trading models or utilizes insights from AI to drive discretionary trades, investment firms must employ an enterprise AI strategy that creates a competitive advantage that otherwise will be captured by the competition.
C-suite and IT leadership at investment firms are challenged to build enterprise-level AI platforms to scale and deliver productivity and return on investments to support the growing AI professionals across their companies. As a starting place, financial institutions need to proactively elevate AI as a strategic imperative to the firm that needs to ultimately become a core competency.

The same opportunity exists within commercial and retail banks. Rather than relegate AI to the “research lab,” the banks that are creating meaningful impact from AI are developing strategic plans, resourcing the teams appropriately and establishing an AI infrastructure platform upon which the bank can productively scale dozens if not hundreds of AI applications and see a significant return on investment.

 

Enabling the “Trader of Tomorrow”

The race is on among investment firms to AI-enable portfolio managers and traders, among other roles and functions. As data continues to proliferate across all variety of channels and dimensions, it’s no longer just the owner of the data who holds the keys, but the ones who can uncover actionable insights to create competitive advantage from all varieties of data will lead the industry to the “Trader of Tomorrow.”

The “State of AI in Financial Services” survey consisted of questions covering a range of AI topics, such as deployment models, infrastructure spending, top use cases and biggest challenges. Respondents included C-suite leaders, managers, developers and IT architects from fintechs, investment firms and retail banks.

 

Top 10

HERE’S HOW INSURANCE IS SET TO CHANGE

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By Adam Goldsmith, Insurance Specialist, SAS UK & Ireland

 

Making predictions about the state of any industry in the coming year is a nigh-on impossible task. But looking to the longer-term, the patterns we’re seeing in insurance firms, which have been inspired by the pandemic, have revealed in no uncertain terms that the industry is in flux. Change is here, and its impact will be felt for many years to come.

Woken up by the sharp jolt of the pandemic, insurance will experience dramatic change by 2025. But not all firms will adapt fast enough to the new insurance landscape or new expectations from customers. Those that pay attention to long-term predictions like the following could reap the rewards post-pandemic.

 

1. Knowing customers inside-out through their data will be non-negotiable

A typical Insurer today is set up in very traditional manner. There remains distinct, separate departments for the key functions: including assessing risk, acquisition, customer engagement, claims handling, customer protection and renewal.

Yet very few insurers have a truly joined-up view of a customer’s full journey with their organisation, let alone what can be done to optimise each interaction. What’s needed is the ability to understand each customer touchpoint as they traverse through their journey, as well as the ability to make decisions as to how best to engage them.

Insurers often cite legacy policy admin and claims systems as the biggest barrier standing in the way of this approach being adopted. By 2025, however, the most successful insurers will have broken those barriers down, gaining an unprecedented understanding of their customers’ needs and preferences, and the ability to offer pricing plans that are both fair and competitive.

 

2. Automation and algorithms will become the bedrock of all insurers

We’ve long heard of ‘digital transformation’ being a key objective for insurance executives. However, by 2025 it’s expected that successful insurers will have completed this transformation. Digitalisation will no longer be the differentiator, it will be the default. As a result, a new way to drive business advantage will have to emerge – and it will be centred on the use of algorithms to drive business decisions.

This is not a new concept. Gartner describes ‘algorithmic business’ as the ‘industrialised use of complex mathematical algorithms pivotal to driving improved business decisions or process automation for competitive differentiation’.

We’ve already seen some insurers start this journey in their claims function. Companies, including Aviva, have long automated decisions concerning whether a vehicle is deemed a total loss or not. However, the trend will become much more prevalent, with Gartner research predicting that, by 2023, over 33% of large organisations will have analysts practicing decision intelligence, such as decision modelling.

 

3. The customer will see positive change as they interact with their insurer

It’s clear by now that COVID-19 will fundamentally change how insurance is done – both in terms of how customers want to interact with insurers, and also how insurers need to adapt. While we hope this pandemic won’t be with us forever, it has opened the eyes of many executives to what is possible within the customer-facing parts of their organisation.

From my discussions with insurers, many have commented on how well employees and customers have adapted to the new normal. While there were initial logistical hurdles in virtualising contact centres, they’ve been impressed at how well staff have adapted under pressure to deliver what customers and shareholders expect. Many are likely to follow the approach of Lloyds in allowing staff to work remotely for the foreseeable future.

 

4. Prevention will be prioritised over payouts

Insurance has long been society’s safety-net, protecting us when something goes wrong in our lives. Yet, it would be to everyone’s benefit if risk could be avoided altogether. The use of telematics to assess the risk of younger drivers was the first big industry push here, but by 2025 we will see this becoming ubiquitous across many other products and customer demographics.

The recent example of Munich Re’s acquisition of IoT service provider Relayr will benefit manufacturers with a ‘pay as you use’ model. This will enable them to be more flexible and react faster to market changes. The IoT Observatory is also exploring new ways that data extracted from connected sensors and devices can help to transform risk assessment and empower insurers with data.

This is no small step for any traditional insurer. But it is one that puts a truly customer-centric lens on the service that insurers deliver. Data-driven risk prevention allows for significant product differentiation, taking insurers out of their comfort zone and enabling them to explore whole new opportunities.

 

5. Fraud prevention must shape up for a post-pandemic world

Come 2025, we will be living in a very different world with new risks that require novel insurance solutions to resolve.

One of the largest looming threats is insurance fraud. Analysis from the Insurance Fraud Bureau shows that fraudulent claims rose by 5% in 2019, and there are concerns the current economic climate could see this rise even further. In the aftermath of the 2008 Financial Crisis insurance fraud rose by 17%, and there’s no guarantee this won’t happen again on the back of growing practices like crash for cash fraud and ghost broking.

Putting in place an effective defence mechanism to intelligently detect, prevent and investigate potentially fraudulent claims will be an essential requirement by 2025. A soft defence is a liability while those that take fraud detection seriously will drive a more profitable outcome. This is especially true when it was announced recently that close to 20% of each policy premium is goes to cover the cost of fraud.

Insurers must be holding a finger to the wind during this unusual time, as many of the themes and patterns emerging now will shape the industry going forward. Insurers must figure out how to adapt their decision-making processes now, to take on an unpredictable and exciting future in insurance.

 

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VOLATILITY IS CRYPTO’S BEST FRIEND

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Stephen Ehrlich, Co-Founder and CEO at Voyager Digital.

 

Volatility is good for crypto. It serves multiple purposes as the whole crypto ecosystem matures, which we have to remember is an industry and technology that is still only just over a decade old. New and emerging industries are by their nature volatile as they move towards mainstream adoption. But the volatility attracts people, investors and technologists, who drive the pace of adoption forward and as it grows, volatility naturally decreases. In the case of Bitcoin, its volatility has steadily been decreasing over time and even the recent sharp moves have not seen such a big rise in volatility compared to historically (see chart below).

Chart showing Bitcoin Price and Volatility

Source: https://www.buybitcoinworldwide.com/volatility-index/

Volatility continues to attract participants as it is unquestionably in our human nature to be drawn to assets that are subject to rapid price appreciation. Throughout history there have been numerous asset bubbles that have burst, with Dutch tulips of the 1600s being the one that most referenced in relation to crypto-assets. But do tulips really provide any utility apart from looking and smelling good? Many crypto-assets actually provide a purpose, a utility, and serve as the backbone to new technology protocols upon which useful apps are being built. This is why we are seeing greater adoption and as the whole market continues to grow, we are now seeing institutions embrace Bitcoin by diversifying into it as an alternative store of value. This is why volatility is good for crypto. But another harsh reality is that it allows people to learn about the risks, as well as the rewards, of getting involved. Hopefully this is done with the assistance of their chosen broker or through educational webinars, video, and other collateral.

Yes, there will be many that will get their fingers burnt, especially if they employ leverage into their trading without a disciplined approach to managing risk. The same can be said of the internet boom and bust in the late 1990s and early 2000s that saw many a “dotcom” go bust. Leverage was around in those days too, so unfortunately many people learnt the hard way, but it is a necessary evil for the industry to become even more established. For Bitcoin, we have seen multiple bubbles burst, with 2017/18 being the last cycle and soon after the sceptics were suggesting the end for crypto-assets was nigh. But those who see the technology’s potential were keeping their heads down and building amazing platforms and applications. If we take a look at Bitcoin today, it’s clear that the end is nowhere near.

Volatility also attracts the attention of regulatory authorities, another natural evolution of nascent industries. On occasions though, there can be overregulation. Whilst the sentiment behind the UK’s FCA ban on retail investors being able to trade crypto derivatives is right, in respect to trying to provide greater investor protection, it can limit choice and ultimately drive investors to offshore brokers that may afford much less regulatory protections. If an investor really wants to employ leverage in their trading then they’ll find a way to do it, so perhaps rather than an outright ban, perhaps limit the amount of leverage they can use instead.

Bans certainly don’t help liquidity and are actually counterproductive. We’ve seen multiple decisions to “ban” crypto reversed as authorities realise that people simply circumvented it by using a VPN or other means to buy Bitcoin. India is now set to vote on a crypto ban, but at the same time they are due to introduce their own Central Bank Digital Currency, which in itself sends out mixed messages. As governments become more knowledgeable on crypto-assets and understand how they are totally borderless, bans are likely to become less and liquidity will continue to improve further.

Coinbase’s prospectus filing and the fact that the SEC is allowing this anticipated $100b direct listing to come public, with significant consumer involvement, is further acceptance of digital assets by the authorities. The continued evolution of the industry going mainstream and public companies vetted and allowed to move forward by the SEC, foreshadows the long-term outlook by the SEC that this industry is here to stay and regulation and acceptance of digital assets as an asset class is forthcoming. Regulation adds legitimacy to the industry and will attract a broader audience of investors and participants, as oversight gives comfort to a larger group of investors.

Regulation is very important, but it needs to find the balance that protects consumers, yet also fosters adoption of what is a truly ground-breaking technology and asset class. So, for those people that complain about crypto markets being too volatile, we NEED volatility in order for the whole ecosystem to thrive.

 

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