So you want to learn how Artificial Intelligence can help you with your investing desires, then you should read on.
From banking to trading, the finance sector has evolved considerably over the past few years and continues to become an increasingly digital world. With fintech companies such as Revolut, Monzo, Chime and Aspiration giving consumers the chance to manage their accounts digitally via a simple mobile app, it’s clear there’s a demand for fast, efficient financial services that resonate with an evolving audience.
Let’s look at the facts…
• FinTech has become globally mainstream with 64% of global consumers adopting FinTech solutions.
• Adoption rates rose from 16% in 2015, to 31% in 2017, to 60% in 2019.
• 96% of consumers have heard of FinTech money transfers and payment services.
• China and India are the leading FinTech adopters with the UK in the top 10.
• 75% of consumers have used a FinTech money transfer or payment service.
Fintech app and software usage:
• User activity on finance apps has rocketed by 354%.
• 55.4 million millennials aged 23-38 will use digital banking in 2019, with almost 40% of this demographic totally abandoning brick and mortar banks altogether.
• 64% of millennials have at least one full-service banking app on their phone, as well as 59% of Gen Xers and 41% of people aged 55+, according to Bankrate.
• 71 percent of Baby Boomers in the US use online banking services once a week.
• UK users check their mobile banking apps over 7x a week.
To-date, new and efficient services have evolved to solve problems and meet consumer needs. Highlights of modern FinTech providers include:
• Free global transfers
• Contactless payments
• Transparent fees
• Real-time exchange rates
• Improved security
• Built-in budgeting
• Instant spending notifications
• Hassle-free spending abroad
• The ability to withdraw money abroad
• Cryptocurrency exchanges…
But, as digital financial services become the norm and demand increases, consumer expectations also rise as people demand slicker, more intelligent offerings. With technology advancing, banks, brokerages, asset managers and other finance specialists are now expected to up their game in order to be a cutting-edge services provider.
Consumers have an extensive FinTech tick-list. As do finance institutions looking to out-do their competitors. Fintech users in the forex sector, for instance, require tools to provide a personalised, tailored and fast-paced digital experience. The need to tap into extensive data to analyse and understand FX markets is stronger than ever before, with many companies now looking for ways to streamline and automate trading elements and improve investment returns. So, how is this being achieved?
Bank interest rates are negative or close to zero within the EU. And with alternative tech-driven investment solutions taking centre-stage, AI is increasingly changing the financial landscape. AI is being used to improve and enhance the investment model for investment firms, becoming the preferred tool to gain a competitive edge. It’s the new frontier for investment management and finance companies – but what is AI and what does it entail?
While there’s no universally accepted definition, AI refers in general to the ability of machines to exhibit human-like intelligence and a degree of autonomous learning.
What are the benefits of AI?
AI has the ability to:
• Recognise patterns
• Analyse huge amounts of data
• Anticipate future events
• Make informed decisions
• Create rules
• Communicate with others
• Assist with onboarding
• Provide tailored and relevant content
• Answer questions promptly
• Respond to client demands in real-time
• Free-up human staff
• Detect fraudulent activity
• Attract a new wave of educated professionals
• Give companies an exciting dynamic
AI isn’t in the pipelines. It’s not on the shelf waiting to be sold but is already being utilised by some of the biggest financial institutions in the world. According to a survey by the Bank of England and the Financial Conduct Authority, a large number of UK financial firms are currently utilising a form of artificial intelligence known as machine learning (ML) with usage predicted to escalate in the coming years. Indeed, two-thirds of respondents claimed they’ve already integrated ML into their business model.
Machine learning is an AI branch that uses algorithms to parse data, learn from it and then make real-world predictions. A rule-based system which carries out tasks based on specific instructions, will perform a task the same way each and every time (regardless of positive or negative outcomes). ML systems, however, are different as they revolve around learning from a particular experience. ML systems are trained by exposing the algorithm to increased amounts of data. Data scientists train machine learning models with existing datasets and then apply well-trained models to real-life situations.
While anomalies such as the 2008 financial crisis do exist in data, a machine can be taught to study the data to find ‘triggers’ for these anomalies, and plan for them in future forecasting as well. Therefore, ML AI systems are smart enough to detect and work with data that may otherwise effect predictions.
The more data you have, the better your algorithms will be. And, thanks to large amounts of historic data in the finance sector, AI and ML are a perfect fit. Banks, for instance, have enormous amounts of customer data such as deposits made, withdrawals at ATMS, purchases at point-of-sales, online payments, KYC profiling and so on. AI can help utilise this rich data in order to provide personalised financial services.
With machine learning, every customer can have their own private adviser, offering advice completely tailored to their unique situation. However, just having a lot of data is not sufficient anymore. You also need high‐quality data, or in the words of Peter Norvig – Director of Research at Google, you need better data: “We don’t have better algorithms, we just have more data. More data beats clever algorithms, but better data beats more data.”
A particular focus should be given to first-party data collection which is provided by a specific target audience and has significant business relevancy.
Major players within the finance realm are already using AI and ML to help them use the huge volumes of data they have to hand.
AI use cases in the finance sector include:
• Process automation – to reduce human error and carry out repetitive tasks
By replacing manual tasks with process automation, companies are able to optimise costs, improve the consumer experience and scale-up services. Examples of process automation in finance include chatbots, call centre automation, paperwork automation, gamification of employee training and more.
Well-known bank JPMorgan Chase launched a Contract Intelligence (COiN) platform which used a machine learning technique known as Natural Language Processing to process legal documents and extract essential information. This proved to be a huge time-saver. While a manual review of 12,000 annual commercial credit agreements would take up 36,000 hours of manual labour, AI reduced this timeframe to just a couple of hours.
What’s more, chatbots and virtual assistants such as those used by DBS Bank, Standard Chartered Bank and TD help answer questions intelligently. According to a company report by Juniper Research, chatbots will be responsible for $8 billion annual cost savings by 2022.
• Fraud Detection and Improved Security
Millennials are one demographic that deeply mistrusts the banking sector, largely due to the financial crash. This is significant considering almost half of traders fall into the millennial bracket. On the upside, millennials are not favourable of traditional services, looking instead of digital-led alternatives. Improved trust is being driven by AI developments.
While threats within the sector continue, AI and machine learning algorithms are showing to be experts at fraud detection. By using AI to monitor consumer habits over time, it becomes easy to identify suspicious account conduct and actions that are not typical. AI evaluations of a transaction take just a couple of seconds and therefore real-time warnings and action can be taken – such as prohibiting an illegal transaction before it’s too late.
Security machine learning is currently being used by FinTech companies including Stripe. According to the payment service provider, machine learning helps to block fraud on the network. Stripe serves millions of businesses around the world with machine learning infrastructure making hundreds of millions of predictions across many machine learning models. Stripe competitor, PayPal, also acquired the machine learning-powered fraud detection start-up, Simility in 2018, showing how the spotlight is increasingly being shine on AI technology and its benefits.
AI can be said to be based around four pillars of transformation for the investment sector.
#1 Generating Alpha – access to big data can lead to big organic growth opportunities.
#2 Enhancing operational activity – improving day-to-day functionality and freeing up time.
#3 Improving content and product distribution – tailoring content to investors’ needs and preferences in real-time.
#4 Managing risk – AI can create a more compliant environment and bolster risk management functions
AI is being directly applied to the investment sector and has many benefits.
• Better predictions of future economic outcomes
Sophisticated data analysis supported by machine learning and increased computer power translates into better economic outcome predictions. AI can lead to a more informed portfolio construction process thanks to increasingly rich statistical models and aids intelligent risk management strategies.
Essentially, by analysing and understanding years of market data, AI tools can effectively identify new trading opportunities.
Investors can also save time knowing that their investment portfolio will be automatically adjusted thanks to algorithmic programs. Such technology also puts less strain on the human staff of financial firms previously required to monitor and react to changing market movements and events.
• Defence against emotional decision making and biases
Humans are often not rational when it comes to making investment decisions. Emotions can lead to behavioural bias and poor judgement, especially in the case of loss aversion, but AI can help eliminate this problem. By allowing a system to create real-time strategies to achieve specific objectives, there’s no room for a last-minute decision or human error.
AI-enhanced trading platforms can be tailored to a wide range of end-users including clients, FX brokers, money managers and business introducers, ultimately rationalising the entire investment process.
• Stronger advisor-client relationships
Thanks to AI, financial advisors and investment firms can now automate certain elements of the advisor-client relationship to improve the overall customer service. From AI-enhanced initial communications to risk profiling, consumers can pursue their investment interests and goals much quicker than if the entire process was manual.
Intelligent information management solutions also make it easy for investment firms to handle, secure and process the relevant documentation in an orderly and compliant way.
• Transparent, efficient, data-accurate platform
Consumers are increasingly looking for brands that do what they say on the tin. The digital world is causing more and more consumers to become impatient and seek advanced services, with little concern about brand loyalty. AI works to give the end-users what they want offering a transparent, data-accurate platform to work from.
So what exactly does 8topuz do?
We are an artificial intelligence and machine, learning provider. 8topuz uses a proprietary neural network that analyses the market’s depth and looks for patterns of pre‐set mathematical models (such as fractals, chaos and waves) that allows it to understand and forecast market trends on a real‐time basis (in fact it is a trending program).
By employing human knowledge, discretionary trading, artificial intelligence and oversight, our system helps members cut risk, increase profits and simplify investment decisions.
With positive results across a consecutive time period, there is evidence to suggest to clients the ability of the trading systems and the people behind it. As always, clients should seek independent advice on their personal circumstances prior to investment into any CFD product.
The system implemented by 8topuz is not static and can be applied to any account and to multiple markets such as FX, Metals, Indices and Commodities.
It’s intelligent and self-learning, with the ability to analyse and recognise patterns of accumulated historic data over a multitude of data points to better predict and react to future outcomes.
The neural network is supervised, multi-layered and composed by variable nodes.
• By feeding the network with pre-set data, the system creates its own real time strategies to achieve the established objectives.
• In only milliseconds, our system can choose the most appropriate strategy among more than 30,000 options in every single market condition.
The network’s main tasks are as follows:
• Pattern recognition
• Process optimization, and Signal validation and information processing
• Usage of analysed market intelligence and troubleshooting:
If the system identifies a trend that is not followed by the market, our software applies a counterstrategy to solve the situation on a real‐time basis, adapting to the new scenario.
During this non‐stop analysis, research and optimization process, all new movements and patterns are stored to be used in up‐coming scenarios, helping solve future market situations.
The A.I implements trades at a high frequency, low volume (HFLV) approach. This means that although there are potentially thousands of trades over the terms of the investment, only a tiny percentage of the account is utilized per trade.
The A.I system is constantly monitored, updated and overseen by a team of experienced traders and coders. This ensures that human intuition and foresight are not removed from the product offering, rather only the inherent emotional risk with discretionary trading.
The 8topuz Digital Wealth Management application will rationalize the entire investment process.
The parameter-driven customization features deliver a solution with a unique level of flexibility to meet your needs.