For financial institutions and online trading platforms, it’s becoming increasingly obvious that quality content is needed to keep users informed and engaged. In fact, the rise of the importance placed on client engagement is a phenomenon across the entire online service sector. It has been well documented that when end-users are effectively engaged through content that is insightful, informative, and valuable to their experience, they are far more likely to stick with the platform or financial institution that provides them this content.
But this begs an obvious question, can FX brokers and financial institutions really afford to keep their platforms constantly updated with content that is enlightening and explanatory, but also of the moment? it seems like a big expectation to have, and a big ask of any size team of content writers and analysts.
There must be another way, and luckily there is something that can transform the way content is generated and delivered in a massive way: Machine-Generated Content. This approach makes creating a mountain of content more affordable than ever, and it is just as useful and engaging for users as any content made by human teams of content writers. Let’s take a look at how machine-generated content can impact the financial sector.
Why is machine-generated content so important?
There are multiple reasons why machine-generated content is so vital to successful client engagement in the modern financial trading world, but the main thing that needs to be understood is that this process is supported by data. Trading platforms that adopt multiple kinds of content delivery systems – such as emails and in-app messages, delivering news and analysis – get a much bigger return on each client. On top of this, clients now expect their experiences to feel tailored to them. They want to feel like they are being personally helped through the investing process, and the most direct way to do that is by constantly giving them relevant content, however markets move quickly, so clients who are kept updated will continue investing and working with the financial institution that is giving them that personalized experience.
In other words, all the evidence is pointing towards end users actively wanting more content as they become more active. This leads to a dilemma for financial institutions as hiring enough copywriters, visual artists, and web programmers to keep apps filled with new content tailored to each individual user just isn’t realistic. Not only is the pace of the financial markets too fast for humans to be able to keep up, but it would be impossible to personalize it manually and it would also cost a fortune to employ enough people to even try.
That really only leaves one viable solution: Machine-Generated Content. Once the infrastructure is in place for content to be created automatically, client engagement teams just need to sit back and watch it be created. This can greatly enhance both engagement through apps as well as websites, and clients will naturally be happier once they have a constant stream of instructive, illuminating and informative news and market commentary, to help them in their trade decision making.
An introduction to Natural Language Generation.
If you’re not familiar with machine-generated content already, you might be feeling a little bit skeptical. After all, the idea of a robot sitting there and typing up an article with beautiful, flowing language just doesn’t seem possible. But this isn’t the realm of science fiction. Machine-generated content is already here, and the technology that allows it to be created has existed for decades.
It can be traced back to the 1950s when Alan Turing started to develop ideas based around artificial intelligence. The ideas were primitive at the time, but the groundwork he laid proved fruitful: just a few short decades later, Natural Language Processing (NLP) was becoming a reality in the world of computer programming.
It is important to highlight at this stage that merely possessing the ability of automatically processing language was never the true end goal. While it is certainly impressive to have computers read and understand written words from millions of documents and data points, it is even more beneficial for financial companies if they can create readable output that brings all of this data together producing content that is accessible and contains the knowledge gained from all of that data, this is where NLG completes the process.
Let’s talk about NLG
The first and most important step in machine-generated content is for the computer algorithms to begin language content determination. This is when it looks at all the different subjects and pieces of data from all of the millions of documents and data-points within its search and then selects which is most important or relevant to the specific focus of inquiry.
Next is document structuring. For the writing to exist, the computer needs to decide which information should go where. Should important info be near the top or the bottom? Should certain percentages be mentioned near each other? These are the questions that it answers automatically in fractions of a second.
Once document structuring is done, the computer goes through aggregation. This mainly involves taking sentences – or bits of data – that are related and combining them into coherent ideas. Non-relevant ideas are deleted here, and similar thoughts are turned into one.
Lexical choice occurs next. This step finds the computer running through a massive lexicon of terms to decide which words accurately match the concepts it is trying to say. In a way, this is where the bulk of the “writing” occurs.
Finally, the realization step happens. This is when the rules of language are applied to the content that has been created. This finalizes the content and makes it fluid to read for all end users accessing the information system.
If you think about it, it’s remarkably similar to the subconscious processes that go into human writing. That’s one of the reasons why machine-generated content is so easily digestible. It turns out that computers can write just as originally and creatively as humans!
A real-world example of NLG
Though it is unquestionably valuable and usable, NLG is still relatively new. With that said, though, there are certain industries and companies that have been early adopters of the technology for commercial as well as operational optimization purposes and have realized the immense value that Machine-Generated Content is able to add to a variety of platforms and sectors as widely diverse as Medical to Meteorological and from Insurance to Legal as well as even some from the Sporting world, at hoopsAI we see the vast opportunities for the financial trading sector creating a realtime value-adding NLG offering for its clients.
Focusing on the financial industry, hoopsAI specializes in the production of Machine-Generated and automated content, using machine learning and pattern recognition to scan large data sets, discover key insights, and automatically generate written analysis, that can take the form of highly technical analysis for professional traders or more condensed news and commentary for less sophisticated readers, and everything in between, delivering educational content as well to ensure the greatest level of engagement throughout the clients journey.
The NLG content engine fits the output into more engaging “themes” using sophisticated algorithms which then realize these data and theme types into “chosen story” formats to best fit the end user, leading to increased client engagement and interaction with the platform.
One of the truly dynamic aspects of the NLG process is its ability to “find the needle in the haystack” in a pile of data, rather than the research requiring countless hours and multiple resources. It saves traders the need to analyze by themselves endless graphs and tables in a process that is more manual and much more time consuming. Now it becomes an automatic process that feels intuitive, and done in real time.
The driving force behind what we are doing is for the financial sector to be able to produce readable insights, and analysis as well as market commentary and news in a way that all consumers can connect with. By providing that, the investors and traders are empowered to engage with the market. Integrating our technology is always going to be invaluable for any financial institution or platform provider.
In recent years and more so than ever before, investors are trading 24 hours a day, with remote working practices becoming more and more the “new normal”. Meaning that it is a must to facilitate and enable the growing demand for constant access to the financial markets, meeting the need for real-time, on-demand, personalized and user centric content as well as news and analysis.
That is quickly becoming the key differentiator between providers, and a key factor in choosing platforms to trade with, and it is this, as we have said already, where the newest and perhaps biggest challenge is to be found. hoopsAI patent pending technology applies NLP, NLG and statistical modeling as well as ground-breaking machine-learning capabilities to generate unique, on-demand news, analysis and research with story types that can be generated in a matter of milliseconds and to the users requirements.
One of the key features of the service we provide is to fit the NLG Content Generation platform output to each individual’s realm of interest and level of expertise, which ensures that content can be tailored, for the best fit to the users needs. For example investors can get personal insights and research based on their specific watchlist or portfolio.
It’s easy to integrate and customize, and can support an endless list of assets, users can even create infographics, something that is hugely beneficial for apps and websites wanting to make financial decisions simple for their users. Bottom line with a simple integration via API or iFrame any broker or financial institution can get hoopsAI real-time content, covering thousands of assets from currencies, commodities stocks to crypto currencies and our coverage includes:
Personal watchlists and portfolios