Deep Machine Learning
Blogs on 10th June 2019
Posted by Ivan Chelombiev on 15/04/19
Deep Machine Learning – How it Can Bring Massive Gains in the Financial Industry and Why the Winner Will Take it All
The remarkable advances in machine learning we have witnessed over recent years are old news within the technology sector. Expansions in machine learning span decades, even centuries; it has become a commonplace element of our day-to-day lives and it is now obvious that machine learning is here to stay.
As of now, in 2019, machine learning technology is only just beginning to realise its potential. And it’s making a lasting mark on almost every industry: from search engines to online shopping websites and social media platforms. Companies in all sectors are becoming increasingly reliant on machine learning to boost their revenues by predicting what their markets and consumers will want next. How do they do that though?
Let’s see how masses of big data (made available during the digital boom), its analysis and machine learning are swaying business performance across all industry sectors.
Using Machine Learning to Maximise ROI in the Financial Industry
Rather than asking whether businesses can benefit from big data and machine learning, the question we should be asking now is who will benefit from it most.
One likely candidate is the finance industry, where a plethora of data and complex financial markets are ideal contenders for the efficiencies offered by machine learning. An additional benefit for financial institutions is the consumer trust inherent within the money management sector. In contrast to services like social media, people are more willing to allow financial institutions to access their data for the purpose of designing product solutions. This use of data will in turn fuel even greater machine learning advances.
Various machine learning techniques are already in operation at banks across the world. Processes such as fraud detection, risk management, and credit decision-making have all become largely automated. Identity checks using data analysis are now more commonly performed by a machine than a human.
Ultimately, most players in the financial world are driven by one common goal: making money. And the holy grail for financiers has always been to devise new methods that increase the efficiency of investments and multiply their capital. Machine learning has been identified as the tool-of-choice for achieving this, leading many top financial institutions to use machine learning for managing investments, trading assets and calculating hedging strategies.
The Stakes are High – Can We Use Machine Learning to Change the Future Outcomes of a Process?
This is a strategy that seems to be paying off. Consider the case of Bill Banter, a professional gambler who decided to automate his horse racing bets using a machine learning programme. Banter’s approach was so successful that he secured entry to the exclusive billionaires’ club by staking big on races in Hong Kong. His case is quite remarkable, given how few players achieve such lucrative results.
The key to his success was that he was the first person to extensively engage machine learning techniques in professional gambling, outperforming all other players by such a large margin that he became the runaway market leader.
This strategy can be also applied to other niches in finance – as financial systems are a “second order chaotic” (as mathematicians label them). This means that predictive information can actually change the future state of the system itself.
Therefore, exploiting these markets will give the first player in the game an extensive advantage. It is hence not surprising that many quantitative hedge funds are vying to create an investment algorithm that will leave others staggering behind.
The Next Evolution: Artificial Neural Networks
Investment, fraud detection and insurance strategies are all well-established machine learning applications, predominantly relying on trusty tools such as linear models and gaussian processes.
Although transparent and easy to interpret, these have limited learning capacities. The real reason why machine automation has hit the headlines lately is due to an internal shift of power within the field, and the evolution of deep learning models called “artificial neural networks”, which are increasing the technology’s popularity as they outcompete classical designs by a hefty margin.
Although neural networks have been around for decades, it is only through recent hardware improvements and an influx of data (thanks to global digitalisation) that they have gone from strength to strength. Unlike other machine learning models, neural networks have a large capacity that enables them to outperform their predecessors. The dominance of neural networks first became apparent in the field of computer vision when, in 2012, a deep neural network entered the ImageNet competition for the first time, alongside other algorithms that are tailored to classify images. The neural network managed to achieve an error rate of just 15%, surpassing the second-best algorithm by a whole 10%. Today, the ImageNet competition features no algorithms other than neural networks, which have even started to outperform human operations.
These deep learning models have yielded a wide range of practical applications, which – while not being specifically designed for the financial industry – have provided massive benefits for it nonetheless. One such use is facial recognition, which has allowed for the simplified and automated identity verification of retail customers. The efficiency of this application offers a competitive alternative to branch-based retail banking, propelling many new digital-only banks into the traditional consumer markets they may not have otherwise accessed.
Similarly, voice assistants are becoming easier to implement; with much of the core functionality already created and open-sourced, these tools are giving digital-based financial institutions a new edge in competitive sectors.
Machine Learning, Neural Networks and Finance – The Future?
In the not-too-distant future lies the realistic prospect that neural networks in the financial industry could leave traditional traders and investment bankers eating their dust. Although neural networks were traditionally used only to perform basic classification tasks (distinguishing cats from dogs, for example), their capacity actually allows them to process extremely complicated information, including visual and auditory data. Indeed, in the past three years, neural networks have evolved to perform numerous tasks that go beyond basic data management; not only can they analyse and interpret data, they can even generate their own photorealistic images. What’s more, a machine recently demonstrated a superhuman capability at playing Go, something that was never considered possible before.
While it’s impossible to say what the future of neural networks technology holds, it is most likely that continued advances in machine learning will change not only the financial industry, but also the economy as a whole. And the individuals at the forefront of this change could well find themselves being the newest entrants into that billionaire’s club.
Undeniably, deep machine learning is one of the most exciting developments of the century, and a potential precursor to the largest technological change in modern history. Whether or not this will be a beneficial change, however, remains to be yet seen. After all, the revolutionary physics of the 1910s and 20s brought not only unseen prosperity to the world, but also the grim spectre of nuclear war. Therefore, it is our collective responsibility to hold decision-makers accountable for managing these new technologies, and to steer developments in machine learning towards a positive socioeconomic future.