Deep Learning and AI in Fintech

Deep Learning

Financial services have been revolutionised over the last 20 years by increasingly powerful technology such as big data analytics, neural networks, evolutionary algorithms, and machine learning. Now, Fintech is on the cusp of a truly revolutionary moment, the integration of AI and deep learning into financial services. This combination really has the potential to revolutionise money and the global financial landscape in ways we never could have imagined 20 years ago. This is hardly by accident, a 2015 report by Accenture tracked the global investment in Fintech; revealing it has jumped from $930 million in 2008 to over $12 billion by the start of 2015.

Data Explosion

The explosion of data over the last 10 years has been incredible, and has been so vast that there has been little way to comprehend it without intelligent automated support. As analytics advances, so does the need for increased computational power to crunch the big numbers, and AI systems are becoming easier and easier to develop adapt and integrate.

Many companies already use programs like Kasisto, a KAI powered conversational UX, to help to improve customer service and connect customers to products and services. Kasisto are currently pioneering the use of their bot to help people manage stocks and portfolios (check it out here). However, this has been around for a few years now, and it really only scratches the surface of how AI is going to transform Fintech. AI and Machine Learning is being applied across the financial world, this is the next step in financial evolution. Fintech start-ups such as Affirm, Zest Finance, and Kensho, have applied deep learning to improve decision making and financial processing. Zest Finance are redefining credit scores and challenging the use of “little data”, using big data analytics to provide a much more accurate credit score than is possible in the current system.


Machine Learning Algorithms

One of the key features of emerging start-ups has been the use of machine learning algorithms to gain an analytical edge in trading. What tends to vary is the proposed clientele. For example, start-ups like Binatix are aimed more at large data sets and analytics for portfolio and hedge fund management on a wider scale; whereas Williamsburg based Inovance, is attempting to bring machine learning based trading analytics to the rescue of common investors. Similarly, earlier this year, Alpaca (deep learning Fintech start-up) launched Capitalico; a deep learning trading platform, that allows traders to automate their trade ideas without any programming or market investing experience.

This theme runs throughout the evolution of finance, banking is becoming ever-more personalised. Smart wallets like will help you consider, analyse, price, and consider every single thing you purchase, in a way that no bank, financial advisor or human assistant could begin to attempt. Following many years of having “offshored” repetitive tasks to lower-cost locations, PWC have predicted that this financial revolution will lead to “reshoring” and localisation of banking again thanks to the falling costs of automation and AI innovation.

AI analytics can be applied across the entire financial world, the next level of disruption has arrived, and it spells the end for banking as we know it. In the coming years we will see deep learning infiltrate identity authentication, portfolio construction, automated investment, fraud detection, and transactional safety; not a stone will lay un-turned in the financial world. PWC has predicted that the next three to five years are likely to lay the foundations, with “modest, evolutionary gains”, followed by rapid expansion as the technology becomes cheaper, more widely available, and more accepted by the general populace. The partnership of deep learning and Fintech is still very young, who knows where it could take us?

By Josh Hamilton

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