25 Feb, 2020
How is Artifical Intelligence (AI) transforming finance?
This informal CPD article on How is AI transforming finance? was provided by CFTE (Centre for Finance, Technology and Entrepreneurship) an education platform that aims to address the needs of financial professionals to upskill in a rapidly changing industry being transformed by emerging technologies.
What is Artificial Intelligence?
When most people think of Artificial Intelligence (AI), vaguely fantastical ideas come to mind. On the contrary, Artificial Intelligence is not a new concept. The field of AI was formally introduced in 1956, at a Dartmouth college conference. AI in the financial sector is ubiquitous, the real question is—how will its transformation grow?
Now, let’s get down to business.
Considering the broad scope of Artificial Intelligence, it is difficult to pinpoint what it is. Further, it has a broad definition which differs from one academic paper to another. To illustrate, we can look at an AlphaZero study that showcased an AI algorithm beating the reigning world champion in chess. Thus, it can be said that Artificial Intelligence is very much related to Neural Networks.
A Neural Network is system software that works similar to the tasks performed by neurons of a human brain. In other words, Artificial Intelligence refers to the ability of a computer program or a machine to think and learn.
Keeping in theme with the topic of this article, let’s discuss a few examples that are commonly leveraged within the financial industry.
Some of the use cases are as follows:
Alphasense is an AI powered search engine that utilises Natural Language Processing to analyse keyword searches within filings, transcripts, research and the news to discover changes in the financial industry.
- Shape Security
Based in the U.S, Shape Security utilises Machine Learning models that are trained to pinpoint fake users, effectively preventing credit application fraud and credential stuffing for their financial institution clients.
- Scienaptic Systems
Having scored over 100 million customers, Scienaptic’s Ether connects structured and unstructured data to offer contextual underwriting intelligence. This provides banks and credit institutions with more transparency when cutting losses.
Having discussed the current applications of AI, let’s talk about other aspects of the financial sector that AI is anticipated to transform. During the Paris FinTech Forum 2020, it was predicted by industry experts that in the following years, the financial industry is likely to experience a disruption of business models as a result of AI.
This makes sense. After-all, over the last decade, we have witnessed the rise of new business models. For example, let’s look at Netflix, Uber and Facebook and their similarities.
Firstly, all three companies are operating on new business models—Facebook is a platform and Uber runs on the gig economy. Further, all of them are disrupting their respective industries, whilst using AI to do so. Netflix leverages AI for content creation and personalised recommendations, Facebook’s image recognition tool is built on deep learning while Uber uses AI for risk assessment, route optimisation and more.
With this information in mind, it is hardly a stretch to think that AI will soon transform business models in finance.
Let’s look at Revolut, a challenger bank based in the UK. While Revolut is not necessarily a very solid case-example of how AI is transforming business models, it does indicate that the trend is burgeoning.
Due to its digital-only nature and lack of physical branches, Revolut needed an innovative method of on-boarding its customers. Enter computer vision. Computer Vision refers to the ability of machines and computers to process and interpret visual data. In layman terms, it is Artificial Intelligence applied in a visual sense.
The three components to Computer Vision:
1. Object classification
2. Object localisation
3. Object detection
By getting their users to simply take a selfie, Revolut is able to utilise object classification and detection to discern and accurately identify the presence of individuals in the picture. This is similar to how Facebook’s image recognition tool works, except that Facebook also uses object localisation to indicate an individual’s presence within a photograph with a bounding box.
Hitherto, we have witnessed how AI transformed finance, disrupted business models and created even new ones. Thus, it is only a matter of time before it begins to impact business models within the sphere of finance. And while examples as such are yet to become widespread, rapid technological advancements and an increasing culture of experimentation may soon lead to the operational nature of financial institutions being redefined.
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