AI is not a new term that appeared in the XXI century, it remotes to ancient Greece and was launched in 1956, in Dartmouth Conference, by10 scientists. Machine learning is a branch of AI which consists of giving machines access to data and let them learn for themselves. Big Data analyses and gathers information from datasets that are too large to be analyzed by traditional systems. It is easy to see that those concepts are closely linked to each other: AI and machine learning need data to learn and evolve, and big data serves them on a large scale.
Nowadays these are hot topics and are everywhere, therefore, there’s a huge investment to develop these technologies as they can be used in every sector and industry. From the AlphaGo AI to self-driving cars, the intelligent assistants such as Siri or Alexa and the study of agricultural land and pesticides, AI is being implemented on our society and changing our way to live and future perspectives. Likewise, Big Data is also a growing industry and the demand for professionals on this subject is increasing. Since they are closely related, the growth of one lead to the growth of the other, which causes a multiplier effect when investing in these technologies. Financial services industry operates worldwide and is one of the pillars of the economy. According to a Deloitte’s recent article, in 2018 it had a global asset valuation of around 123 trillion dollars. Nowadays, the financial services industry offers a lot of services that can be applied to every sector of activity, since deposit accounts of every kind, specialized and personal loans, insurance, investment advisory, and wealth and assets management, among others. With all these services offered, financial institutions have now more than ever plenty of data in their active deals. As the economy keeps on growing, the higher the demand for these financial services and more money needed to invest and finance new projects. Therefore, the data that need to be evaluated will keep on growing and we have reached a point where this data can’t be analyzed traditionally.
From here we start to see the emerging of FinTechs, which consists in the combination of Finance and Technology.
This is where Big Data and AI starts to appear and one of the things that is being replaced in financial services is credit score evaluation, loans consideration and alternatives and general financial rating of a person or company. The Internet of Things (IoT) brought us a lot of information that, without Big Data, would be impossible to analyze perfectly.
“Fintech, or financial technology, illustrates perfectly how AI/ML is shifting how banking institutions provide financial services to consumers today.”
Samir Dixit, general manager of data, analytics, and AI/ML at Persistent Systems affirmed that “Fintech, or financial technology, illustrates perfectly how AI/ML is shifting how banking institutions provide financial services to consumers today. Back-office operations at banks involve large and complex data sets that are labor-intensive. When handled by robotic process automation [combined with] AI/ML, there are significant savings on time and costs when performing tasks such as ‘know your customer’ checks, where the identity and address of the customer are verified. The loan process itself is also labor-intensive. With AI/ML, the ability to reduce costs and offer loans at more attractive rates to those with limited credit history is widening a previously underserved market.” This way, not only is there an improvement inefficiency in back-office operations as there is more capacity for front-office operations.
In its essence, financial services are in a big part the negotiation of money, therefore, the decisions made must be extremely correct and have the minor mistakes possible such as risk evaluation.
For example, in 2017, Ayasdi, a leader in machine intelligence software and a pioneer in enterprise-class intelligent applications, announced the release of a new intelligent application called Ayasdi Model Accelerator. This machine could be applied to a variety of modelling challenges, especially in “risk-modelling problems such as loss-given default (LGD), probability of default (PD) and other regulatory modelling problems. These models typically make use of simple regression techniques to support the simplicity and explain ability requirements associated with regulatory oversight. However, Ayasdi Model Accelerator supports a range of approaches and it can model exceptionally complex problems.” (Press Release, 21 June 2017).
Related to the risk-management topic, AI is also getting into the world of financial markets and investments/trading. Both investments and trading require the analysis of different data and ratios, which gives AI and Big Data another chance to be applied in financial services. The objective of investors is to look for companies with a good intrinsic value and invest in them, while traders try to predict the stock behavior by analyzing its price, volume and other indicators evolution and even look for historical patterns that may anticipate a stock’s movement. What if you could have both in one? With the developing of AI and Big Data, Robo-Advisors are entering the financial markets to help and complement every type of investor or trader. By analyzing financial statements, stock movements, macroeconomic scenarios and other factors, robot-advisories are improving the work of professionals that need this information to trade and invest. According to a CFA Institute report, a use case adapted to the theme “Smart Cities” helped a fund manager to increase is returns in 25% in two years. Without the help of this AI and Big Data analysis, research like this would be way more expensive, not so exact and would take much more time, which could lead to indicators that appeared later and wouldn’t give so much return.
These are just some examples of AI and Big Data in the financial services industry, but they are getting in every type of service and industry and improving human work in a big pace and, with rapid technological advances and improvements that are living today, we may see AI where we never though.
Article published in our January Newsletter
Diogo Marques, BSc in Economics