Machine Learning in Corporate Finance: the 4.0 Horizons of the Financial Market

9 March 2018

Technological innovation and the world of finance seem to have proceeded in the same direction for years now, further accelerating the pace with the entry into the field of Machine Learning and Artificial Intelligence.

The high volumes of data, the ability to access to accurate historical documentation and the quantitative nature of the Finance universe, make it one of the most suitable fields to integrate machine learning and free that way the finance professionals from activities that are necessary but of low creative value. “The digital transformation of finance is an essential, urgent and continuous task, which requires managers specialized in corporate finance, projected towards the change and able to intercept the trends of the global market, identifying at the same time the tools necessary to face it,” says Emanuele Bajo, Scientific Director of the Full-time Master in Finance and Fintech at Bologna Business School.


The machine learning is an area of Artificial Intelligence that allows computers to make independent decisions: instead of providing the computer with a set of instructions for carrying out a task, it gives instructions on how to learn to do it. This particular ability to self-study, combined with the analysis of big data and special algorithms, has assumed a fundamental role in various financial transactions, from risk assessment to loan approval.


The robo-advisors have long been employed, with minimal or even no human intervention, in the management of the customer portfolio and in the forecast of investments. Based on mathematical formulas or algorithms and calibrating according to market changes, they are able to invest the client’s assets in stocks, bonds, futures, real estate or mutual funds, while taking into account both the risk and the investment goal decided by the user. Likewise, machine learning algorithms can be trained on millions of examples and very high volumes of data to detect trends that could influence insurance decision making and loan authorization. The machine learning is also used for the analysis of the sentiment, with the aim of replicating human intuition in financial activity, in order to discover new trends and market signals.


One of the sectors that benefits most from the advantages of machine learning is undoubtedly that of anti-fraud. While the previous systems of detection of anomalous activities or behaviors depended on a list of risk factors and a series of complex pre-established instructions, today self-learning allows the active and continuous re-calibration of the activities necessary to keep pace with the countless creative ways in which security can be violated. In the years to come, this ability to cross and analyze heterogeneous and unstructured data to prevent abuse, will be further assisted by the use of security systems based on biometric data such as facial conformation, fingerprints or voice.


Not only risk and safety prevention, but also and mainly financial transactions: more than half of the trading on the stock exchange – that is the choice, execution and management of orders – are already today left to the decisions of a machine. The High Frequency Trading (HFT), for example, is a mode of intervention on the markets that uses a series of purchase and sale algorithms performed in fractions of a second, which are therefore impossible to be executed by a human operator.


The use of machine learning seems to have a very broad horizon, which in some points completely comes out of the financial sphere and approaches that of the customer service in a broad sense. Chat bots and conversational interfaces become valuable interactive tools for communicating with customers, who can query them on the expenditure of the previous months for home maintenance or on average monthly savings. In addition, a robot advisor could suggest changes to the investment portfolio, while digital personal assistants can offer tailor-made financial products, as we see already happening in the insurance industry.


Through machine learning, a financial organization can innovate its working methods and increase efficiency, output and, ultimately, profitability. Understanding the importance of this evolution opens the door to numerous opportunities, but also to the need to rely on a management capable of recognizing change, accepting and managing it. The technological innovation will profoundly change the structure of the financial market and only through the deep understanding of the digital technological innovations will be possible to survive and compete with new business modes,” adds Professor Bajo. The Full-time Master in Finance and Fintech at Bologna Business School trains young professionals to act as a link between the management and the computer scientists, becoming the engine of the development of new digital financial activities.



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