Machine Learning in Finance: how to drive business with data innovation
Author: Tommaso Motta
In a context which is extremely volatile, the iterative aspect of machine learning in finance is becoming important because as models are exposed to new data, they can independently adapt. They learn from previous simulations to produce reliable, repeatable decisions and results. Despite not being a new technology, it is being heavily used and it is gaining fresh momentum, since only recently they have obtained the ability to automatically apply complex mathematical calculations to big data over and over, faster and faster, so that they can keep up with the humongous quantity of data. This is extremely useful since now more than ever, being able to process data faster and more efficiently is crucial for the development or the survivability of businesses.
What is machine learning
When using the term machine learning we intend an artificial intelligence (for short AI) that through the repetition of different scenarios or interactions, is able to understand the reality it is put in and effectively learn, just as a human being would. The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make decisions in the future based on the examples that are in the system. We classify algorithms used in machine learning into:
- Supervised machine learning algorithms can apply what has been learned in the past to new data using labelled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system can provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
- Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labelled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabelled data. The system does not figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabelled data.
- Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Why is machine learning important
The resurging interest in machine learning is to be found in the same factors that have made data mining and Bayesian analysis more popular than ever. Growing quantities and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage made it possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results even on a very large scale. Furthermore, the models created are being more precise, and thus organizations have a better chance of identifying profitable opportunities or avoiding unknown risks. If we consider only the financial world, we still find a massive presence and use of machine learning technology, due to the abundance of data that characterize the finance industry. Moreover, applying machine learning technologies are proven to lower operational costs, boosting productivity and thus revenues and reinforce the security, by the automatization of critical processes.
How is Machine Learning applied to Finance
Machine learning is currently used in many financial fields, such as:
- Processes, and more exactly in the process automation; machineries have been used to increase productivity through the automation of small and repetitive tasks since the Second Industrial Revolution with Taylor and the whole Scientific Management paradigm. Thought with the introduction of machine learning more complex and resource draining activities can become automized (i.e. chatbots, automated call-centre etc..)
- Security, machine learning algorithms can identify buying patterns of retail clients and, basing on those patterns, decide whether or not a certain transaction is suspicious (i.e. a client that has never withdrawn more than 100 $ and suddenly withdraws 10.000 $ might be considered suspicious or a client whose purchases are mostly primary goods suddenly buys luxury goods) and ask for additional identifications to process the purchase. Moreover, machine learning algorithms can be used to track enormous amounts of microtransactions and stress them out as potential money laundering activities.
- Trading, as many algorithms are being used to buy, sell or hold stocks, there would be a significant improvement if those algorithms could learn from past mistakes, or from personal preferences of the single person using the “learning” algorithm. Putting into perspective, if a retail customer buys always only when a determinate type of stock falls down the support line, a learning algorithm could automatically buy it when such event happens, granting for the customer an edge over other players who have higher reaction times.
- Robo-advisory, online applications that dispenses support by providing financial guidance and service. To these days there are two main applications: Portfolio management (where the algorithm creates and manages a portfolio based on the client’s risk preferences and desired earnings) and Recommendation of financial products (used just as the human counterpart but are cheaper and have faster response times).
Some FinScience solutions
Recognising that introducing machine learning algorithms within the finance world would lead to having a competitive advantage in terms of knowledge, Finscience has proposed several solutions
Finscience has developed through the years a platform where more than 1 TB of information is processed yearly, that aims both to support the user for making financial decisions and promotes continuous control of those decision. This is possible through the realization of a dashboard of key performance indicators, which can grasp weak signals that better suits the user, thanks to a high index of customizability.
Based on the analysis of traditional financial data and Alternative Data, these models are used to forecast stock trends and build investment strategies. The proposal of Finscience in this field is a hybrid quantamental strategy, which aims to use both AI – such as cloud database, alternative data and machine learning technologies – and human resources, to both reduce emotional thinking and thus avoiding useless risks and to respond quicker to changing conditions.
Knowledge Discovery Machines
It is a solution that aims to convert paper data into digital data and categorize them, in order to have an organized database. The process is completely automated, and divided into 4 steps: digitalization, so that data can be converted into digital format, classification, where information are grouped into categories depending on the content, labelling, where each class of information is further classified into sub-classes, and finally the enrichment, where additional data (normally customer satisfaction or reviews) is added in each category.
The benefits of these solutions are several, for instance the searching activity is severely eased and optimized, time is saved – so other, more crucial activities can be addressed – and moreover hidden data can be found.