Machine learning in finance: beyond prediction algorithms, what is the true value?

Machine learning continues to revolutionize the financial industry by offering highly accurate predictions on market trends, customer behavior or the evolution of an investment portfolio. However, the true power goes beyond the prediction algorithms. This technology can be used to automate financial processes, detect fraud, or personalize banking services.

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In addition, it is possible to use machine learning to create a company culture that improves the performance of fintech startups. How? Well, with online training programs, management systems or collaboration and gamification tools, to name just a few.

By harnessing the power of machine learning, FinTech companies can also improve their operational efficiency, reduce costs, and deliver better user experiences.

Technology improves the skills, motivation and commitment of finance professionals.

Óscar Barba

Co-founder & CTO of Coinscrap Finance

These are the main areas where Machine Learning can add value

Task automation

Machine learning turns manual and repetitive tasks into mechanical ones, allowing employees in the banking sector to focus on activities with greater value for their company. This includes the processing of documentation, fraud detection or risk management. There are many daily processes that can benefit from the use of algorithms.

Improved customer experience

With data analysis, technology helps us better understand user’s preferences and needs, allowing us to hyper-personalize banking services. Thanks to machine learning, entities can recommend financial products adapted to each customer, provide financial advice on a case-by-case basis and improve the customer journey.


Risk management

The industry can take advantage of these developments to identify and cope with risks more effectively. By analyzing large amounts of information, machine learning detects patterns and sends alerts that prevent potential security breaches. Thus, banks make more informed decisions regarding risk management and regulatory compliance.

Fraud detection

This technology can be used to identify patterns and anomalies in financial transactions, helping to detect and prevent deceit. Machine learning algorithms can analyze vast amounts of data in real time and generate alerts when suspicious transactions are detected, allowing banks to take swift action to mitigate risk.

Operation optimization

It can also help optimize banking operations by analyzing huge volumes of information and finding hidden patterns. For example, algorithms can analyze the data contained in transactions to identify areas of inefficiency and suggest improvements in operational processes, which can translate into cost reduction.

And… in addition, the European System of Financial Supervision highlights its importance

The studio “Will video kill the radio star? Digitalization and the future of banking”, of the European System of Financial Supervision (2022), indicates that the information technology revolution, including the rise of Cloud Computing, has facilitated the creation, processing and use of big data and statistics applied to measure and manage financial risk.

“Artificial intelligence and machine learning enable improved detection and monitoring models over existing techniques””

European System of Financial Supervision (2022)

…Such as traditional, most static, credit scoring models. The document indicates that, in the case of loans, that technology can help understand the information, thus expanding the available credit offer and the type of customers to whom it can be offered.

He also mentions other studies that have shown how big data is more helpful in predicting patterns than the traditional approaches, such as credit file data, which many banks rely on. These technologies can measure and manage operational risks and activities such as cyber incident monitoring, anti-money laundering, etc.

Big data can be used for other services such as insurance and investment advice (InsurTech and Roboadvisor). They are extremely useful for measuring the underlying risk of insurance, allowing more contracts to be issued at lower cost. In this way, users take advantage of a wider range of products and services.

Insurers can also use them for other purposes. In the case of advice, machine learning takes advantage of the data provided by investors to create and manage a personalized investment portfolio for each customer. It can even reduce the effect of the behavioral biases of traditional financial advisers (Foerster et al. 2017).

Machine learning helps the financial sector cope with big tech companies

Compared to banks and fintech companies, tech companies have advantages such as: having all the technical knowledge, updated and optimized systems, important customers and great financial capacity. Apart from these benefits, they also have access to a wide variety of data and can enter the playing field without the inheritance or organizational problems of traditional banks.

However, it is also true that they do not have the accumulated experience of entities and startups. Its advantages may be drastically reduced if banks and insurers modernize and incorporate data analytics, machine learning and artificial intelligence tools. This represents a clear improvement to existing services and the ability to attract more users.

The possibilities offered by the algorithms are innumerable and translate into benefits for all the economy sectors. At Coinscrap Finance we apply them to the analysis of transactional data in order to enrich the information and return power to the end user. When we have an enriched overview of our financial situation, we can really make the best decisions and live better.

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About the Autor

Óscar Barba is co-founder and CTO of Coinscrap Finance. He is an expert Scrum Manager with more than 6 years of experience in the collection and semantic analysis of data in the financial sector, classification of bank transactions, deep learning applied to stock market sentiment analysis systems and the measurement of the carbon footprint associated with transactional data. 

With extensive experience in the banking and insurance sector, Óscar is finishing his PhD in Information Technology right now. He is an Engineer and Master in Computer Engineering from the University of Vigo and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca and more.

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