This article was a collaboration with Óscar Becerril

An industrial revolution can be defined as a period of significant economic and social transformation that occurs when there are radical changes in production methods, technology, working organizations, and infrastructure. During an industrial revolution, a society experiences a drastic transformation in the way it produces goods and services, which in turn has a profound impact on the economy, culture, politics, and in people’s daily lives.

The first industrial revolution took place in Great Britain at the end of the 18th century and was characterized by the transition from an economy based on agriculture to an industrialized economy driven by machinery and mass production. The use of new technologies, such as the steam engine, the mechanical loom, and the locomotive, revolutionized the manufacturing and transportation processes, allowing a significant increase in production and a reduction in costs.

Subsequent industrial revolutions, such as the second and third industrial revolutions, were characterized by advances in mass production, electrification, automation, and the use of information technology. These technological advances led to increased productivity, the emergence of new industries, and the transformation of agricultural communities to urban centers.

The fourth industrial revolution refers to the current era of technological transformation that we are experiencing, characterized by the convergence of digital, physical and biological technologies that are generating radical changes in the way we live. Unlike previous industrial revolutions, which focused primarily on automation and mass production, the fourth industrial revolution is marked by the digitization of almost every aspect of life.

This industrial revolution is based on advances in areas such as artificial intelligence (AI), machine learning, robotics, nanotechnology, biotechnology, cloud computing, augmented reality, 3D printing and the internet of things. These technologies are interconnected and are being integrated into various sectors and fields such as manufacturing, healthcare, transportation, energy, agriculture, education, and more.

AI enables machines and computer systems to perform activities that could previously only be performed by humans, such as data processing, complex analysis, decision making, and task automation. This will have a profound impact on the industry and the economy in general. AI is improving efficiency and productivity, lowering costs, and accelerating innovation in many industries. For example, in manufacturing, AI is used to optimize production processes, predict equipment maintenance, and improve product quality. In the service sector, AI is applied in areas such as customer service, financial analysis and inventory management.

AI is also driving new opportunities and business models. Companies are using AI to create innovative products and services, such as virtual assistants, autonomous vehicles, personalized recommendation systems, and robot-based healthcare.

Like the Industrial Revolution which had significant social and economic implications, the widespread adoption of AI also poses challenges. AI-powered automation can lead to job losses in certain sectors, requiring adaptation and retraining of workers. Ethical and legal concerns also arise about data privacy, algorithmic bias, and the impact on machine decision-making. For example, what liability a robot-driven car may have in an accident from a legal and ethical standpoint.

Machine learning is a key component of artificial intelligence, and it relies on the ability of machines to learn and improve from data without being explicitly programmed for each task. Machine learning algorithms allow machines to analyze vast amounts of data, identify patterns, and make decisions or predictions based on those patterns. AI uses neural networks, which are models that imitate how the human brain works.

The use cases of AI in the financial sector are very promising, generating a great opportunity to better manage credit portfolios, increasing financial inclusion and improving risk management. Financial institutions that take advantage of these opportunities will have an better position over those that continue to work with tools and schemes from the previous century.

AI can also be used to monitor customer transactions and financial behaviors in real time. This will make it possible to detect suspicious activities or risk indicators more quickly and accurately, such as late payments, sudden changes in spending patterns, or unusual transactions.

By combining these AI techniques with the knowledge and judgment of credit experts, financial institutions will be able to gain a more comprehensive and accurate view of the economic solvency and credit risk of prospective clients, helping them to make more informed decisions and mitigating the risks associated with the credit process.

It is relevant to point out that AI will end up becoming a great tool that can be used by any commercial company to generate and evaluate its credit portfolio and the risks associated with its customers.

Today, is a reality that AI is beginning to be integrated into the operating models of many financial institutions around the world, with uses that range from having elements that strengthen the conclusions of traditional credit analysis in support of decision makers, to cases that use credit granting models based on parametric models, generating high-quality decisions very quickly, and allowing the significant reduction of personnel that previously performed credit analysis tasks, with the consequent savings in operating costs.

Use cases in the financial sector continue to evolve every day and constitute a fertile field for innovation. An interesting example is a Canadian company ( specialized in the application of AI to the granting of SME credit, arguing that SMEs globally face two very strong problems: Their inconsistency or instability in the production of reliable financial information, and a negative bias because they are considered a high-risk sector by many banks.

This company has developed a model, based on the analysis of credit information of SMEs at a global level, which allows to determine the payment capacity of the SME, quantifies its possibility of bankruptcy, and even predicts the moment in time in which it is most likely that this could happen. On the other hand, it is a model that learns every day from the new information available, and self-qualifies the percentage of reliability of its predictions, based on the behavior it is observing in the market. This company recently won the “Fintech Startup of the Year-Lending” award at the 2023 Banking Tech Awards USA.

Definitely the arrival of AI will mark a before and after in the financial sector. In the case of credit analysis, and in general in all aspects of banking operations, we will see very interesting solutions in a clear line of improvement in terms of efficiency. A very complete credit analysis, which before would have required a large group of analysts and would have cost thousands of dollars, is now available to any Sofom or Non-bank Financial Company, in a software as a service model.

AI is very powerful. By processing thousands of variables in millions of combinations, it is capable of producing results with a high degree of reliability, achieving better results than any other analysis tool. AI can process large volumes of historical data, such as payment records, financial information, and credit behavior, to identify patterns and correlations that may facilitate the understanding of an applicant’s real credit risk. Machine learning algorithms can be trained using historical data sets to learn to predict specific outcomes. This will allow for a much more accurate assessment of a credit score or probability of default, eliminating biases and subjectivity.

In an increasingly unequal world, AI can help to promote the democratization of services and financial inclusion. Financial inclusion is important because it seeks to ensure that all people have access to and can effectively participate in enjoying basic financial services. This includes access to bank accounts, credit, insurance, electronic payments, and other financial products.

Financial inclusion contributes to inclusive economic development, reduces poverty, fosters financial stability, promotes entrepreneurship and employment, improves financial security and resilience, fosters gender equality, and promotes innovation. We think that we are in the very early stages of the application of AI in the financial sector, however, the possibilities are enormous, and we are just glimpsing the first and timid steps towards what will be the financial sector of the 21st century. | [email protected]

Article prepared with the support of artificial intelligence.

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