Decoding the Fintech Universe

10.11.23 07:42 PM - Comment(s) - By alberto martellini

A Methodology for Categorizing Innovative Businesses.

As growth and innovation advisor my continuous pursuit involves finding efficient and effective means to discover new data and insights to support my clients. However, this task is easier said than done. Throughout the years, I have dedicated countless hours to querying databases using specific filters such as industry codes, financial metrics, and keyword searches. Such approach has proven successful when working with established businesses in conventional industries. However, when it comes to innovative businesses, distinct challenges arise. The applicability of industry codes becomes questionable, and financial data is often scarce and scattered.


Exploring Technological Innovation and Opportunities in the FinTech Industry. As example, I will dive into one of the largest and most innovative industries: FinTech. FinTech emerged in the 19th century when technology was harnessed to facilitate financial transactions[1]. However, it has only gained significant recognition in the past fifteen years, with the rise of challenger banks and shadow banking solutions in the aftermath of the 2008 financial crisis. Nowadays, FinTech is a well-established sector, yet there is still no universally agreed-upon definition for it. As eloquently defined in the Kalifa's review[2],

FinTech is not merely a niche or subsector; it represents a permanent technological revolution, transforming the way we engage in finance.

In this brief research, I propose a different approach to identifying opportunities within an innovative sector, like the FinTech sector. This approach leverages the advancements brought about by new Large Language Models and embraces a semantic rather than a syntactic logic. To harness these newfound capabilities, I explore the concept of clustering FinTech companies together using high dimensional data with numerous features.


Methodological Approach to Clustering FinTech Companies. Classifying FinTech companies necessitates the collection of information pertaining to their business models, customer interactions, data sources, monetization approaches, technology usage, channel strategies, existing partnerships, funding stages, and geographical presence. This information can be sourced from specialist databases as well as news articles and regulatory documents. To be clear, my intent is not to derive a novel taxonomy in the field of FinTech.

Taxonomies are characterized by being mutually exclusive and collectively exhaustive[3], therefore, such an approach would be overly restrictive for this analysis.

I opted instead to create a classification based on clusters that are potentially overlapping to emphasize the importance of a less defined and constantly changing sector, as previously described.

My proposed methodology can be summarised in the following steps.

  1. Access relevant information regarding FinTech companies, such as their websites, news articles, and regulatory documents.
  2. Employ machine learning algorithms to derive similarities in terms of business model, technologies utilised, and target markets.
  3. Combine the algorithmic outputs with expert knowledge and domain expertise to refine and validate the findings.
  4. Continuously update the analysis as new companies and technologies emerge in the industry.

Understanding the Importance and Benefits of FinTech Company Categorization. A one-size classification of FinTech companies falls short in encompassing all possible products and solutions. The experience gained from this exercise enhances my role as an advisor, especially if I have access to data and research tools that offer new ways to access company information.

But why did I place so much expectations on the categorization of FinTech companies? There are several reasons:

  • It enriches the understanding of trends and opportunities within the FinTech industry .
  • It helps to outline the fundamental aspects of their business models, assisting with analysis of strategies, operations, commercialisation opportunities.
  • It derives a comparative assessment of FinTech companies using various criteria, such as service offerings, funding success, and long-term viability. This comparison can yield insights on the performance, scalability, and investment potentials of FinTech companies.

Finally, establishing FinTech clusters set the groundwork for future analyses, fostering the interest in exploring innovative business models.

alberto martellini

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