In an era of rapid and sudden change, and in the evolution from Moore and Kurzweil, where technology is an exponential, open-ended process, Fintech couldn’t but show its continuous growth and enhancement features. And that is when the focus shifts from the technological tools’ development, use and adoption, to their integration with different sources: from data quantity to data quality.
The technological paradigm is no longer under discussion, it’s a necessary obviousness. Proprietary and alternative open data are now center stage; not to mention the world of alternative data and their potential, as well as one of the key topics of the near future, namely ESG.
How to use them? Why should they be shared? Who protects us from unjustified use? Along with their availability, these are the questions that the entire Fintech ecosystem – and the user – ask in the discussion on the future of financial services and on ethical, environmental and economic sustainability. Quoting Laura Grassi, director od the Osservatorio Fintech e Insurtech of the Politecnico di Milano university, “data can break new ground for monitoring and understanding enterprises’ and persons’ state of health, thus leading to loans, insurance, or better and more inclusive financing and capital allocation decisions; but they can also enable better financing mechanisms and new business models, as in the case of banking-as-a-service or embedded finance."
And if the issue “so much data, so little time” was to impose the choice between monotony and automation, we all know the ease of choosing for the institution and the company. It is when the question becomes more subtle, and we switch from analysis collection “only”, that we delimit the distance between standard capacity and advanced modeling.
From FinTech to TechFin, evolution of the paradigm
Manual processes often turn out to be not only time-consuming, but they also make it difficult to guarantee the absence of factual mistakes and a consistent monitoring, involving different departments on multiple platforms (ERP, CRM, DBMS) or formats (paper, Excel, cvs, zip and ASCII). In cases where the choice of working together with FinTechs or agencies that work on the development of technology products, or where third-party database integration has already been made, improvements were not long in coming: cutting time and resources dedicated to low value-added activities, better relationship with customers, and increased revenues.
Now, the question shifts to the much talked-about ChatGPT: without going into the merits of the tool, the real issue emerged from the hype related to the artificial intelligence that it generated, is the data gap that we face at a time when everything is at least 6 time faster around us. If we consider that Fortune 500 enterprises say they are data driven (only for the 24%), that they have grafted a data culture (21%), this implies that 79% claims to have no data culture at all.
What may seem trivial, namely data simplification and standardization, actually constitutes a further evolutionary step: in some fields (for example, consider investments), a combination of geographical, sentiment analysis and market trends data, is already fully functional to the analysts’ work, which is crucial to the institutions’ and enterprises’ growth worldwide.
This trend, seemingly only lexical, in the reality shifts the vision from financial models to the selection of the best data “to be offered to” already advanced engineering technologies and to meet specific evaluation needs. Therefore, thanks to the flexibility and modularity of technologies and tools previously developed, it is possible to connect various environments with different databases, which can interact and dialogue, although with different languages. Better data mean resources that can devote less time to the resolution of factual problems, to the data quality correction, and operating on the better planning for the revenue growth and customer experience.
Public data, financial data providers, illth and alerts, corporate structures and trends, invoice flows, open banking (PSD2 and PSD3), ERP provisional budget sheets, environmental data and supply chain information: this is useful to improve the study and examination of practices, and it can be automatically integrated in advanced tools that both banks and enterprises are adopting.
The key: independent micro-services
In the same direction as the development of operating systems, mainly mobile banking and digital payments, goes Embedded Finance: in fact, there is an increasing interest in microservices, modular and independent, and easily integrated with each other.
Namely, according to Gartner’s definition “a microservice is a service-oriented application component that is tightly scoped, strongly encapsulated, loosely coupled, independently deployable and independently scalable.” Tools that promise to solve specific needs both of more structured financial institutions, already in possession of proprietary management and procedural platforms, and of challenger entities, unable to support technological and structural developments combined with significant costs.
Let’s try to give a practical example: in the case of a digitalized rating process, based on advanced models (Rating-as-a-Service), and here is where the microservices’ outputs are represented by scorecard custom KPI-ed, that synthetize previous assessments, and are capable of meeting regulatory challenges and TechFin visions.
Education and regulation as growth drivers
Within banks and enterprises, investments don’t stop anymore to technologies and Data Analytics: we focus on learning of one’s resources regarding technology and data literacy. Knowing how to read and interpret data, as well as proactively derive value, is essential for the entire chain of analysts.
In fact, with the exponential increase of available data, sources to be integrated and formats in which data occur increase, as well. It is precisely in this context that summaries of data within advanced models become increasingly important, in a process from FinTech to TechFin able to truly revolutionize the approach to business growth.
Not only banks and corporates: financial institutions are doing their part, progress in digital transformation and in its regulation are in place, but maturity varies. Dimensions, commitment and investments play a fundamental role, and the discussion is livelier than ever, from common strategy on Digital Finance to the very recent topic of PSD3. The authorities themselves are fully engaged in projects that foster innovation (ex. sandbox), and perhaps the regulation can become a stimulus for innovation itself.
Thus, the mix of skills and expertise that existed already in FinTech grows stronger:
- Variety and quality of data – geolocation, sentiment, market, ESG, in a unique combination of accuracy, verifiability, perception and context – in real time and with global coverage
- Technological scalability – thanks to the previous API knowledge, AI technologies, cutting-edge Machine Learning, modularity and flexibility of platforms – open to interaction with different data sources
- Monitoring of analyses, results and evaluations carried out, as well as of the most particular positions and complex dimensions, and providing information on future services
- Multidisciplinarty, built-up experience and knowledge, backgrounds that range from finance to IT development.
Results? Richer and more accurate analysis experiences, an increased rapidity of feedback to the end user, more precise and structured access to credit on the enterprise’s actual possibilities; what matters the most is the substantially improved decision-making, with which the institutions can favor informed business choices.
If the horizon is a world of open banking and embedded finance, data are the guiding light of every business, and therefore we have to learn to know them, enhance them and take care of them with the help of FinTech.