Big Data, Finance and Transparency

Thanks to data science and machine learning methodologies, it’s possible today to provide accurate and transparent credit risk assessment of any company or financial institution worldwide. From distributed computing to big-data processing and artificial intelligence, advancements in hardware and software are being applied across all fields of knowledge and economy, finance included. We discussed the subject with Velentino Pediroda, modefinance CEO & Founder. Modefinance is a FinTech native company who develops Artificial Intelligence solutions for the assessment and management of credit risk, as the first official FinTech Rating Agency in Europe registered as a CRA and ECAI.

Today the Artificial Intelligence has found in Finance one of the most prolific application fields, could you explain what it means for entrepreneurs and investors?

Applying AI to Finance means a lot of things. In credit risk management, modefinance’s main field, it means helping companies and entrepreneurs with more accurate risk evaluations, which lead to a reduced risk for the investors. Today, thanks to the AI, it is possible to perform more accurate assessments, using all the available information (BigData) and more sophisticated numerical technologies. This improvement causes an increase of automation, and investors are now able to deepen their investments’ financial risk, taking more careful decisions and conscious actions.

Which type of data set helps to improve predictive capabilities related to financial issues?

Today we live in a data world: all information are available, anything may become a source, and these data may help us to identify and predict risky situations. Even more important, we are able to do this in real-time. Social founts, web navigation, deliveries, every single information is now included in risk evaluation models.  So the real challenge is no more collecting enough data, but being able to detect and choose the most important information for the problem we want to solve, checking case by case. To solve this, Deep Learning Artificial Intelligence is fundamental.

You argue that the Fintech algorithms direct our money towards sustainability, what does it mean?

Transparency is the key, and this is what we aim to reach. For us, transparency means being able to give everybody the right instruments to be well-informed and aware of risks. To reach sustainability, which is the right of anyone to take responsive decisions towards their goals.

What can we expect in the near future from applications of artificial intelligence and Big Data methodologies in the financial sphere?

I am pretty sure that the entire financial world will adopt new technologies: we’re almost there, even if it was a hard one. Finance is historically one of the first industries to be touched by this revolution, being at the same time hardly conservative. We can’t be sure about it, but right now almost all the decisions are taken (or supported) by algorithms: loan approvals, credit card acceptances, cash emissions, everything. I think we won’t see so much for a revolution in the future of this sector, because the r-evolution is already here!

Will you be attending ESOF 2020 in Trieste next July? If so, what are your goals and expectations?

As a truly fan of ESOF, I won’t miss. ESOF 2020 is a once-in-a-lifetime opportunity, both as a guest and as a host. I think that for a city as Trieste, which is immersed in a scientific environment, thanks to all the great academic and research institutions, is a very important moment, but also for the entire science and tech industry worldwide.

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