How artificial intelligence is transforming the financial industry

I am currently preparing my PhD at Vigo University, focused on capturing information, its semantic analysis and application to the financial sector. This is how the categorization engine we use at Coinscrap Finance to help people improve their financial health was born: COCO (which is also my nickname since I was a kid). It is a tool that uses Artificial Intelligence, Machine learning and Natural Language Processing.

Today we are going to talk about that first leg, artificial intelligence.

Let’s start with some context:

Óscar Barba

Co-founder & CTO of Coinscrap Finance

Before Chat GPT. What is and when did artificial intelligence emerge?

The RAE defines AI as: “the scientific discipline that deals with creating computer programs that execute operations comparable to those performed by the human mind, such as learning or logical reasoning” (by the way, it was named expression of the year by the FundéuRAE in 2022). This term was incorporated into the Dictionary of the Spanish language in 1992.

The Harvard University blog makes an interesting summary of the history of AI, from its beginnings in the 50s.

Let’s see the graph with its evolution up to the 21st century:

For many of us, that 1997 chess game in which Deep Blue defeated the champion Gary Kasparov was an historic breakthrough. With a mixture of disbelief and (why not to say it?) fear, we realized that AI had come into our lives (although the first thing that came to mind were some scenes from science fiction). But the reality is that its massive application did not arrive until years later, with the explosion of “Big Data”, a global market valued in 2013 at 10,000 million dollars.

The application of artificial intelligence to the industry

Arthur Samuel, an engineer at IBM, developed the first checkers game program skillful enough to challenge any amateur player. It caused the company’s shares to skyrocket 15 points the next day. It was back in 1955. Something more recent is the case of Google, which in 2012 developed a deep learning AI called Google Brain, capable of significantly improving the ability of computers to recognize images and voice.

Of course, governments have always been very interested in the benefits of facial recognition and data analysis. Many companies have made developments in this area. In recent years, we have enjoyed a simpler life due to AI: the virtual assistant that turns on the light when we ask it to, the Netflix content recommender, the fridge that makes online purchases or –the increasingly used– GPT Chat, to mention some applications.

We could go on for hours, but let’s focus on what interests us: the world of banking and insurance.

Why is AI so important for the financial sector?

As we said, the current rise of Artificial Intelligence is related to Big Data, since it allows managing huge volumes of information and processing it in real time. The greater the volume of information, the better training is generated and therefore the more accurate the answers. This is something that banks and insurers are very interested in, because their customers generate billions of transactions daily.

The analysis and processing of this data is carried out due to the development of algorithms, which are capable of learning and making decisions autonomously. It is a discipline that aims to achieve a level of reasoning that is similar to that of the human brain. The fact that entities can come to understand the financial situation of their customers in depth means being able to offer highly personalized advice.

Once the AI does its job, valuable insights are obtained, such as insurance expiration dates, recurring expenses, subscriptions, risk behaviors, habitual income and a long etcetera.

Let’s give an example:

“If the user receives an extra income, the entity may recommend that they save a part to increase their digital piggy bank and get closer to their goals (a trip, a new computer…).”

The power of an Insights tool can take your business off the ground

In this way, messages are hyper-personalized and communications are limited to messages that are really of interest to customers. The secret of insights is that they serve as decision triggers for people, who can finally take control of their domestic economy, thanks to data. In addition, they make it possible to focus the business strategy of banks and insurers and multiply the engagement of users, who see their experience on their digital platforms improve.

Our AI engine is nourished by the teachings of a Nobel Prize in Economics 

I would also like to mention that many of the COCO use cases take into account the principles of Richard H. Thaler. He was awarded the 2017 Nobel Memorial Prize in Economic Sciences for his contributions to behavioral economics. His theory explains how people need a little “nudge” to be able to make the best economic decisions.

“If you want to encourage someone to do something, make it easy.”

Richard Thaler

His studies shed light on the behavior, limitations and difficulties that people encounter in relation to the domestic economy. Inspired by their work, our solutions show users understandable data so that, with all the information about their habits, they are able to set goals, anticipate events and improve their financial health.

70% of households have problems saving

We find ourselves in a turbulent economic environment and, precisely for this reason, our mission is to improve people’s lives. As reflected in the latest “OCU Family Solvency Index”, 70% of households have problems saving and 11% to make ends meet. Faced with this situation, many people choose their financial institution based on the digital tools available for managing their finances. We must not forget that the penetration of mobile banking stands at more than 90% for the age group between 16 and 74 years.

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, Master in Electronic Commerce from the University of Salamanca, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca and more.