Hyperpersonalized Finance Intelligence: Opening the doors to financial management

What could be more important than the stability and economic well-being of users? For those of us at Coinscrap Finance, nothing at all. Our project "Hyper Personalized Finance Intelligence" has just received the approval from CDTI Innovation.

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Due to their financial contribution, we will continue to enhance our Personal Finance Management (PFM) system by leveraging cutting-edge data analysis technologies.

Óscar Barba

Co-founder & CTO of Coinscrap Finance

A glimpse into the “Hyperpersonalized Finance Intelligence” project

Personalized financial management has become a necessity for users in recent years. The way we manage our money has become -almost- 100% digital, making it essential for banks, neobanks, and fintech companies to optimize the tools available on their digital platforms.

At Coinscrap Finance, we are committed to taking economic management to the next level, offering more comprehensive experiences tailored to the needs of the end customer.

Key developments on which we will focus our efforts

In the Hyperpersonalized Finance Intelligence project, we focus on real-time user segmentation, the ability to make financial predictions, and the delivery of hyperpersonalized recommendations, among other key developments.

These functionalities will allow entities to better understand the individual needs of users, providing them with recommendations and suggesting actions tailored to their specific financial situation.

Collaboration and participation: an outstanding team

The project has a team of top-level experts: first, we work with the research AtlanTiC group of the University of Vigo, and secondly, we have the participation of 9 multidisciplinary professionals within our ranks.

This collaboration ensures a comprehensive and expert vision in the development and implementation of new functionalities. Our strategic partnership ensures excellence in the development of new technologies. In this way, we hope to further strengthen our position as a technological leader in the fintech industry.

With the successful implementation of this project, we will also become references in the field of personalized financial management, improving decision-making and the economic situation of banking users.

The focus on hyper-personalization, financial prediction, and the generation of user clusters will provide a unique and valuable experience to customers, also driving the growth and expansion of entities.

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Innovation in action: 5 main areas of banking research

Bank customer segmentation

This area allows grouping users into different categories based on their financial behavior. Segmentation facilitates communication, sending personalized recommendations, and understanding the needs of each group of customers. The deep knowledge gained from data enrichment will be vital to provide the best service on the market.

Identification of financial trends

Another important aspect is the identification of trends in users’ economic habits. By enriching banking transactions, companies can anticipate customer needs, improving their products and services ad hoc. Discovering patterns and identifying sales opportunities are two of the main advantages of this section.

Fraud prevention and increased security

The project will also focus on this point, using transactional data analysis to detect suspicious or fraudulent activities and take preventive measures. This provides an additional level of protection for users and their financial assets. Entities that prioritize security maintain higher loyalty rates and are more likely to be recommended to others.

Improvement in financial product recommendations

As we have already mentioned, a better understanding of user behavior translates into more accurate sales techniques with less resource investment. It is possible to offer precise and relevant products and services that enhance the customer experience and increase engagement.

Predictive analysis with machine learning techniques

Finally, the project will use machine learning and artificial intelligence to predict users’ future behavior, allowing companies to anticipate their needs and offer solutions at the right time. This eliminates the friction generated by the mass messages sent by financial institutions.

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Objectives and methodology: “Hyperpersonalized Finance Intelligence” project details

The main objective of the project is to develop and implement new functionalities in the PFM system of Coinscrap Finance, focusing on previous research, scientific-technical analysis, and the development and integration of specialized modules.

The methodology will combine supervised and unsupervised machine learning techniques to address the different objectives of the project.

The application and development of technologies such as clustering techniques, statistical analysis, product recommenders, time series study, streaming of machine learning models, and explainability techniques will also be necessary.

All this will allow data processing, pattern detection, generation of personalized recommendations, and interpretation of the results obtained.

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Advantages of research for the financial sector

This is an initiative that offers significant benefits for both customers and financial institutions. Users will gain greater control over their economic situation, with a complete view of all their banking transactions, detailed reports, personalized experiences, not to mention the transparency and security of their data.

On the other hand, entities will be able to improve their services, prevent fraud, and make strategic decisions based on the analysis of their customers’ behavior patterns. The main value proposition of the project is to empower entities to provide a hyperpersonalized experience to users.

Thanks to Coinscrap Finance, banks, neobanks, and fintech companies will be able to generate greater interaction and engagement with each and every one of them. Because this time it’s personal.

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 and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca. He recently obtained the ITIL Fundamentals certification, a recognition of good practices in IT service management.

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