Overcoming these barriers–and achieving effective adoption of AI, especially conversational AI–requires a shift in how we perceive and manage data. As someone who has spent more than 10 years focused on banking transactions, I hope some of my insights will help you integrate AI into your organization.
Óscar Barba
Co-founder & CTO of Coinscrap Finance
Banking process optimization: opportunities, challenges and real cases
Digital banking is being transformed by machine learning
Machine learning is a branch of artificial intelligence that enables machines to learn and improve automatically from data, without being explicitly programmed for each task.
Algorithm capabilities continue to grow, but in the financial sector, it’s essential to include expert supervision (or semi-supervision) throughout the training process. This is the only way to reduce errors to a minimum and ensure reliable outputs.
These systems don’t rely on rigid instructions–they analyze large volumes of information, detect patterns, and make predictions or decisions based on accumulated experience.
As more data is processed, detection accuracy improves. This technology has been revolutionizing digital banking for years and is now a fundamental pillar in the industry’s evolution.
In the case of COCO, our proprietary AI engine, the efficiency and performance of its algorithm allow Coinscrap Finance to process and classify banking data in under 10 milliseconds, optimizing real-time decision-making.
💡 Did you know that…
“Our algorithm can help you determine which customer is ready for a new financial product.”
The myth of perfect data: a trap that slows digital strategy
As a business’s data volume grows, it becomes essential to have systems capable of analyzing it in real time to generate tangible value. Interestingly, all organizations believe their data is worse than that of others. This mentality leads to chronic paralysis.
The key isn’t waiting for a perfect data ecosystem, but starting now–with existing tools–to achieve a quick win and build a solid foundation to progressively improve data quality and usability.
Organizations that adopt this mindset move faster and more effectively. A good recommendation is to rely on fintech experts with proven experience and the ability to execute reliable, fully secure integrations.
Transactional data is sensitive information and must be protected with multiple layers of encryption and obfuscation, alongside robust security protocols and regulatory compliance at every stage of the process.
A one-off solution isn’t enough: deep understanding of your institution’s infrastructure is essential. A provider who doesn’t align with this vision becomes a burden rather than a partner.
How to align technology, culture and business to create real impact
A powerful approach to AI implementation is to reverse-engineer the process: start by defining what perception you want customers to have of your brand in the coming years, then work backwards from there. This method connects strategy, culture, and technology in a coherent and effective way.
It’s not just about implementing AI tools for the sake of innovation, but about asking what kind of relationship you want to build with your users and what tangible value you want to provide.
With this mindset, it becomes easier to prioritize investments, choose the right partners, and build a unified organizational vision. The institutions making the most progress aren’t necessarily those with the largest budgets or cleanest datasets–but those willing to act with what they have, embracing continuous improvement like a fintech would.
Examples of innovative tools in digital banking
Traditional segmentation is long gone. Today’s products, services, and communications are tailored to the specific behaviors and needs of each user, in real time and fully automated.
As mentioned months ago, our paper “Applying Machine Learning to Detect Periodicity in Transactional Banking Data” was presented at the IEEE Symposiums on Applied Computational Intelligence and represents a significant improvement in this field.
Instead of offering generic recommendations, modern banking creates tailored financial experiences by analyzing spending habits, location, saving patterns–and even carbon footprint–to fully adapt the offering.
The case of B100 and a new banking category: The Healthy Banking
The numbers speak for themselves:
- 2 tons of plastic collected thanks to the Pay to Save card.
- €230 million in business projected for 2024 by a newly launched neobank.
- 500,000 customers expected by 2026.
A bank that rewards your movement and is committed to a sustainable future. That’s the mindset of a company unafraid to change the rules.
Pensumo: saving for retirement with every online purchase
What if saving for retirement were as easy as shopping online? Well–it already is. A value proposition designed for new generations, making long-term financial planning more attractive by offering automatic rewards through digital banking.
It’s a kind of cashback 2.0: customers contribute to their pension plan with every purchase made at participating stores. Benefits all around–for the user, the bank, and the merchants.
Unicaja success story: automated microsavings
Many people aim to automate their savings and improve financial well-being effortlessly. That’s why Unicaja, like others, partnered with us to create a smart tool integrated into their digital platform.
With it, users can:
- Schedule recurring or one-time contributions.
- Share savings goals with friends or family.
- Round up purchases made with their card.
- Save a percentage of their salary or pension.
- Even save when their favorite football team scores a goal.
All while adapting to their financial goals and needs.
What can we expect from the future of banking? Conversations that convert
In the near future, talking to your bank will be the core of the financial experience. Today’s customers expect their bank to listen, understand, and respond immediately–based on their personal context and life stage.
Soon, virtual financial assistants will deliver exceptional service–fully understanding both financial terminology and the user’s natural language to provide the best, most relevant response possible.
How can banks achieve this? Conversational AI is reaching unprecedented levels of capability, becoming a game-changing tool for business, customer experience (CX), and compliance alike.
Coinscrap Finance is set to play a leading role in its large-scale adoption–so follow me on LinkedIn to stay tuned for exciting updates coming soon.
Thanks for reading!
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.
Doctor in Information Technologies from the University of Vigo, he holds a Master’s degree in Computer Engineering, a Master’s in E-Commerce from the University of Salamanca, a Scrum Manager Certificate, and Project Management certification from CNTG, as well as a Certificate in SOA Architecture and Web Services from the University of Salamanca. He has recently obtained the ITIL Fundamentals certification, which recognizes best practices in IT service management.
