Improving this aspect is key to enriching the customer experience with offers that bring real value to their lives. The information is 100% available, but financial institutions need to know how to leverage it.
Consumers expect every interaction to reflect a deep and contextual understanding of their financial goals, including savings, mortgages, loans, and transaction history. This is where banks can become indispensable in personal finance management. Technology can enable them to extract strategic insights into their customers’ financial lives.
Óscar Barba
Co-founder & CTO of Coinscrap Finance
The protection of transactional data and its ethical use: banks focus on benefiting the final user
Data protection is a subject I’ve covered in this post, and I want to emphasize its importance above everything else. In a highly regulated environment like financial institutions, it is guaranteed. That said, its intelligent and, above all, ethical use is a rapidly growing trend due to its power to strengthen relationships and improve the financial health of the population.
Through an innovative and responsible approach, transparent and close communication between banks and their communities is promoted. How is this achieved? By using tools that provide accurate, reliable, and useful answers in milliseconds to help people manage their money in their daily lives. We are talking about transactional data enrichment engines.
💡 Did you know…
how chatbots understand what you say to them?
The technology described in this post emerges as a solution that allow banks and other financial entities to harness the power of data.
The capabilities of transactional data enrichment engines
After analyzing, categorizing, and enriching information, these engines take a step further by integrating Generative AI (Gen AI). This makes it possible to engage in real conversations with users, answer their questions, and become true financial coaches—all automatically. Let’s explore which areas can benefit most from these innovations.
Improved interaction with online banking users
Gen AI enables systems to understand and process natural language much more efficiently. This means users can interact with enrichment engines using their everyday expressions, without needing to adapt to specific commands.
By fully understanding natural language, the system, in turn, offers friendly and completely understandable responses. For example, if a user asks, “What mortgage can I afford right now?” the engine analyzes the question in its full context. It uses Natural Language Processing (NLP) algorithms and specific language models to interpret the intention behind these words.
The algorithms break down the sentence, identify key entities (such as “mortgage” and “afford”), and understand the user’s request. Then, it turns to the huge amount of information available in the user’s transactional history to assess their usual expenses, income, spending patterns, credit history, etc., and provide an answer perfectly tailored to their specific circumstances.
Big Data: Analyzing and synthesizing vast amounts of information
As we’ve seen, this technology helps entities and users manage the vast amounts of transactional and financial data generated over the years. The engines collect information from multiple sources, such as bank transactions, payments, receipts, salaries, collections, market trends, or even social media.
Only then do they synthesize this information to offer useful insights for a specific user. To achieve this, they use machine learning and deep learning algorithms, such as neural networks and decision trees. This enables them to identify patterns and relationships among the data. These algorithms can predict future financial behavior and offer personalized recommendations.
In the case of the mortgage question mentioned earlier, the system would evaluate the user’s financial situation and suggest products suited to their payment capacity. With these ultra-customized recommendations, the customer feels that their unique case is being addressed and that the bank’s ultimate goal is to help them improve their financial health.
What algorithms do global financial institutions use?
Natural Language Processing (NLP)
With techniques like syntactic and semantic analysis, banks can fully understand human language. Models like Transformers (present in BERT or GPT) are essential for interpreting and generating natural language.
Deep Neural Networks (DNNs)
These allow institutions to identify complex and non-linear patterns. Used in areas like fraud detection, risk management, and behavior analysis, DNNs continuously improve their predictions and automate decisions
Supervised and Unsupervised Learning
Used for data classification, such as credit application evaluation, where historical data is labeled to predict future behavior. Additionally, it clusters unlabeled data, helping to discover customer segments, detect transaction anomalies, and personalize offers based on hidden patterns.
Recommendation Algorithms
These are essential for personalizing the offer of financial products. They use collaborative filtering techniques, based on the preferences of similar customers, and content-based systems, which analyze the user’s individual characteristics.
All these tools allow recommending anything from credit cards to investments, right when the person needs them, aligning the available options with the user’s specific needs and habits. Furthermore, they enable banks to anticipate trends or future requirements and create personalized, innovative services that may not yet be available in the market.
How can banks take advantage of new technologies?
The new generation of customers not only prioritizes efficiency and convenience in the digital world, but they also seek value and rewards for their loyalty. There is a clear connection between a bank’s technological capabilities and its financial performance, indicating that success depends more on the strategic use of technology than on higher investment in it.
An analysis by Bain & Company revealed that tech-leading banks achieve a total shareholder return 5 percentage points higher than the average, a cost-to-income ratio 10 points lower, and an NPS score 12 points higher than their competitors. In our day-to-day operations, we see how financial institutions leverage the technical advances in data science to grow and expand their customer base.
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.