Here’s the summary: BBVA is going to listen more closely to the people who use its products, to better understand what they need and make their lives significantly easier. Stay tuned for this summary where we outline the future of the financial sector!
The global digital banking revolution has arrived
Improving customer relationships is no easy task. The financial industry has learned this the hard way: millions of euros lost in customer acquisition, price wars within the sector, and investments in developments that never saw a return. What has BBVA realized that others haven’t? That conversation is now fundamental.
It’s the heart of the financial experience. Offering innovative services is no longer enough–people expect their bank to listen, understand, and respond in real time and in ways that add value to their daily lives. Otherwise, they’ll simply look elsewhere.
BBVA understands this and has decided to place the customer at the center of its business strategy. This is not a marketing trick. As Onur Genç put it: “We are going to help people make better financial decisions.” This commitment is reflected in three core priorities:
- Products that improve financial health.
- More personalized and contextual interactions.
- Execution guided by excellence.
To put it plainly: BBVA aims to create positive experiences. That’s the top priority. Because if a user doesn’t enjoy the best possible experience through their online banking platform, they’ll likely switch to another.
Why BBVA is revamping its app
BBVA isn’t the only financial institution undergoing transformation. Innovations from major players in the sector are responding to real user demands: people want help managing their personal finances, along with speed and professional support.
As noted in Accenture’s Banking Consumer Study 2025, “Today’s customers expect financial institutions to do more than provide services–they want efforts to improve their financial well-being.”
The study also notes that this matters across all age groups but is especially important for younger generations, stating: “88% of Gen Z and millennial respondents said they are eager to expand their financial knowledge.”
Banking giants want to make the most of AI
Here’s how the industry is progressing in the race for innovation. We’ve selected six global cases that illustrate advances in customer service, fraud prevention, data analytics, and personalized banking:
JPMorgan Chase: a conversation worth millions
The New York-based bank is betting big on generative AI. Its virtual assistant can handle the vast majority of customer inquiries autonomously–understanding not just what users say, but what they really mean.
DBS digibank: the financial future lives in Singapore
Southeast Asia’s largest bank is also leading the charge. Its virtual assistant uses generative AI to simplify financial management. DBS has been recognized for its adoption of this technology, boasting over 800 models and 350 use cases.
Capital One Eno: the silent guardian of your finances
Eno, Capital One’s virtual assistant, takes “prevention” to a new level. It doesn’t just alert users to suspicious activity–it detects it before anyone else. Available via chat, email, or SMS, Eno monitors transactions, offers savings tips, and resolves issues.
NOMI: the assistant that cares for your wallet without being asked
Royal Bank of Canada’s virtual agent is a prime example of how AI can improve users’ financial health. It analyzes spending habits in real time, flags anomalies, and suggests personalized savings opportunities.
Spending Intelligence: Starling Bank’s AI chatbot
The UK-based bank launched a tool that answers spending-related questions as if it were a human agent. Just ask: “How much did I spend on coffee this week?” The magic is powered by Google’s Gemini AI.
N!Assistant: the new financial co-pilot
Nest Bank’s assistant, powered by GPT-4, can analyze accounts, pay bills, schedule transfers, book meetings with advisors, and more. The bank claims N!Assistant keeps context throughout every interaction.
All of these assistants share one core idea: Conversational AI is no longer a bonus–it’s the new standard in banking experiences. It’s not just about automation–it’s about listening, anticipating, and supporting customers with intelligence, empathy, and efficiency.
The challenge is deploying AI without hallucinations–efficiently
At Coinscrap Finance, we believe the success of these generative AI use cases in banking depends on meticulous groundwork–especially during the ETL (Extract, Transform, Load) data process. The dataset that feeds the algorithm must be curated and contextually rich.
Using pre-calculated indicators and structured data is essential for generating useful, accurate answers–free of hallucinations–while also keeping computational costs under control.
Banks are also realizing that simply integrating AI into internal processes isn’t enough. It must reach the end user. Customers expect technology to be embedded in tools that listen, understand their financial situation, and respond in real time.
To structure this information and make Big Data actionable, banks must rely on fintech partners who specialize in transactional data analysis. Engines trained on millions of real transactions are the game-changers: they understand financial language, translate complex data into insights, and create commercial opportunities in nearly every interaction.
A conversational AI engine specialized in finance?
What does it take to train algorithms to understand banking customers? At Coinscrap Finance, we’ve spent years applying AI to income and expense classification, transaction categorization, merchant identification, and carbon footprint calculation—just to name a few of the features of COCO, our intelligent engine.
That’s why interpreting each user’s financial context and enriching it with key details (like recurring payment frequency) comes naturally to us. Thanks to our data-driven approach, banks can now talk to customers like never before.
Our clients already know: adapting services, anticipating user needs, and creating unique banking experiences is not the future–it’s the present. What’s their secret? A curated, enriched data structure that enables AI to eliminate hallucinations and access the most relevant information in milliseconds.
An accessible, understandable, and genuinely useful financial history will set the trend in the months ahead–we’re sure of it. Now is the time to put the customer at the heart of your business strategy, enhance every interaction with your brand, and drive better results.
Frequently Asked Questions (FAQs)
Why is BBVA’s AI strategy relevant to the entire digital banking sector?
BBVA is taking a bold step by deeply integrating the user’s perspective into its strategy–using AI to personalize experiences, anticipate needs, and improve financial health. This customer-first approach is setting a benchmark and inspiring other banks aiming to differentiate through innovation.
What are the main benefits of conversational AI in banking?
Conversational AI allows banks to listen and respond to customers in real time, enhance personalization, boost operational efficiency, and lower costs. It also supports fraud prevention, data analysis, and process automation–delivering more secure, seamless user experiences.
What are the challenges of implementing generative AI in the financial industry?
The biggest challenge is avoiding hallucinations (fabricated or incorrect outputs) and minimizing computational load. This requires a robust ETL process, curated datasets, and models specifically trained in financial language.
How can innovation and customer experience teams benefit from AI?
• Identifying personalization opportunities and anticipating user needs.
• Automating support and inquiry resolution.
• Analyzing transactional data to uncover patterns and generate actionable recommendations.
• Improving security and fraud detection through behavioral analysis.
What role do AI engines specialized in transactional data play?
Tools like COCO, the proprietary AI engine from Coinscrap Finance, help banks build the perfect data foundation for conversational AI to deliver optimal results.
Its training and design make it ideally suited for the financial sector. Clients such as Santander, ABANCA, Unicaja, B100, and Bankinter use it to categorize income and expenses, identify merchants, calculate carbon footprints, enrich transaction data, and extract valuable insights.
