Coding with compassion: How Security Bank’s CDTO is rewriting the digital transformation playbook

In an era dominated by hyper-automation, Generative Artificial Intelligence (AI), and algorithms that predict our next move, the banking industry has spent years chasing a cold, clinical standard of efficiency. But during my recent chance to sit down and have a conversation with Patrick Meneses, senior vice president and chief data and technology officer (CDTO) of Security Bank Corporation (SBC), it became clear that the true differentiator in digital transformation isn't the strength of your code -- it’s the depth of your empathy. Since taking the tech helm at Security Bank, Meneses, a self-described "engineer at heart" and proud "nerd," has championed a philosophy that bridges the gap between complex data infrastructure and human-centric service. For Meneses, technology is not a replacement for humanity; it is its ultimate amplifier. The Power of the "Unobvious" Superpower: Empathy in Data When I asked Meneses what the number one skill the next generation of data leaders needs to possess, he didn't point to Python, Machine Learning (ML), or cloud architecture. "Empathy is the most important skill for everybody on this planet," Meneses asserts. "As a technologist who loves technology, I use that as a significant superpower for everything that informs my approach, my decisions, and how I view the lens of the world." This belief has heavily guided Security Bank's cultural and technological shift. Instead of building customer profiles purely through a product lens -- asking “How do we sell this?” – Meneses’ team uses data to ask, “Who can we help, and how can we help them?” By putting himself in the customer's shoes, Meneses often reverse-engineers his own experiences with banking and fintech applications to identify subtle friction points. This practice of "metacognition" -- thinking about how we think, feel, and experience -- is what drives the bank's internal framework, appropriately named CARE. Moving from Real-Time to Proactive Banking While "real-time data" remains a massive buzzword in financial technology, Meneses views it as a baseline rather than the end goal during our chat. He argues that traditional banking models operate on a reactive framework, where data is only used to respond after a friction point or problem has already occurred for the customer. Security Bank is shifting this paradigm toward a proactive model. By leveraging predictive and prescriptive analytics alongside artificial intelligence, they anticipate challenges and see around corners before the customer ever encounters an issue, ensuring a seamless experience. By mapping out entire "customer journeys" -- whether a client is walking into a physical branch to update a phone number or applying for a credit card -- the bank gathers critical data. This data isn't used to automate humans out of the equation, but to arm frontliners with actionable insights to better serve clients. Solving Real-World Friction: The Smart Locker Solution To understand how Meneses' philosophy translates into the real world, he shared a practical example involving the bank's credit and debit card delivery system. Manila's complex logistics environment often creates massive friction for everyday customers. In a traditional setup, clients are often forced to adjust their personal schedules to wait for a delivery driver, and incomplete address data in navigation apps often results in failed deliveries and missed handoffs. Instead of treating this strictly as a logistics routing problem, Meneses and his team re-framed the issue through the lens of customer convenience. They introduced secured lockers at select branches, allowing customers to safely pick up their cards after hours on their own terms. Meneses pointed out that if you don't frame the problem correctly and see all the facts from a macro view, you miss the human element. The solution wasn't about perfectly matching the driver and the customer, but about giving the customer more options to do what they need to do in their own time. Guardrails, Bias, and the "Lego" Approach to Data With great data comes great responsibility. Security Bank has implemented rigid Data Loss Prevention (DLP) controls and strict opt-in/opt-out mechanisms, ensuring that customer data remains firmly in the hands of the customer. If a customer chooses to opt out of promotional tracking, they are entirely excluded from downstream segmentation models, protecting their privacy at every level. In addition, Meneses is hyper-aware of the dangers of AI bias. To keep models fair and transparent, Security Bank utilizes a "Lego block" philosophy, which means reducing the dimensionality of data to strip away unnecessary noise that could lead to algorithmic discrimination. For instance, Meneses notes that traditional banking models relied heavily on legacy metrics like age or address to determine creditworthiness. Security Bank’s modern data science models challenge those old-school assumptions. He shared with me that Data Science has proven that age has nothing to do with creditworthiness whatsoever, noting that young workers in the booming gig economy are incredibly financially viable and profitable, despite being routinely ignored by traditional banking metrics. The Goal: Reliability Over Perfection As Security Bank continues its digital evolution, Meneses remains grounded. He recognizes that external macro-factors in the Philippines -- such as telco availability, internet connectivity, and power grids -- mean a system will never be 100% flawless. Because of this, he prioritizes consistency and operational resilience over a vague chase for perfection. Our conversation closed on a deeply human note. Meneses emphasized that digital transformation is ultimately about the evolution of humanity, aided and enhanced by analytics and artificial intelligence. In his eyes, no matter how advanced the technology becomes, nothing will ever replace the human touch, because connection is at the very core of who we are.