Shrutika Poyrekar is the Vice President and Head of Analytics for Fraud at the largest bank in UAE, driving AI-led transformation in real-time fraud detection and risk management. With deep expertise in MLOps, large-scale AI deployment, and data-driven strategy, she has built scalable, production-grade AI systems. A passionate leader, they focus on mentoring teams, AI governance, and bridging the gap between AI innovation and business impact. Her long-term vision is leading AI-driven strategy and enterprise transformation at scale.
Recently, in an exclusive interview with Digital First Magazine, Shrutika shared insights on what drives her passion for AI and data science, personal source of inspiration, significant career milestones, future plans, words of wisdom, and much more. The following excerpts are taken from the interview.
Hi Shrutika. What drives your passion for AI and data science, and how do you stay current with emerging trends?
My passion for AI and data science is driven by the challenge of solving high-stakes, real-world problems—especially in fraud prevention, where adversaries constantly evolve. I don’t just apply AI; I architect scalable, intelligent systems that detect and mitigate fraud proactively.
I stay ahead by deeply engaging with AI advancements—fine-tuning LLMs, optimizing RAG pipelines, and integrating ML-driven decision systems into banking operations. I actively follow AI research, experiment with new methodologies, and ensure my team is leveraging the latest innovations. For me, leadership in AI isn’t just about staying informed; it’s about driving adoption, aligning AI with business strategy, and pushing the boundaries of what’s possible.
What do you love the most about your current role?
What I love most about my role is building and deploying AI-driven fraud detection at scale on live transactional data. Fraud is one of the most complex problems in finance because it requires solving multiple ML challenges—real-time risk scoring, anomaly detection, indirect liability estimation, and high-dimensional behavioral modeling. Essentially, fraud detection is a microcosm of financial AI—person classification, credit risk assessment, and network analysis all come into play.
It’s not just about AI; it’s about making it work in production—deploying models with MLOps for real-time monitoring, handling high-velocity data streams, and ensuring fraud decisions are explainable, precise, and fast. The challenge of optimizing for both accuracy and latency in a live banking environment, while continuously evolving defenses against adaptive fraudsters, is what makes this role exciting.
What do you believe are the most significant challenges facing AI and data science professionals today, and how can they address them?
The biggest challenges in AI and data science today revolve around execution, trust, and expectations. Many AI models never make it to production due to real-world constraints like latency, cost, and integration challenges, with MLOps often overlooked. To drive real impact, AI professionals must prioritize deployment, monitoring, and continuous improvement from the start. Another major hurdle is the gap between AI expertise and applied execution—many practitioners lack real-world implementation skills, while domain experts resist AI-driven insights, assuming outsiders can’t model their industry effectively. Bridging this divide requires hands-on AI leadership and strong cross-functional collaboration. Additionally, hype vs. reality remains a key challenge. AI isn’t magic, and garbage in, garbage out still holds true—poor data quality, biased inputs, and unrealistic expectations can easily derail AI projects. Overcoming these issues demands clear communication, realistic goal-setting, and a focus on incremental, measurable impact to ensure AI delivers sustainable value.
How do you see the field of AI and data science evolving in the next 5-10 years, and what skills and qualities do you believe will be essential for success in this field?
Over the next 5-10 years, AI and data science will evolve from standalone models to fully integrated, intelligent systems that drive real-time decision-making. The focus will shift from just building models to deploying and operationalizing AI at scale, making MLOps, automation, and real-time data pipelines essential. AI will enhance, rather than replace, human expertise, especially in fields like fraud detection and risk management, where explainability and governance will be critical.
Success in this evolving landscape will require a hybrid skill set—deep technical expertise in AI deployment, a strong understanding of domain-specific challenges, and strategic thinking to align AI with business goals. The ability to bridge the gap between AI and business, ensuring ethical, interpretable, and scalable AI adoption, will set leaders apart. As AI adoption grows, professionals who can navigate both technology and business execution will drive the most impact.
What do you think are the most significant opportunities and challenges facing women in technology and leadership positions today?
Women in technology and leadership today have more opportunities than ever, with increasing recognition of the value diverse leadership brings. AI and data science, in particular, offer a merit-driven space where expertise and execution matter more than hierarchy, allowing women to lead transformative change.
However, challenges persist. Breaking into leadership often requires proving competence repeatedly, as biases around technical and strategic decision-making still exist. Many women also balance high-pressure roles with personal responsibilities, making career progression more complex. The key to overcoming these barriers is sponsorship, not just mentorship—having advocates who actively push for women’s leadership in critical decision-making spaces. Building strong networks, driving measurable impact, and fostering inclusive, high-performance cultures are essential for long-term success.
Who has been a significant influence or mentor in your career, and how have they helped shape your professional journey?
My husband has been my biggest mentor, not just in my career but in shaping how I approach problem-solving and leadership. As a founder and a product leader, he has a deep understanding of how technology intersects with business, and being around him has given me invaluable insights into the real-world application of AI.
Early in my career, I was deeply focused on complex AI models, often assuming that the most sophisticated algorithm would always be the best solution. However, through our discussions, I realized that not every problem requires an advanced AI approach—sometimes, the right answer lies in a simple, scalable, and more operationally efficient solution. He taught me to think beyond the model itself—to question whether an AI solution aligns with business objectives, integrates well with existing systems, and ultimately delivers measurable impact.
His experience in building and scaling products has also influenced how I lead teams and drive AI adoption. I’ve learned the importance of balancing technical depth with strategic execution, ensuring that AI isn’t just an academic exercise but a real, functioning system that creates value. More than just offering advice, he has been a sounding board, pushing me to think critically, challenge assumptions, and refine my decision-making. His influence has helped me transition from being just a data science expert to a leader who understands the bigger picture—how to drive AI at scale while keeping business goals at the core.
What has been your most career-defining moment that you are proud of?
I’ve had several career-defining moments that shaped my journey. From building the fraud score for one of the largest bureaus, to working on robotic microscopes just because finance felt too conventional at the time, to deploying deep learning solutions for real-time fraud detection at scale in a major bank—each experience reinforced my passion for solving complex problems.
But what I’m most proud of isn’t just the technical achievements—it’s building and mentoring my team. Watching each team member grow, take ownership, and lead their own projects has been the most fulfilling part of my career. Creating an environment where AI talent thrives and scales solutions beyond just models is what I consider my greatest accomplishment.
How do you prioritize your well-being and self-care amidst a demanding career?
Balancing a demanding career with well-being comes down to discipline and intentionality. I take self-care seriously and ensure it’s a consistent part of my routine. My mornings start with beauty detox drinks and meditation to stay grounded and focused. While I’m not always in a strict workout routine, I make an effort to exercise daily to maintain both mental and physical well-being.
Planning is key—I write down my to-dos every night to have a clear roadmap for the next day, helping me stay productive without feeling overwhelmed. For me, proactive self-care is essential rather than reactive recovery—neglecting it occasionally is fine, but if it becomes the norm, it’s a red flag. The goal isn’t just career success but sustaining it without burnout, ensuring that both professional growth and personal well-being go hand in hand.
What are your long-term career aspirations, and how do you see yourself evolving as a leader over the next five years?
My long-term goal is to become a Chief AI Officer (CAO) and eventually transition into a CTO role, leading AI-driven transformation at scale. I want to shape AI as a core enabler of business strategy—not just as a tool for insights, but as a system that drives automation, real-time decisioning, and risk management at scale. AI in enterprises needs to move beyond isolated models to fully integrated, production-grade systems, and I want to be at the forefront of that evolution.
Over the next five years, I see myself evolving from a technical leader to a strategic AI executive, focusing on AI governance, ethical AI adoption, and deploying AI at scale with tangible business impact. I want to bridge the gap between cutting-edge AI research and real-world execution, ensuring that AI solutions are not just innovative but also practical, explainable, and optimized for business needs.
A key part of my journey will be mentoring and scaling high-performing AI teams, fostering a culture where AI practitioners are empowered to build robust, scalable solutions rather than one-off models. I also aim to work more closely with cross-functional stakeholders—product, risk, compliance, and technology teams—to drive AI adoption that is not just technically sound but also aligned with regulatory and operational constraints.
Ultimately, I want to be in a position where I drive AI adoption at an enterprise level, setting the vision for how AI integrates into critical decision-making systems while ensuring responsible and scalable deployment.
What advice would you give to individuals looking to break into the field of AI and data science?
Breaking into AI and data science isn’t just about memorizing algorithms—it’s about deploying AI at scale, integrating it into real business systems, and making sure models don’t fall apart when faced with live data.
Strong fundamentals in math, statistics, Python, SQL, and cloud platforms are essential, but what truly sets professionals apart is understanding MLOps, data pipelines, and model monitoring. AI isn’t just about accuracy; it’s about solving real problems and ensuring solutions are explainable, scalable, and useful.
Technical skills alone aren’t enough—communicating AI’s business impact is just as critical as building the model itself. Instead of chasing endless certifications, hands-on execution matters: experiment, build end-to-end projects, collaborate with different teams, and learn from those who have deployed AI in production. The field moves fast, and success comes to those who stay curious, adaptable, and focused on real-world impact.