Mohammad M Tammana

Mustafizur Rahman’s career is a story of persistence, adaptability, and innovation. His professional journey began in Bangladesh, where he worked at some of the nation’s most respected organizations, including AB Bank Limited and Teletalk Bangladesh Limited. In these roles, he moved from frontline operational responsibilities to more advanced analytical positions. At AB Bank, Rahman initially joined as a Management Trainee Officer, where he gained exposure to general banking, foreign exchange, and credit management. His strong performance earned him a promotion to Principal Officer, where he led digital transformation initiatives and began integrating data-driven approaches into financial decision-making. Reflecting on those years, Rahman noted, “It was the perfect training ground. I learned how to see patterns in financial transactions and how small insights could prevent large risks.” His early experience in risk management and data analysis laid the foundation for his later work in artificial intelligence and machine learning.

Transition to Data-Driven Marketing and Technology : 

Rahman’s move to Teletalk Bangladesh Limited marked a new chapter in applying data for strategic impact. As Assistant Manager for Marketing and Value-Added Services, he managed CRM systems, analyzed customer behavior, and spearheaded data-backed marketing campaigns. His work helped personalize communication and enhance customer retention, showing that business intelligence could directly improve consumer experiences. He recalls how the role sharpened his perspective: “At Teletalk, I saw firsthand how data isn’t just numbers—it tells stories about people. If used properly, it can predict their needs before they even realize them.” This transition from finance to telecommunications not only broadened his professional expertise but also deepened his belief in analytics as a transformative force across industries.

Academic Pursuits in the United States : 

Building upon his industry expertise, Rahman later moved to the United States to pursue a Master’s degree in Management Information Systems at Lamar University, Texas. His academic work was not isolated from his career trajectory; instead, it extended his focus on how machine learning and business intelligence can solve systemic problems. While serving as a Graduate Student Assistant at the Mary and John Gray Library, he engaged with peers and faculty on research, academic workshops, and data-driven projects. His dual role as both a student and mentor allowed him to bring real-world experiences into academic spaces. As Rahman often emphasizes, “Academia and industry are not two worlds—they are parallel paths. What we discover in one must be tested in the other.”

The Fraud Detection in Healthcare Insurance Project : 

Among Rahman’s many research efforts, one project stands out for its scale, ambition, and social importance: his work on detecting fraudulent insurance claims in healthcare systems. Published in 2025 in the American Journal of Interdisciplinary Studies, the study employed graph neural network (GNN) models to uncover fraudulent patterns hidden within massive healthcare datasets. Unlike traditional fraud detection methods, which rely on rule-based systems or linear analytics, Rahman’s approach allowed for the mapping of complex relationships among patients, claims, and providers. This enabled his system to detect fraud schemes that would otherwise remain invisible. He explained in an interview-style reflection, “Healthcare fraud is a hidden tax on society. Every false claim inflates costs, drains resources, and undermines trust in the system. My project was about more than detection—it was about protecting patients and ensuring fairness in healthcare.” The project not only drew attention in academic circles but also demonstrated how advanced AI models can address real-world problems with significant financial and social implications

Impact and Broader Significance : 

The significance of Rahman’s healthcare fraud detection project cannot be overstated. Fraudulent insurance claims cost billions of dollars globally, straining healthcare providers and driving up premiums for patients. By creating a scalable and accurate detection model, Rahman’s work showed potential for transformative application in the U.S. healthcare system and beyond. His system was designed to adapt to various datasets, ensuring usability across different insurance markets and regulatory environments. Industry experts reviewing his publication noted its practical applicability, and some suggested pathways for integration into existing insurance frameworks. As Rahman put it, “Innovation only matters if it can be applied. I wanted this model to work not just in theory, but in hospitals, clinics, and insurance offices.”

Linking Past Experiences to Present Breakthroughs : 

What makes Rahman’s project especially compelling is how it draws from his professional history. His early work in risk assessment at AB Bank prepared him to recognize anomalies in financial systems. His time at Teletalk, where he studied consumer behaviors, gave him insights into how patterns can emerge in large datasets. Even his early role at The Daily Prothom Alo, where he analyzed advertising and sales data, contributed to his appreciation of market trends and anomalies. Each step in his career can be seen as a building block leading toward his groundbreaking healthcare fraud research. As he reflects, “I did not set out to focus on healthcare fraud from the start. But each role I took taught me something about data, behavior, and trust. Over time, the path became clear.”

A Global Vision for Data Integrity : 

Rahman’s vision for data science goes beyond any single project. His publications cover a wide range of topics—from big data security in global banking to predictive analytics for renewable energy demand forecasting. Yet, a common thread runs through them: the pursuit of data integrity and systemic trust. He insists that technology must not only improve efficiency but also reinforce fairness and resilience in the systems people rely on every day. “Data has no borders,” he often says. “If we solve problems in healthcare fraud detection here, those solutions can inform how we tackle financial fraud in Bangladesh or energy inefficiencies in Africa.” His global outlook ensures that his work resonates across contexts and contributes to shared challenges in diverse sectors.

On the Horizon : 

As Rahman continues his academic and professional journey in the United States, his focus remains on projects that balance technical innovation with societal impact. His healthcare fraud detection research is just one example of how machine learning can redefine traditional problem-solving frameworks. Colleagues and peers describe him as a visionary who combines analytical precision with human-centered values.

 For Rahman, the future of AI is not merely about building smarter machines, but about creating systems that empower societies to be more just, secure, and sustainable. In his words, “Technology must be built on trust. Without trust, even the most powerful system fails. That principle guides every project I take on.”