Bridging Financial Risk with Data-Driven Intelligence with Moral Philosophy
Exploring financial risk, banking stability, anti-money laundering, customer analytics, and digital transformation through data analytics, explainable AI, and evidence-based research to support smarter and more responsible financial decision-making.
Platform competition analytics examines network effects, pricing, participation, ranking, advertising, switching, access conditions, and strategic interaction across digital ecosystems shaped by previous product and policy choices. The article shifts attention from prediction to intervention by defining treatment, outcome, population, time horizon, and the counterfactual. It highlights confounding in historical business data and uses experiments, natural experiments, quasi-experimental designs,
AI-enabled employee engagement includes survey analytics, sentiment analysis, personalized recommendations, workforce listening, communication tools, and manager decision support, all operating within sensitive employment relationships. The article explains adoption gaps through organizational capability rather than enthusiasm alone. Data maturity, infrastructure, governance, privacy processes, leadership support, incentives, switching costs, and local evidence determine whether advanced analyti
AI-enabled employee engagement includes survey analytics, sentiment analysis, personalized recommendations, workforce listening, communication tools, and manager decision support, all operating within sensitive employment relationships. The article shows how implementation can fail even when the model still operates. Data fragmentation, proxy metrics, workflow friction, unclear ownership, and misaligned incentives can disconnect analytics from value, so baseline comparisons, balanced measures, i
AI-enabled employee engagement includes survey analytics, sentiment analysis, personalized recommendations, workforce listening, communication tools, and manager decision support, all operating within sensitive employment relationships. The article compares humans and algorithms on matched tasks, information, and outcome horizons rather than benchmark scores alone. Human context and judgment are weighed against algorithmic scale and repeatability, while hybrid rules, disagreement analysis, blind
AI business model renewal concerns changes in value creation, value delivery, and value capture through AI-enabled services, workflow redesign, personalization, pricing support, and new information products. The article emphasizes real-world causal testing through random assignment, phased rollouts, encouragement designs, or cluster experiments. It calls for pre-specified outcomes, contamination controls, comparisons among human-only, algorithm-only, and hybrid workflows, and stop rules when har
Talent decision fairness covers recruitment, promotion, performance management, development, scheduling, and related employment decisions, with particular attention to proxies, subgroup outcomes, accessibility, and calibration. The article treats explainability as useful only when it changes or supports action. It compares transparent and complex models, tests explanation stability, warns that feature importance is not causality, and incorporates fairness, calibration, error costs, operational c
Talent decision fairness covers recruitment, promotion, performance management, development, scheduling, and related employment decisions, with particular attention to proxies, subgroup outcomes, accessibility, and calibration. The article keeps accountability with the deploying organization through named owners, separation of development and approval authority, reproducible documentation, meaningful human oversight, and formal reassessment when scope, data, vendors, or policies change. Override
Talent decision fairness covers recruitment, promotion, performance management, development, scheduling, and related employment decisions, with particular attention to proxies, subgroup outcomes, accessibility, and calibration. The article distinguishes data drift from concept drift and separates model quality from decision quality. It favors temporal validation, subgroup monitoring, statistical drift signals interpreted with business context, and pre-defined triggers for recalibration, retraini
Explainable prescriptive analytics goes beyond predicting outcomes by recommending actions under objectives and constraints, which requires separating predictive models, causal assumptions, optimization rules, and business limits. The article shifts attention from prediction to intervention by defining treatment, outcome, population, time horizon, and the counterfactual. It highlights confounding in historical business data and uses experiments, natural experiments, quasi-experimental designs, s
This article explains uneven adoption of Omnichannel Attribution across Canadian Markets, arguing that differences in capability and readiness matter as much as enthusiasm for new technology. It estimates how online and offline touchpoints contribute to conversion, while identity gaps, walled-garden platforms and the difference between observed credit and causal incrementality complicate interpretation. Larger or more mature organizations can invest in data engineering, experimentation, governan
This article explains why Omnichannel Attribution can fail in Canadian Markets even when the underlying analytical model appears technically strong. It estimates how online and offline touchpoints contribute to conversion, while identity gaps, walled-garden platforms and the difference between observed credit and causal incrementality complicate interpretation. The main failure modes arise earlier in the system: fragmented identifiers and definitions, weak ownership, vague objectives, poor basel
This article compares human and algorithmic decision making for Omnichannel Attribution in Canadian Markets, focusing on downstream decision quality rather than prediction accuracy alone. It estimates how online and offline touchpoints contribute to conversion, while identity gaps, walled-garden platforms and the difference between observed credit and causal incrementality complicate interpretation. Human judgment contributes context, tacit knowledge, strategic interpretation and sensitivity to
This article evaluates Algorithmic Hiring in American Workplaces through field experiments designed to test whether analytical or automated interventions improve outcomes under real operating conditions. It covers sourcing, screening, assessment, ranking, interviewing and recommendation, where candidate access, protected-group outcomes, productivity and experience can diverge across stages. A credible experiment begins with a narrow treatment, prespecified outcomes and explicit attention to both
This article considers how explainable modeling can improve Decision-Aware Machine Learning in American Firms by linking interpretation to real decisions rather than treating explanation as a dashboard feature. It treats prediction as a means to action and evaluates technical model behavior together with the downstream business, fairness, safety, service and resource consequences of decisions. It argues that optimization should balance predictive performance with interpretability, error costs, c
4 Aug 2026
CategoryAdvanced Analytics, Causal Methods & Model Reliability
This article examines how Decision-Aware Machine Learning should be governed in American Firms when analytical recommendations influence consequential organizational decisions. It treats prediction as a means to action and evaluates technical model behavior together with the downstream business, fairness, safety, service and resource consequences of decisions. Its central principle is that responsibility remains with the deploying organization: software or vendors do not absorb legal, managerial
11 Aug 2026
CategoryAdvanced Analytics, Causal Methods & Model Reliability
This article examines Decision-Aware Machine Learning in American Firms, arguing that technical performance is insufficient unless analytical outputs continue to improve real decisions as operating conditions change. It treats prediction as a means to action and evaluates technical model behavior together with the downstream business, fairness, safety, service and resource consequences of decisions. It treats data drift and concept drift as normal operating conditions that can alter inputs, pred
18 Aug 2026
CategoryAdvanced Analytics, Causal Methods & Model Reliability
Sabbir Ahmed Chowdhury. Research manuscript investigating privacy-preserving causal marketing analytics and sustainable personalization. Using 2,205 anonymized customer records, the study evaluates whether data minimization can preserve causal, predictive, and customer-value performance while reducing dependence on extensive behavioral data.
Sabbir Ahmed Chowdhury. Research manuscript examining how generative AI is reshaping platform work, with particular emphasis on job quality, skill polarization, algorithmic management, and worker agency. The study uses a systematic comparative evidence synthesis of 28 peer-reviewed journal articles published between 2017 and 2025 and compares pre- and post-generative-AI research patterns.
Sabbir Ahmed Chowdhury. Research manuscript proposing a socio-technical destination digital twin framework for climate-resilient tourism. The study integrates tourism demand forecasting, mobility simulation, visitor experience, operational carbon emissions, and resident wellbeing within a unified decision-support framework.
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Education & Experience
2013
Champion – AIUB Premier League T20 Cricket Tournament
American International University-Bangladesh (AIUB) (February 2, 2013)
Member of the champion team in the AIUB Premier League T20 Cricket Tournament, demonstrating teamwork, discipline, competitive spirit, and collaboration.
2003
2nd Place – 440 Meter Sprint
Ideal School and College (February 5, 2003)
Secured second position in the 440-meter sprint, reflecting athletic ability, perseverance, discipline, and competitive performance.
2017
Cash In-Charge
Mutual Trust Bank PLC, Gulshan Branch (2017 – Present)
Leading branch cash operations, including cash-flow monitoring, reconciliation, vault management, transaction accuracy, team supervision, compliance, auditing, customer service, and operational risk management. Also supports business development and coordinates responses to cash shortages, system interruptions, and other operational emergencies.
2014
Customer Service Executive
Mutual Trust Bank PLC, Card Division (2014 – 2017)
Managed customer inquiries, card transaction issues, dispute resolution, billing and payment assistance, card activation and replacement, credit-limit requests, complaint documentation, product information, cross-selling, and monitoring of unauthorized or suspicious card transactions.
2013
Pre-Sales & Product Portfolio Management Intern
GrameenPhone IT (2013)
Contributed to the development of PIPILIKA, one of the early Bangla search-engine initiatives. Coordinated between the Shahjalal University of Science and Technology technical team and GPIT, identified search-algorithm issues, and supported research, business-development ideas, and revenue-expansion initiatives.
2018
Master of Business Administration (MBA) in Tourism & Hospitality Management
University of Dhaka (2014–2018)
Completed MBA in Tourism and Hospitality Management, earning a CGPA of 3.62/4.00. Degree awarded for the 2018 examination.
2014
Bachelor of Business Administration (BBA) in Human Resource Management
American International University-Bangladesh (AIUB) (2009 – 2014)
Completed BBA with a major in Human Resource Management, earning a CGPA of 3.35/4.00. The official transcript records graduation on 27 February 2014.
2008
Higher Secondary Certificate (HSC) – Business Studies
Dhaka Commerce College (2008)
Completed HSC in Business Studies with GPA 4.90/5.00 under the Board of Intermediate and Secondary Education, Dhaka.
2006
Secondary School Certificate (SSC) – Business Studies
Ideal School and College, Motijheel (2006)
Completed SSC in Business Studies with GPA 4.69/5.00 under the Board of Intermediate and Secondary Education, Dhaka.
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Available for research collaborations, speaking engagements, and consulting on AI ethics frameworks.
