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All Journal The Eastasouth Journal of Information System and Computer Science
Semanto Saha Utsab
Montclair State University, Montclair, New Jersey, United States
Computer Science & IT

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Business Analytics for United States Retail Channel Planning Under Structural Change Md Jubayar Hossain; Semanto Saha Utsab; Sadia Afrin Jarir; Evha Rozario; Zerin Akter Tanni; Tushar Roy
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1263

Abstract

Retail channel planning requires forecasts that remain useful when purchasing patterns change abruptly. This study examines the quarterly e-commerce share of United States retail sales using 99 observations from 2000 Q1 through 2024 Q3 in the data vintage available on December 31, 2024. Census Bureau estimates distributed through ALFRED provide the sole empirical data source. Six transparent specifications are compared at 59 successive one-quarter forecast origins using chronological estimation. Evaluation distinguishes 2010–2019, 2020–2021, and 2022–2024 Q3 and combines absolute error, squared error, directional bias, and asymmetric planning-loss sensitivity. The lowest mean absolute errors in these periods are 0.0755, 0.9375, and 0.1215 percentage points, respectively. Method rankings depend on the evaluation period and criterion. The findings support period-specific assessment, simple benchmarks, and explicit attention to planning consequences. Results use a fixed historical data vintage; they do not establish causal pandemic effects or realized firm-level savings.
Cost Sensitive Machine Learning for Credit Risk Assessment and Lending Decisions Muhammad Imran Syed; Mitu Akter; Semanto Saha Utsab; Md Tanvirul Islam; Yeasmin Jahan
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1264

Abstract

Machine learning can improve credit risk prediction, yet predictive gains do not automatically translate into better lending decisions. The financial value of a score depends on which errors it makes, whether its probability is calibrated, and how its operating threshold reflects the costs of approval and rejection. This study develops an analytical framework linking these quantities for statistical and ensemble credit scoring models. Published findings on classifier benchmarking, imbalanced credit data, behavioral information, cost-sensitive learning, and distributional effects establish the research context. The original analysis derives feasible loss bounds from accuracy and class prevalence, identifies conditions for economic dominance between classifiers, and examines sensitivity to error costs and probability uncertainty. Deterministic scenarios use adverse class prevalences from 5% to 50%, relative error penalties from one to ten, and classifier accuracies from 70% to 95%. No borrower-level models are newly trained. At 85% accuracy, 30% adverse prevalence, and a five-to-one error penalty, feasible loss ranges from 150 to 750 units per 1,000 applications. Two feasible classifiers with accuracies of 85% and 80% can incur losses of 750 and 200 units, respectively, demonstrating a possible reversal between statistical and economic rankings. Expected loss minimization gives a probability threshold of one sixth at a five-to-one penalty ratio. A general regret expression also shows why probability uncertainty near that threshold deserves particular attention. The framework explains how findings from credit scoring research can inform model selection without treating results from different populations as directly comparable. It supports evaluating the combined prediction and decision process through discrimination, calibration, financial consequences, and distributional outcomes.
Co-Authors Evha Rozario Md Jubayar Hossain Md Tanvirul Islam Mitu Akter Muhammad Imran Syed Sadia Afrin Jarir Tushar Roy Yeasmin Jahan Zerin Akter Tanni