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A DMAIC-Analytics Framework for Cost, Pricing, and Margin Optimization in Financial Services
Subject area: Management and Commerce · Area of research: Financial Analytics and Optimization
Abstract
Financial-services firms operate under compounding pressure from margin compression, regulatory scrutiny of pricing conduct, and the rapid diffusion of data-driven competitors. Yet cost, pricing, and margin decisions are frequently made in organizational silos, using inconsistent data and weakly governed heuristics, which produces value leakage and reputational risk. This paper proposes a conceptual framework that fuses the Define-Measure-Analyze-Improve-Control (DMAIC) cycle of Lean Six Sigma with contemporary business analytics to deliver disciplined, auditable, and continuously improving cost, pricing, and margin management. We name the framework the DMAIC-Analytics Margin Engine (DAME). For each DMAIC phase we map a set of analytics techniques, ranging from cost-to-serve decomposition and process capability analysis to price-elasticity estimation, machine-learning propensity models, and statistical process control, to specific commercial levers across the cost, pricing, and margin dimensions. The framework is presented through a described architecture figure and a phase-by-technique-by-lever mapping table, and is illustrated with a stylized application to a retail-and-commercial bank optimizing the pricing and cost-to-serve of a lending portfolio. We argue that embedding analytics inside a DMAIC control scaffold addresses three chronic weaknesses of analytics-only pricing initiatives: the absence of a defined problem and baseline, the tendency toward one-off models that decay, and the lack of governance over deployed decisions. We discuss benefits, limitations, and governance requirements, including model risk management and fair-pricing obligations, and outline a research agenda spanning causal inference, reinforcement learning, and responsible-analytics governance. The contribution is a coherent, practitioner-oriented synthesis rather than an empirical test.
Keywords
DMAIC; Lean Six Sigma; pricing optimization; margin management; cost-to-serve; business analytics; financial services; statistical process control; model risk governance
How to cite this paper
@article{1722631,
author = {Funmilayo Ashore-Onisemo, Ebehiremen Faith Iziduh},
title = {A DMAIC-Analytics Framework for Cost, Pricing, and Margin Optimization in Financial Services},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {12},
pages = {839-876},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722631.pdf},
abstract = {Financial-services firms operate under compounding pressure from margin compression, regulatory scrutiny of pricing conduct, and the rapid diffusion of data-driven competitors. Yet cost, pricing, and margin decisions are frequently made in organizational silos, using inconsistent data and weakly governed heuristics, which produces value leakage and reputational risk. This paper proposes a conceptual framework that fuses the Define-Measure-Analyze-Improve-Control (DMAIC) cycle of Lean Six Sigma with contemporary business analytics to deliver disciplined, auditable, and continuously improving cost, pricing, and margin management. We name the framework the DMAIC-Analytics Margin Engine (DAME). For each DMAIC phase we map a set of analytics techniques, ranging from cost-to-serve decomposition and process capability analysis to price-elasticity estimation, machine-learning propensity models, and statistical process control, to specific commercial levers across the cost, pricing, and margin dimensions. The framework is presented through a described architecture figure and a phase-by-technique-by-lever mapping table, and is illustrated with a stylized application to a retail-and-commercial bank optimizing the pricing and cost-to-serve of a lending portfolio. We argue that embedding analytics inside a DMAIC control scaffold addresses three chronic weaknesses of analytics-only pricing initiatives: the absence of a defined problem and baseline, the tendency toward one-off models that decay, and the lack of governance over deployed decisions. We discuss benefits, limitations, and governance requirements, including model risk management and fair-pricing obligations, and outline a research agenda spanning causal inference, reinforcement learning, and responsible-analytics governance. The contribution is a coherent, practitioner-oriented synthesis rather than an empirical test.},
keywords = {DMAIC; Lean Six Sigma; pricing optimization; margin management; cost-to-serve; business analytics; financial services; statistical process control; model risk governance},
month = {June},
}