Financial and economic cost modeling of products and services: the role of cost drivers, data quality, and analytical infrastructure

Introduction

According to OECD data, small and medium-sized enterprises account for approximately 99% of all companies in OECD countries and generate more than half of total economic value added [1]. Therefore, the sustainability of SMEs directly affects economic stability, employment, and entrepreneurial development.

In modern economic conditions, a company’s ability to effectively manage costs has become one of the key factors of long-term sustainability and competitiveness. This issue is especially relevant for organizations with high operational workloads and complex business process structures.

In many companies, cost calculation is still based on simplified models focused primarily on direct expenses. A significant portion of indirect costs is allocated conditionally or not considered at all, which leads to distorted assessments of actual profitability.

Problems of Traditional Cost Calculation Approaches

In many organizations, cost analysis is still performed manually using Excel spreadsheets into which data is collected from multiple disconnected sources. This approach creates several systemic limitations, including a high probability of errors, lack of unified calculation logic, inability to quickly recalculate data, and low analytical reporting speed.

An additional problem is the subjective allocation of indirect expenses. In the absence of a transparent cost driver system, many costs are distributed conditionally without considering the actual operational load on processes, departments, or products.

As a result, companies often face situations where revenue growth is not accompanied by profitability growth, while business scaling leads to increasing hidden operational expenses.

Cost Drivers as the Foundation of Accurate Cost Modeling

One of the key elements of building an accurate cost model is the identification of cost drivers. Cost drivers are indicators that directly influence the formation of company expenses.

In practice, product or service costs cannot be accurately calculated solely based on direct expenses. An objective model requires consideration of employee labor costs, transaction volumes, process duration, regional factors, infrastructure load, process support expenses, and the level of automation.

The quality of financial and economic modeling depends not only on mathematical calculations but also on the completeness, transparency, and reliability of source data.

Data Architecture and Data Quality

Modern studies demonstrate that one of the primary reasons for the inefficiency of AI projects is the lack of standardized data structures, transparent business process logic, and high-quality analytical integration systems [2].

Even the most advanced analytical models cannot ensure reliable results in the presence of incomplete data, inconsistencies, or fragmented information systems.

Building an effective cost modeling system requires the integration of multiple categories of data, including financial information, HR data, operational indicators, reference tables, and product-related information.

Automation of Financial and Economic Modeling

Modern financial and economic modeling systems are increasingly based on automated data processing technologies, including SQL logic, ETL processes, analytical data marts, and BI systems.

Automation significantly reduces calculation time, minimizes the impact of human error, improves analytical accuracy, and enables multidimensional analysis.

Visualization systems are becoming particularly important, allowing businesses to analyze indicators by products, regions, departments, operations, and expense categories.

Practical Example from the Banking Sector

As part of a practical project aimed at developing a cost calculation system for the operational department of a large commercial bank, a comprehensive expense allocation model was implemented based on the integration of multiple data sources [7].

Before implementation, calculations were performed manually using Excel, and reporting preparation required up to 14 days per reporting period. The project introduced a unified architecture integrating more than seven data sources, processing over 350,000 rows of data per reporting period, and supporting a multi-level cost allocation system.

As a result, the total calculation time was reduced to less than 50 minutes, while transparency and analytical quality significantly improved.

The Role of AI and Future Development of Analytics

According to international studies conducted by McKinsey and Deloitte, the adoption of AI and data-driven analytics continues to grow rapidly in the corporate sector [3, 4]. Companies implementing AI in financial analysis and operational management can significantly improve process efficiency and accelerate decision-making.

AI-supported systems are capable of forecasting expense changes, detecting anomalies, and building scenario-based models. However, the effectiveness of such solutions directly depends on the quality of the underlying data architecture and the accuracy of cost driver systems.

Even the most advanced AI models cannot compensate for poor-quality source data or the absence of transparent expense allocation logic.

Why AI Cannot Compensate for Poor Data Architecture

One of the most common misconceptions in modern business analytics is the belief that artificial intelligence itself can solve operational and financial inefficiencies. In practice, however, AI systems are only as effective as the quality of the underlying data and analytical infrastructure.

If a company lacks a transparent expense allocation methodology, standardized business logic, and validated source data, AI systems may simply scale existing analytical errors. Incorrect cost drivers, fragmented databases, and inconsistent operational indicators can lead to distorted forecasts and inaccurate profitability analysis.

Therefore, organizations seeking to implement AI-driven analytics must first establish a reliable data architecture and a transparent financial modeling framework.

Conclusion

Financial and economic cost modeling is becoming one of the most important components of strategic management in modern organizations.

An accurate cost calculation model is impossible without transparent data architecture, a structured cost driver system, high-quality analytical infrastructure, and automated data processing.

Companies capable of building scalable and transparent financial modeling systems gain significant advantages in cost management, profitability forecasting, and operational efficiency improvement.

Table

Main Cost Drivers in Cost Modeling