Machine learning can change the way indirect costs are allocated by using large collections of operational data, resource utilization, and past spending patterns to accurately predict what will drive costs. Algorithms can find hidden links between overhead costs and business units, which makes it possible to allocate costs depending on what is happening in real time.

ML models can keep learning from changes in how things work, which helps them find inefficiencies and improve budgets. Predictive analytics may also run "what-if" scenarios, which assist management make decisions based on data that will cut down on waste and boost profits. In the end, this turns cost allocation from a static, manual procedure into a smart, adaptive system.

Qasim Habeeb Nashid this might help do go through.

Machine learning (ML) holds transformative potential for the allocation of indirect costs, moving far beyond the constraints of traditional, rule-based methods. Here’s how ML can revolutionize this area, along with the key challenges companies might face during implementation:

How ML Revolutionizes Indirect Cost Allocation

· Data-Driven Accuracy: ML models, such as decision trees and random forests, can analyze massive datasets to identify patterns in cost allocation that traditional methods (e.g., simple allocation bases like labor hours or square footage) might overlook. Studies have found ML-based models achieve allocation forecast accuracy between 80–95%, reducing error rates by about 20% compared to statistical methods.

· Capturing Complex Relationships: ML can handle multiple variables simultaneously (e.g., fluctuating utility costs, mixed resource usage, project interconnections) to distribute costs more accurately and equitably among departments or products. This means less distortion in financials, better pricing, and sharper profitability analysis.

· Continuous Learning and Adaptation: Unlike static allocation formulas, ML systems learn from new data over time. As business processes or resource usage evolve, models can update to reflect current realities without manual rule changes, continuously optimizing allocations.

· Automation and Scalability: ML automates much of the data collection, processing, and allocation tasks. This reduces manual effort, speeds up reporting, and allows the system to handle increasing data volume seamlessly as organizations grow.

Key Challenges in Implementing ML for Cost Allocation

· Data Preparation and Quality: ML’s effectiveness relies on large quantities of accurately categorized and labeled data. Many companies struggle with fragmented or inconsistent expense data, which can hinder ML training and result accuracy.

· Model Interpretability: Advanced ML models can function as “black boxes,” making it difficult for finance teams to understand and audit the rationale behind particular allocations—especially for compliance or regulatory reporting.

· Organizational Buy-In: Moving away from familiar, transparent methods (like using square footage as a cost driver) to algorithmically-determined allocation often meets internal resistance. Stakeholders may be skeptical of the fairness or transparency of ML-driven results.

· Implementation Complexity: Deploying ML solutions requires technical expertise in data science, as well as integration with accounting/ERP systems. Smaller organizations may lack these resources, increasing upfront costs and timeline.

· Change Management and Governance: Effective implementation demands new cost governance policies, ongoing model monitoring, and clear communication with all stakeholders to ensure trust and understanding of the new allocation logic.

Machine learning offers a way to unlock granular, unbiased, and highly adaptive indirect cost allocation. However, realizing these benefits depends on the organization’s capacity to address the challenges of data quality, interpretability, and change management throughout implementation.

Dear Qasim Habeeb Nashid ,

Machine learning can change the way indirect costs are allocated by using large collections of operational data, resource utilization, and past spending patterns to accurately predict what will drive costs.

The current momentum in Machine Learning (ML) development and adoption is making companies to reflect on their positioning and the associated configuration of their resource bases. Some companies try to stand out with leading ML models (e.g., OpenAI with ChatGPT) or to capture the market with resource-integrating service platform offerings.

Article The Impact of Resource Allocation on the Machine Learning Lifecycle

Regards,

Shafagat

Qasim Habeeb Nashid

Machine Learning in Indirect Cost Allocation – A Real-World and Research-Aligned View

In many industries—especially banking, healthcare, and large-scale manufacturing—indirect costs like compliance, IT overhead, or security operations are often allocated using static formulas (e.g., headcount, floor space). These methods miss the complexity of modern operations where costs are driven by dynamic, multi-factor usage patterns.

Real-World Scenario

Imagine a multinational bank allocating its cybersecurity overhead. Traditional allocation might spread costs evenly across departments. In practice, risk exposure varies—high-frequency trading desks generate more encrypted transactions, triggering higher storage, encryption, and integrity verification demands. An ML model could:

Integrate multiple cost drivers in real time—transaction volumes, encryption key rotations, anomaly detection alerts.

Adapt to changing business conditions—e.g., spikes in secure data transfers during regulatory audits.

Link allocation to actual consumption—charging departments proportionally to the computational, storage, and security resources they use.

Similarly, in a hospital network, indirect IT costs could be allocated based on actual use of encrypted patient data retrieval, as tracked by secure access logs. This approach not only improves fairness but also incentivizes cost-conscious behavior.

Challenges

Data integration: Indirect cost drivers are often spread across ERP, security logs, and IoT devices.

Model explainability: Finance teams and auditors must understand why certain departments are allocated more cost.

Governance and compliance: Models must align with regulatory frameworks such as Basel III in banking or HIPAA in healthcare.

Alignment to My Research

In my IEEE paper on Data Governance , I showed that strong governance improves ML accuracy and compliance by standardizing and cleansing inputs. For cost allocation, this ensures that multi-source operational data—e.g., encrypted transaction logs, storage metrics—remains reliable and auditable.

In my patent on Distributed Cloud Data SecurityPatent, I developed a blockchain-backed, attribute-based encryption framework with multi-user integrity verification. The same architecture could feed ML models with trustworthy, tamper-proof cost driver data, such as encryption runtimes or keyword search activity, enabling fair and compliant cost allocation.

By combining governance-driven data quality with secure, verifiable operational metrics, companies can implement ML-based cost allocation models that are both technically accurate and regulatorily defensible.

Please read more on my research here :

Data Governance :-

https://www.researchgate.net/publication/391826918_Efficacy_of_Data_Governance_a_Cutting_Edge_Approach_to_Ensuring_Data_Quality_in_Machine_Learning_for_Banking_Industry