All projects
Investment & Fintech
10 months2023 — 2024

Cost Allocation & MIS Reporting Platform

Financial Cost Allocation & Shared-Services MIS

A rule-driven engine that allocates shared costs across business units and produces drill-down MIS reports.

Rule-driven

Engine

Drill-down MIS

Reports

Ledger dumps

Input

Page-level RBAC

Access

Overview

A management-information platform that ingests ledger dumps, applies configurable allocation rules to distribute shared-services costs across departments and cost centres, and generates drill-down MIS reports.

The business problem

Allocating shared costs across business units was a manual, opaque spreadsheet exercise that was hard to audit and slow to reproduce each period.

The solution

I built a Flask backend with a custom allocation rule engine over a hierarchical PostgreSQL model, plus an Excel-template reporting system that lets finance configure allocations rather than recompute them by hand.

My responsibilities

  • Designed the department / cost-centre / account-group hierarchy
  • Built the configurable allocation rule engine
  • Implemented ledger-dump ingestion and normalization
  • Delivered drill-down MIS reports with template-based output

Challenges solved

  • Expressing complex, multi-level allocation logic as configurable rules
  • Validating messy ledger dumps before processing
  • Keeping historical allocations reproducible

Key features

Master data for departments, cost centres and account groups
Configurable allocation rule definition and execution
Ledger-dump ingestion and normalization
Drill-down MIS reporting with templates

Architecture highlights

  • Flask REST API organised with Blueprints
  • Custom rule engine executing hierarchical allocations
  • Excel-based ingestion and template-driven reporting
  • Page-level RBAC over financial data

Technologies used

PythonFlaskSQLAlchemyPostgreSQLpandasopenpyxlAngularAngular MaterialNginx

Scalability

The rule engine is data-driven, so new allocation logic is configured, not coded, and batch processing handles growing ledger volumes.

Performance

  • Vectorised allocation math with pandas
  • Multi-step batch report generation
  • Indexed hierarchy traversal

Security

  • Role-based access with page-level permissions
  • Audit logging of all data modifications
  • Validated ingestion before processing

Screenshots

Product screenshots available on request (NDA-protected).

Architecture diagram

Detailed system architecture available on request.