This project delivers an executive-level credit risk and cashflow exposure analysis for manufacturing and B2B environments.
Using Accounts Receivable (AR) data, the analysis identifies customer dependency risk, exposure concentration, and overdue payment behavior that materially impact working capital stability and operational continuity.
The project is designed to reflect CFO-oriented financial risk analytics, similar to practices used in enterprise finance and risk management functions.
In manufacturing and B2B operations, delayed customer payments and excessive revenue concentration expose organizations to:
- Cashflow instability
- Working capital pressure
- Operational disruption due to dependency on a limited number of customers
This analysis evaluates AR exposure through a risk-based financial lens, enabling early identification of structural credit risk.
- Identify customers contributing disproportionately to total AR exposure
- Measure dependency risk that may threaten operational continuity
- Analyze overdue payment behavior using AR aging structure
- Support data-driven credit policy and risk mitigation decisions
- Which customers present the highest cashflow and dependency risk?
- How concentrated is AR exposure across customers?
- What proportion of outstanding receivables exceeds acceptable overdue thresholds?
- Which customers require immediate credit policy intervention?
- Total Outstanding Accounts Receivable
- Exposure Ratio (%)
- Average Days Overdue
- AR Aging Distribution
- Customer Risk Tier
| Exposure Ratio | Risk Tier |
|---|---|
| ≥ 60% | Operational Shutdown Risk |
| ≥ 50% | Dependency Risk |
| ≥ 30% | High Risk |
| < 30% | Controlled |
This classification framework enables executives to prioritize intervention based on financial materiality rather than volume alone.
- Data validation and preparation using PostgreSQL
- Outstanding AR calculation and customer-level exposure analysis
- Credit risk classification based on exposure thresholds
- AR aging and overdue structure evaluation
- Executive-ready dashboard development using Power BI
All analytical steps emphasize reproducibility, transparency, and business relevance.
The Power BI dashboard is structured into four executive-focused sections:
-
Executive Overview
High-level KPIs summarizing AR exposure and financial risk indicators -
Credit Exposure by Customer
Identification of concentration and dependency risk -
AR Aging Structure
Distribution of overdue receivables by aging bucket -
Risk Matrix
Exposure versus overdue behavior to support prioritization and policy decisions
Borneo Mining Services contributes 56% of total AR exposure, exceeding the dependency risk threshold.
This level of concentration represents a material threat to cashflow stability and working capital, requiring immediate executive-level credit policy review.
- Immediate credit limit and payment term review for high-risk customers
- CFO-led audit for customers exceeding dependency thresholds
- Stricter credit controls for accounts with persistent overdue behavior
- PostgreSQL – data querying and validation
- Power BI Desktop – data modeling, DAX, and executive visualization
- Microsoft Excel – initial data inspection
financial-analysis/
│
├── data/
│ ├── customers.sql
│ ├── products.sql
│ └── sales_invoices.sql
│
├── sql/
│ ├── 01_ar_aging.sql
│ ├── 02_exposure_analysis.sql
│ ├── 03_credit_policy_simulation.sql
│ └── 04_bad_probability_credit_scoring.sql
│
├── power_BI/
│ └── finance.pbix
│
├── images/
│ ├── Screenshot (815).png
│ ├── Screenshot (816).png
│ ├── Screenshot (817).png
│ └── Screenshot (818).png
│
└── README.md
This project uses simulated and anonymized data for analytical and portfolio demonstration purposes only. No real customer or company data is disclosed.
Sigit Dwiantoro Data Analyst — Financial Risk & Cashflow Analytics