# Bank Reconciliation — Three-Source Analysis (2026-05-26)

Reference for matching bank transactions against Coop SAP exports and Uniconta
ledgers. This extends the two-source reconciliation with a third (bank) dimension
that serves as the "ground truth" for actual cash flow.

---

## Bank file format (Danske Bank CSV export)

| Kolonne | Type | Brug |
|---------|------|------|
| Dato | DD.MM.YYYY | Primær dato |
| Valør | DD.MM.YYYY | Sekundær dato |
| Tekst | string | **Identifikation** |
| (tom) | — | Padding |
| Beløb | +/- flot/DK | Primær beløb |
| Saldo | flot/DK | Sekundær — løbende saldo |
| Egen bilagsreference | string | Audit/trace |

**CSV separator**: `;` (semikolon)
**Encoding**: `latin-1` (CP1252)
**Decimal**: Danish comma, Danish point-as-thousand-separator
**Negative**: explicit minus in front

Typisk tekstmønster for Coop-indbetalinger:
- `"Coop  0010014808 -SE MEDD."`
- Samme kundenummer for ALLE transaktioner
- Stort set alle transaktioner har identisk tekst

---

## Bank transaktionskategorier

### Standard indbetalinger (~98%)
- Beløb fra ~30.000 kr til ~800.000 kr
- Sum 2024: +9.225.626 kr (129 stk)
- Sum 2025: +10.031.368 kr (118 stk)
- Udgør den "faktiske" Coop-omsætning der rammer banken

### Negative poster — bonus/modregning/retur (~1-2%)
| Dato | Beløb | Tekst | Type |
|------|-------|-------|------|
| 13.06.2024 | -18.750,00 | COOP DANMARK AS | Bonus-tilbagebetaling |
| 25.11.2024 | -31,25 | Coop 365 | Lokal retur/modregning |
| 22.04.2025 | -128,00 | DK 57014 Coop Kvickly Slagelse | Butiksretur |
| 13.05.2025 | -40,00 | DK 50960 Coop Kvickly Slagelse | Butiksretur |

**Total negative: -18.949,25 kr**

---

## THREE-SOURCE MATCHING CONCEPT

```
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Coop Excel     │     │  Uniconta Excel │     │  Bank CSV       │
│  (fakturaer)    │     │  (bogføring)    │     │  (indbetalinger)│
└────────┬────────┘     └────────┬────────┘     └────────┬────────┘
         │                       │                       │
         ▼                       ▼                       ▼
┌──────────────────────────────────────────────────────────────────┐
│          MATCH LAYER 1: Post-for-post (Coop ↔ Uniconta)          │
│  Reference+abs(amount)+date → 68% match rate                     │
├──────────────────────────────────────────────────────────────────┤
│          MATCH LAYER 2: Month-level (All 3 sources)              │
│  Sum(Coop) ≈ Sum(Uniconta) ≈ Sum(Bank) per month                │
├──────────────────────────────────────────────────────────────────┤
│          MATCH LAYER 3: Bonus reconciliation (separate)           │
│  Sum(KG + Kreditnota) per period ≈ Bank negative per release     │
│  (tolerate time lag — bank credit arrives later than postings)   │
└──────────────────────────────────────────────────────────────────┘
```

---

## Bank-to-Coop / Bank-to-Uniconta matching

### Month-level validation (PRIMARY)

Compare month totals across all three sources. If Coop and Uniconta diverge,
the bank settles the dispute.

| Måned | Bank (kr) | Coop (kr) | Uniconta (kr) | Status |
|-------|-----------|-----------|---------------|--------|
| 2024-04 | 2.139.610 | TBD | TBD | To verify |
| 2024-05 | 1.632.814 | TBD | TBD | To verify |
| 2024-09 | 1.053.598 | TBD | TBD | To verify |

**Interpretation rule:**
- Bank ≈ Coop: period is captured correctly in Coop
- Bank ≈ Uniconta: period is correct in Uniconta
- Bank >> Coop: missing Coop postings (likely late/omitted)
- Bank >> Uniconta: missing Uniconta postings
- Bank << both: bonus/modregning not yet reflected in accounting

### Many-to-one: Bank single payment = sum of Coop invoices

Each bank posting (e.g. 641.168,06 kr) is a **consolidated transfer** that
aggregates many individual Coop invoices. Algorithm:
1. Take bank amount B on date D
2. Find Coop transactions in [D-30 days, D+3 days] that sum to B ± tolerance
3. This is a "subset-sum" / knapsack search:
   ```python
   def find_combo(target, candidates, tol=5.0):
       # Greedy for small candidate sets
       # DP for larger sets
       candidates = sorted([c for c in candidates 
                           if abs(c.amount) <= target + tol])
       # ... subset sum with tolerance
   ```
4. If exact match: mark all matched Coop invoices as "grouped under bank payment"
5. If near-match (±500 kr): flag for manual review — likely partial payment or fees

---

## Bonus / Rebate reconciliation

### The mismatch:why Bank negatives don't match individual KG/Kreditnota

Coop KG-posteringer: 486 stk, total -1.101.849,97 kr
Bank negative total: -18.949,25 kr (4 stk)
Uniconta Kreditnota total: -4.593.829,17 kr
Uniconta Rabat: 1 stk, -178.194,55 kr

**Observation:** Bank -18.750 kr does NOT match ANY individual Coop KG posting
(nor any Uniconta Kreditnota). The nearest Uniconta Kreditnota is -18.961 (2025-03-21).

**Interpretation:** Bank negatives are **consolidated credit notes or bonus
payments** that aggregate MANY smaller individual credit postings across a period
(quarter or year). They arrive in the bank later than the individual postings.

### Suggested bonus reconciliation strategy

1. **Period-based grouping** (not transaction-based):
   - Group Coop KG by quarter/year
   - Group Uniconta Kreditnota by linked Faktura-number
   - Group Uniconta Rabat by year
2. **Compare aggregated totals per period** with bank negatives per period:
   ```
   Coop KG per period = sum(KG amounts where Posting Date in period)
   Uniconta credit per period = sum(Kreditnota + Rabat amounts in period)
   Bank negative per period = sum(bank negative amounts with Value Date in period)
   ```
3. **Differences indicate:
   - Timing: bank credit arrives 1-3 months after postings
   - Pooling: quarterly/yearly bonus vs monthly individual credits
   - Source mismatch: some credits originate from Uniconta only, some from Coop only
4. **Manual review** for amounts > 50.000 kr discrepancies per period

### Special: Uniconta "Rabat" -178.194,55 "Coop bonus 2023"

- Single large posting on 2024-01-16
- No matching bank negative from 2023 or early 2024 found
- Likely an **accrual/adjustment** recognized in 2024 for 2023 bonus, but the
  actual cash flow may have been handled differently (e.g. applied as discount
  on future invoices rather than direct bank transfer)
- **Flag:** accrual-type postings should NOT be matched against bank — they
  are non-cash adjustments. Separate them into "bonus accrued" vs "bonus paid".

---

## Key insight: Three sources triangulate

| Question | How to answer with 3 sources |
|----------|-------------------------------|
| "Is this invoice paid?" | Uniconta Faktura + Coop RE + Bank amount all agree |
| "Did bonus arrive in cash?" | Bank negative confirms, Coop KG shows breakdown |
| "Is Uniconta missing a posting?" | Bank has it but Uniconta doesn't → missing entry |
| "Is Coop missing a posting?" | Bank has it but Coop doesn't → missing entry |
| "Is this payment split?" | Bank single amount = sum of multiple Coop invoices |
| "Is there a bonus adjustment?" | Coop KG present, Uniconta Kreditnota present, but bank negative missing → accrual |

---

## Practical notes for the webapp

### Bank import
- Import as a third source alongside Coop and Uniconta
- Store as `Transaction(source="bank", ...)` with minimal fields:
  - date, amount, text, original_value_date, original_balance
- Apply the same year filter (2024-2025) to exclude stray transactions

### Bank-specific views
1. **Month-level dashboard**: Three-bar chart per month (Coop, Uniconta, Bank)
2. **Divergence report**: Months where |Coop - Bank| > tolerance
3. **Bonus reconciliation**: Separate tab for aggregated KG/Kreditnota/Rabat vs bank negatives
4. **Many-to-one explorer**: Click a bank transaction to see suggested Coop invoice combinations that sum to the bank amount

### Data model extension for bank
```python
class BankTransaction(models.Model):
    date = models.DateField()
    value_date = models.DateField(null=True)
    amount = models.DecimalField(max_digits=18, decimal_places=2)
    text = models.TextField()
    balance = models.DecimalField(max_digits=18, decimal_places=2)
    source_file = models.ForeignKey(SourceFile, ...)
```
Or reuse existing `Transaction` model with `source="bank"`.

---

## Performance / verification script

Use `docker exec` with the container's Python + pandas to parse bank CSVs
and cross-check against already-loaded Coop/Uniconta data.

```python
# Quick verification of month-level totals
import pandas as pd

def parse_bank_csv(path):
    df = pd.read_csv(path, sep=';', encoding='latin-1',
                     header=0, names=['Dato','Valør','Tekst','_','Beløb','Saldo','Ref'])
    df['Dato'] = pd.to_datetime(df['Dato'], format='%d.%m.%Y', errors='coerce')
    df['Beløb'] = df['Beløb'].str.replace('.', '').str.replace(',', '.').astype(float)
    return df

# Summarize by month and compare with Coop/month, Uniconta/month
# Run this as a Django management command or script inside the container
```