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Smart Financial Review Formula

This document is the implementation reference for the Home > Smart Financial Review feature in the Kesles Merchant app.

Active implementation references:

  • smart_financial_review_form_page.dart
  • smart_financial_review_models.dart
  • smart_financial_review_result_page.dart
  • smart_financial_advanced_analysis_page.dart
  • smart_financial_growth_simulation_page.dart

Feature Purpose​

Smart Financial Review is not a final net-profit calculator. The feature is used to:

  • read the daily transaction scale of a business
  • benchmark the business position against similar businesses
  • give a quick health signal of the business
  • give an early growth simulation
  • flag inputs that are too extreme so they can be re-validated

Input​

The feature currently uses these inputs:

  • Transactions per day
  • Average transaction amount
  • Business scale
  • Business category
  • Business type
  • Business readiness checklist

Master data status notes:

  • Business Scale (Micro, Small, Medium) is already recorded as master data in db_reference
  • Business Scale is now an active input in the Smart Financial Review page
  • the active implementation currently uses Business Scale, Business Category, and Business Type as the primary benchmark input

Current readiness checklist:

  • has organised transaction recording
  • already accepts digital payments
  • revenue is relatively stable
  • has regular customers
  • has tracked business cash flow

Source of Business Categories​

The current business catalog is derived from the UMKM sectors in the Bank Indonesia document Profil Bisnis UMKM, then filtered to only the business types that realistically use a QRIS Plus device at a cashier, payment desk, or counter.

Master references currently prepared in db_reference:

  • ref_business_scale
  • ref_business_category
  • ref_business_type

Business Scale​

Currently active options:

  • Micro
  • Small
  • Medium

The scale is used to adjust the base benchmark from Business Type.

Business scale guidance used in the current UI copy:

  • Micro
    • business capital: up to Rp1,000,000,000
    • monthly revenue: up to about Rp166,666,667
    • employee count: 1 - 4
  • Small
    • business capital: Rp1,000,000,001 - Rp5,000,000,000
    • monthly revenue: about Rp166,666,668 - Rp1,250,000,000
    • employee count: 5 - 19
  • Medium
    • business capital: Rp5,000,000,001 - Rp10,000,000,000
    • monthly revenue: about Rp1,250,000,001 - Rp4,166,666,667
    • employee count: 20 - 99

Notes:

  • this capital, monthly revenue, and employee count guidance is used as business context and UI helper text
  • the Smart Financial benchmark is still computed from the combination of Business Scale + Business Category + Business Type
  • business capital figures follow PP No. 7/2021 criteria
  • monthly revenue is derived from the official annual sales result thresholds, then rounded to the nearest rupiah
  • employee count follows the BPS Sensus Ekonomi 2016 classification: micro 1-4, small 5-19, medium 20-99
  • the article UMKM Naik Kelas: Strategi, Tantangan, dan Solusi Digital yang Nyata is used only as narrative context, not as the primary cutoff numbers

BI sectors used as the basis:

  • Trade
  • Manufacturing
  • Services

Important notes:

  • the BI document is the sectoral reference
  • the selection of business types in the app is the result of Kesles Merchant product filtering
  • sectors such as agriculture, plantation, livestock, and fisheries are not included in this version because they do not always fit the QRIS Plus device usage pattern at a direct payment point

Categories and Benchmarks​

Each Business type has its own benchmark:

  • daily transaction benchmark
  • ticket size benchmark
  • base operational cost rate

1. Trade Category​

  • Grocery / Essentials Store

    • transaction benchmark: 150
    • ticket size benchmark: Rp 32,000
    • base cost rate: 13%

  • Minimarket / Convenience Store

    • transaction benchmark: 190
    • ticket size benchmark: Rp 43,000
    • base cost rate: 12%

  • Clothing & Accessories Store

    • transaction benchmark: 60
    • ticket size benchmark: Rp 175,000
    • base cost rate: 15%

  • Pharmacy / Drug Store

    • transaction benchmark: 80
    • ticket size benchmark: Rp 95,000
    • base cost rate: 14%

  • Cosmetics & Personal Care Store

    • transaction benchmark: 55
    • ticket size benchmark: Rp 135,000
    • base cost rate: 15%

  • Electronics & Gadget Accessories Store

    • transaction benchmark: 22
    • ticket size benchmark: Rp 850,000
    • base cost rate: 11%

  • Bookstore / Stationery / Photocopy

    • transaction benchmark: 70
    • ticket size benchmark: Rp 48,000
    • base cost rate: 14%

2. Manufacturing Category​

  • Coffee Shop

    • transaction benchmark: 110
    • ticket size benchmark: Rp 38,000
    • base cost rate: 24%

  • Restaurant / Eatery

    • transaction benchmark: 145
    • ticket size benchmark: Rp 52,000
    • base cost rate: 26%

  • Bakery

    • transaction benchmark: 95
    • ticket size benchmark: Rp 47,000
    • base cost rate: 23%

  • Modern Drinks / Juice / Tea

    • transaction benchmark: 140
    • ticket size benchmark: Rp 28,000
    • base cost rate: 24%

  • Snack Stall / Street Food

    • transaction benchmark: 130
    • ticket size benchmark: Rp 23,000
    • base cost rate: 27%

  • Cake Shop / Dessert

    • transaction benchmark: 68
    • ticket size benchmark: Rp 92,000
    • base cost rate: 23%

  • Catering / Daily Orders

    • transaction benchmark: 20
    • ticket size benchmark: Rp 430,000
    • base cost rate: 20%

3. Services Category​

  • Laundry by Weight

    • transaction benchmark: 48
    • ticket size benchmark: Rp 32,000
    • base cost rate: 16%

  • Salon / Barbershop

    • transaction benchmark: 38
    • ticket size benchmark: Rp 78,000
    • base cost rate: 17%

  • Motorbike / Car Workshop

    • transaction benchmark: 26
    • ticket size benchmark: Rp 185,000
    • base cost rate: 18%

  • Motorbike / Car Wash

    • transaction benchmark: 44
    • ticket size benchmark: Rp 56,000
    • base cost rate: 16%

  • Shipping / Parcel Agent

    • transaction benchmark: 72
    • ticket size benchmark: Rp 30,000
    • base cost rate: 15%

  • Phone Credit Counter / PPOB

    • transaction benchmark: 88
    • ticket size benchmark: Rp 27,000
    • base cost rate: 12%

  • Gadget / Electronics Repair

    • transaction benchmark: 18
    • ticket size benchmark: Rp 245,000
    • base cost rate: 17%

  • Photo Studio / Printing

    • transaction benchmark: 24
    • ticket size benchmark: Rp 115,000
    • base cost rate: 15%

Main Formulas​

1. Benchmark after business scale adjustment​

Current implementation:

benchmark_transactions = base_benchmark_transactions x scale_transaction_factor
benchmark_ticket_size = base_benchmark_ticket_size x scale_ticket_size_factor

Active scale factors:

  • Micro

    • transaction_factor = 0.60
    • ticket_size_factor = 0.78
    • operational_cost_rate_adjustment = +2.0%

  • Small

    • transaction_factor = 1.00
    • ticket_size_factor = 1.00
    • operational_cost_rate_adjustment = 0%

  • Medium

    • transaction_factor = 1.85
    • ticket_size_factor = 1.28
    • operational_cost_rate_adjustment = -1.5%

2. Daily revenue​

daily_revenue = daily_transactions x average_transaction_amount

3. Business readiness ratio​

readiness_ratio = checked_checklist_count / total_checklist_count

Notes:

  • if total_checklist_count <= 0, the result is forced to 0

4. Daily revenue benchmark​

benchmark_daily_revenue = benchmark_transactions x benchmark_ticket_size

5. Ratios against benchmark​

volume_ratio = daily_transactions / benchmark_transactions
ticket_size_ratio = average_transaction_amount / benchmark_ticket_size
daily_revenue_ratio = daily_revenue / benchmark_daily_revenue

6. Operational cost estimate​

Current implementation:

operational_cost_rate =
  base_operational_cost_rate
  + scale_operational_cost_rate_adjustment
  - (readiness_ratio x 0.05)
  + scaled_bonus(volume_ratio)
  + scaled_bonus(ticket_size_ratio)

The final result is then bounded:

operational_cost_rate = clamp(10%, 32%)

Business meaning:

  • the more operationally ready, the relatively more efficient the cost can be
  • the higher the transaction scale and ticket size, the more the operational cost may also rise

7. Nominal operational cost​

operational_cost = round(daily_revenue x operational_cost_rate)

8. Estimated net received​

estimated_net_revenue = max(0, daily_revenue - operational_cost)

Score Formulas​

1. Volume score normalisation​

normalized_volume_score = normalized_score_from_ratio(volume_ratio)

2. Ticket size score normalisation​

normalized_ticket_score = normalized_score_from_ratio(ticket_size_ratio)

3. Revenue score normalisation​

normalized_revenue_score = normalized_score_from_ratio(daily_revenue_ratio)

Current normalisation function:

  • if ratio <= 0, result 0
  • if ratio 0 - 1, result follows the ratio
  • if ratio > 1, result is given a bounded logarithmic bonus so the score keeps rising but does not explode

Anomaly Penalty​

This feature applies a penalty so that extreme inputs are not always treated as very healthy.

High anomaly​

Enters high anomaly if any of the following is true:

  • ticket_size_ratio > 8
  • volume_ratio > 10
  • daily_revenue_ratio > 80

Extreme anomaly​

Enters extreme anomaly if any of the following is true:

  • ticket_size_ratio > 25
  • volume_ratio > 40
  • daily_revenue_ratio > 400

Penalty formula​

The penalty is built from three components:

ticket_penalty
volume_penalty
revenue_penalty

After summation:

anomaly_penalty = clamp(0, 0.80)

Overall Score​

Current implementation:

overall_score =
  (normalized_volume_score x 0.30)
  + (normalized_ticket_score x 0.25)
  + (normalized_revenue_score x 0.25)
  + (readiness_ratio x 0.20)
  - anomaly_penalty

The result is then bounded:

overall_score = clamp(0, 1.25)

Status Criteria​

Status evaluation order:

  • if hasExtremeAnomaly
    • label: Data Needs Validation
  • if hasHighAnomaly
    • label: Scale Above Benchmark
  • if overall_score >= 1.05
    • label: Very Strong
  • if overall_score >= 0.95
    • label: Healthy & Stable
  • if overall_score >= 0.75
    • label: Fairly Stable
  • if overall_score >= 0.55
    • label: Ready to Grow
  • otherwise
    • label: Needs Attention

Growth Potential Simulation​

1. Suggested additional transactions​

If extreme anomaly:

suggested_extra_transactions =
  max(10, min(1% x daily_transactions, 100))

If high anomaly:

suggested_extra_transactions =
  max(10, min(3% x daily_transactions, 150))

If benchmark not yet reached:

suggested_extra_transactions =
  benchmark_transactions - daily_transactions

If already above benchmark:

suggested_extra_transactions =
  max(5, min(12% x daily_transactions, 75))

2. Revenue potential​

potential_daily_revenue =
  (daily_transactions + suggested_extra_transactions) x average_transaction_amount

3. Net received potential​

potential_net_revenue =
  max(0, potential_daily_revenue - round(potential_daily_revenue x operational_cost_rate))

4. Growth percentage​

growth_potential_percentage =
  ((potential_net_revenue - estimated_net_revenue) / estimated_net_revenue) x 100

Notes:

  • if estimated_net_revenue <= 0, the growth percentage is forced to 0

Calculation Examples​

Example 1​

Input:

  • category: Trade
  • business type: Grocery / Essentials Store
  • transactions per day: 100
  • average transaction: Rp 100,000
  • checklist met: 4/5

Benchmark:

  • transaction benchmark: 150
  • ticket size benchmark: Rp 32,000
  • base cost rate: 13%

Basic results:

daily_revenue = 100 x 100.000 = 10.000.000
readiness_ratio = 4 / 5 = 0,80
volume_ratio = 100 / 150 = 0,67
ticket_size_ratio = 100.000 / 32.000 = 3,13
benchmark_daily_revenue = 150 x 32.000 = 4.800.000
daily_revenue_ratio = 10.000.000 / 4.800.000 = 2,08

Interpretation:

  • daily revenue is above the grocery store benchmark
  • ticket size is also far above the grocery store benchmark
  • not yet automatically considered an anomaly because still below the high anomaly threshold

Example 2​

Input:

  • category: Trade
  • business type: Grocery / Essentials Store
  • transactions per day: 1,000
  • average transaction: Rp 1,000,000

Interpretation:

  • volume_ratio and ticket_size_ratio jump very high
  • result enters at minimum Scale Above Benchmark
  • if the ratio crosses extreme thresholds, the result becomes Data Needs Validation

Example 3​

Input:

  • category: Trade
  • business type: Grocery / Essentials Store
  • transactions per day: 100,000
  • average transaction: Rp 100,000,000

Interpretation:

  • volume, ticket size, and revenue will far exceed validation thresholds
  • the result is forced to Data Needs Validation
  • the goal of this logic is to prevent the score from looking too healthy when the input is likely to have an extra zero or unit error

Model Design Principles​

The current model is intentionally:

  • more sensitive to differences across business types
  • not equating a grocery store with a coffee shop or a workshop
  • still leaves room for businesses that genuinely grow above benchmark
  • but applies a penalty for inputs that are too extreme

As a result:

  • 100 transactions x Rp 100,000
  • 100 transactions x Rp 1,000,000
  • 1,000 transactions x Rp 100,000
  • 100,000 transactions x Rp 1,000,000

will not fall into the same status.

Limitations of the Current Version​

This model does not yet include:

  • MDR
  • tax
  • employee salary
  • rent
  • utilities
  • detailed raw material cost
  • discount and promo
  • returns
  • daily device service fee
  • seasonal cost

So:

  • estimated_net_revenue is an early operational estimate
  • not the final accounting net profit

If the feature is to become more precise, the next version should add:

  • MDR
  • rent / utilities cost
  • salary
  • raw material cost
  • discount
  • returns
  • operational days per month
  • device / service cost

This way, the analysis output can grow into:

  • daily revenue
  • daily operating profit
  • daily net profit
  • monthly projection
  • cost efficiency against a similar-business benchmark