Branch Demand Forecasting
“How many tellers does each branch need next week?”
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A real-world example
How many tellers does each branch need next week?
US banks operate 70,000+ branches, and labor is the largest controllable cost at 55-65% of branch operating expense (BAI). Overstaffing wastes $15-25K per branch annually in idle teller hours, while understaffing drives average wait times above 8 minutes, directly correlating with a 12% drop in customer satisfaction (J.D. Power). The challenge: branch traffic depends on payroll cycles, local events, weather, nearby business hours, and even competitor branch closures. Static scheduling models based on last year's averages miss these dynamic signals.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects branch profiles, historical foot traffic, transaction volumes, local event calendars, weather data, and regional payroll cycles into a relational graph. The model predicts that Branch S-14 will see 340 transactions next Tuesday because it is the first of the month (payroll deposits), a local employer just switched to bi-weekly pay, and a competitor branch 2 miles away recently closed. These cross-table signals produce staffing forecasts 35% more accurate than static models.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
BRANCHES
| branch_id | name | region | avg_daily_txns | teller_stations |
|---|---|---|---|---|
| BR-014 | Union Square | West | 285 | 6 |
| BR-022 | Midtown | Northeast | 420 | 8 |
| BR-037 | Lakeside | Midwest | 180 | 4 |
DAILY_TRAFFIC
| branch_id | date | transactions | avg_wait_min | tellers_on_duty |
|---|---|---|---|---|
| BR-014 | 2025-09-29 | 310 | 4.2 | 5 |
| BR-014 | 2025-09-30 | 395 | 8.7 | 5 |
| BR-022 | 2025-09-30 | 480 | 6.1 | 7 |
LOCAL_EVENTS
| branch_id | date | event_type | expected_impact |
|---|---|---|---|
| BR-014 | 2025-10-01 | Month Start (Payroll) | High |
| BR-014 | 2025-10-01 | Competitor Branch Closure | Medium |
| BR-022 | 2025-10-03 | Local Festival | Low |
WEATHER_FORECAST
| region | date | condition | temp_f | precipitation |
|---|---|---|---|---|
| West | 2025-10-01 | Sunny | 72 | 0% |
| Northeast | 2025-10-01 | Rain | 58 | 80% |
| Midwest | 2025-10-01 | Cloudy | 65 | 20% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(DAILY_TRAFFIC.TRANSACTIONS, 0, 7, days) FOR EACH BRANCHES.BRANCH_ID
Prediction output
Every entity gets a score, updated continuously
| BRANCH_ID | DATE | PREDICTED_TXNS | TELLERS_NEEDED | VS_SCHEDULED |
|---|---|---|---|---|
| BR-014 | 2025-10-01 | 385 | 7 | +2 |
| BR-014 | 2025-10-02 | 260 | 5 | 0 |
| BR-022 | 2025-10-01 | 350 | 6 | -1 |
Understand why
Every prediction includes feature attributions — no black boxes
Branch BR-014 (Union Square), Oct 1
Predicted: 385 transactions, 7 tellers needed
Top contributing features
Month-start payroll cycle
1st of month
32% attribution
Competitor branch closure spillover
+45 txns est.
24% attribution
Day-of-week pattern (Wednesday)
Above avg
18% attribution
Weather (sunny, high foot traffic)
Sunny 72F
14% attribution
Regional employment trend
+2.1% YoY
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Reduce average wait times by 30% and cut overtime costs by $15-25K per branch annually, translating to $100-175M in savings across a 7,000-branch network.
Related use cases
Explore more financial services use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
