Banking Customer Churn Prediction
“Which banking customers will close their accounts in the next 90 days?”
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A real-world example
Which banking customers will close their accounts in the next 90 days?
Retail banks lose 10-15% of depositors annually, with each lost household representing $3,000-$8,000 in lifetime revenue. By the time a customer calls to close an account, it is already too late. Most churn models rely on static snapshots of balance and tenure, missing the behavioral signals buried in transaction graphs: reduced direct-deposit frequency, declining debit-card spend, new ACH transfers to competitor accounts, and fading branch or app engagement. A top-20 US bank estimated $150M in annual deposit runoff from customers it could have saved with 30 days of lead time.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects accounts, transactions, customer profiles, product holdings, branch interactions, and digital-channel events into a single relational graph. Instead of hand-engineering 200+ features, you write a two-line PQL query. Kumo's graph neural network learns patterns like declining transaction frequency at merchants where a customer used to spend weekly, new recurring transfers to Ally or Marcus, and reduced mobile-app logins. These cross-table signals surface churn risk 60-90 days before closure, giving retention teams time to intervene with targeted offers.
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
CUSTOMERS
| customer_id | name | segment | tenure_years | region |
|---|---|---|---|---|
| C-10042 | James Whitfield | Mass Affluent | 8.3 | Northeast |
| C-10078 | Maria Gonzalez | Premier | 12.1 | Southeast |
| C-10115 | David Park | Mass Market | 2.7 | West |
ACCOUNTS
| account_id | customer_id | type | balance | opened_date |
|---|---|---|---|---|
| A-50001 | C-10042 | Checking | $45,230 | 2017-04-12 |
| A-50002 | C-10042 | Savings | $112,500 | 2017-04-12 |
| A-50003 | C-10078 | Checking | $28,900 | 2013-01-08 |
TRANSACTIONS
| txn_id | account_id | type | amount | merchant | timestamp |
|---|---|---|---|---|---|
| T-900001 | A-50001 | debit | $127.40 | Whole Foods | 2025-09-01 |
| T-900002 | A-50001 | ACH_out | $5,000 | Marcus Savings | 2025-09-03 |
| T-900003 | A-50003 | direct_dep | $4,200 | Employer | 2025-09-15 |
DIGITAL_EVENTS
| event_id | customer_id | channel | action | timestamp |
|---|---|---|---|---|
| E-001 | C-10042 | mobile_app | login | 2025-09-01 |
| E-002 | C-10042 | mobile_app | view_rates | 2025-09-02 |
| E-003 | C-10078 | web | bill_pay | 2025-09-14 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ACCOUNTS.STATUS = 'closed', 0, 90, days) FOR EACH CUSTOMERS.CUSTOMER_ID WHERE ACCOUNTS.TYPE = 'Checking'
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | NAME | SEGMENT | CHURN_PROB | RISK_TIER |
|---|---|---|---|---|
| C-10042 | James Whitfield | Mass Affluent | 0.87 | Critical |
| C-10078 | Maria Gonzalez | Premier | 0.12 | Low |
| C-10115 | David Park | Mass Market | 0.64 | High |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-10042 (James Whitfield)
Predicted: 87% probability of account closure within 90 days
Top contributing features
New ACH transfers to competitor bank
3 in 30d
34% attribution
Declining debit-card transaction frequency
-62%
22% attribution
Mobile app login decline
-78%
18% attribution
Balance drawdown velocity
-$12K/mo
15% attribution
Rate comparison page views
4 visits
11% 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: Identify at-risk depositors 60-90 days before closure and retain 20-30% with targeted interventions, saving $30-50M in annual deposit runoff for a top-20 bank.
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.
