Card Activation Prediction
“Which new cardholders will activate within 30 days?”
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A real-world example
Which new cardholders will activate within 30 days?
25-35% of newly issued credit cards are never activated (Mercator Advisory Group). Each unactivated card represents $500-$800 in lost annual revenue from interchange, interest, and fees. For a large issuer with 5M new cards per year, that is $625M-$1.4B in unrealized revenue. The activation window is narrow: if a cardholder does not activate within 30-45 days, the probability drops to under 10%. Most issuers send generic reminder emails to all new cardholders, but conversion rates on these campaigns are just 3-5%.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects new cardholders to their application data, existing product relationships, transaction history on other accounts, digital engagement signals, and demographic patterns. The model identifies that Cardholder CH-5501 applied through a branch referral, has 3 existing products, high mobile-app engagement, but has not received the physical card yet (shipping delay). Meanwhile, CH-5520 applied through a generic online ad, has no prior relationship, and has not logged into the app. Kumo scores activation probability so marketing teams can target high-risk-of-dormancy cardholders with incentivized activation 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
NEW_CARDHOLDERS
| cardholder_id | card_type | application_channel | approval_date | existing_products |
|---|---|---|---|---|
| CH-5501 | Rewards Platinum | Branch Referral | 2025-09-01 | 3 |
| CH-5520 | Cash Back | Online Ad | 2025-09-03 | 0 |
| CH-5534 | Travel Elite | Pre-approved Mail | 2025-09-05 | 2 |
CARD_STATUS
| cardholder_id | card_shipped | card_delivered | activated | days_since_approval |
|---|---|---|---|---|
| CH-5501 | True | True | False | 14 |
| CH-5520 | True | True | False | 12 |
| CH-5534 | True | False | False | 10 |
DIGITAL_ENGAGEMENT
| cardholder_id | app_logins_7d | email_opens | push_enabled |
|---|---|---|---|
| CH-5501 | 5 | 2 of 2 | True |
| CH-5520 | 0 | 0 of 2 | False |
| CH-5534 | 3 | 1 of 2 | True |
EXISTING_ACCOUNT_ACTIVITY
| cardholder_id | other_card_txns_30d | checking_balance | direct_deposit |
|---|---|---|---|
| CH-5501 | 42 | $8,200 | Active |
| CH-5520 | N/A | N/A | N/A |
| CH-5534 | 28 | $15,400 | Active |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CARD_STATUS.ACTIVATED = 'True', 0, 30, days) FOR EACH NEW_CARDHOLDERS.CARDHOLDER_ID WHERE CARD_STATUS.ACTIVATED = 'False'
Prediction output
Every entity gets a score, updated continuously
| CARDHOLDER_ID | CARD_TYPE | ACTIVATION_PROB | RISK_OF_DORMANCY | RECOMMENDED_ACTION |
|---|---|---|---|---|
| CH-5501 | Rewards Platinum | 0.89 | Low | Standard Welcome |
| CH-5534 | Travel Elite | 0.64 | Medium | Bonus Offer |
| CH-5520 | Cash Back | 0.18 | Critical | Call + $100 Bonus |
Understand why
Every prediction includes feature attributions — no black boxes
Cardholder CH-5520 (Cash Back card)
Predicted: 18% activation probability (critical dormancy risk)
Top contributing features
No existing banking relationship
0 products
30% attribution
Zero app logins since approval
0 in 12d
26% attribution
Email engagement absent
0 of 2 opened
20% attribution
Application channel (low-intent)
Online Ad
14% attribution
Push notifications disabled
False
10% 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: Target the 30% of new cardholders at highest dormancy risk with personalized activation offers, recovering $200-400M in annual unrealized revenue for a top-10 issuer.
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.
