- Solution
- Multi-Armed Bandit
Find the message that works in weeks, not years.
KredosAi’s multi-armed bandit engine tests every approved message and call to action at once, and shifts volume toward what gets each account to pay.
In short: KredosAi’s multi-armed bandit is the decision engine behind every KredosAi message. An A/B test splits volume evenly. The bandit instead shifts volume toward the approved messages and calls to action that work for each type of account, while it keeps testing the rest. It reaches optimized performance in 30 to 60 days.
A pilot built around your portfolio. Early results in as little as 2 weeks.
Last updated: November 3, 2026
One test waits. One starts winning.
Message, tone, call to action and offer multiply into hundreds of thousands of combinations. Testing them two at a time would take years. A champion/challenger test waits months for one winner. A multi-armed bandit starts shifting volume toward what works on day one.
vs
Champion/challenger
Volume moves toward what works as results come in
Volume split evenly, including to losing variants
50+ variables in one experiment
2 to 3 variants at a time
Optimized in 30 to 60 days
18 to 24 months to one winner
Adjusts as customer behavior shifts
Start a new test when behavior shifts
Approved templates only
Approved templates only
A recommendation system for your messages.
It works much like the systems that choose which video or post to show you next: it learns from how each person responds and adjusts what it recommends.
See the engine on your own portfolio.
1. Explore
2. Learn
3. Shift
For your data science team
See the engine on your own portfolio.
Isn’t this old technology?
The idea is old. Running it live is new.
The multi-armed bandit problem has been studied for decades, and Thompson sampling dates back to 1933. What is new is running it continuously on daily account files, at enterprise volume, inside compliance rules. That is what KredosAi built.
- It can’t write a message. It only chooses among templates your legal team approved.
- It can’t send the wrong message at the wrong time. Scenario and sequence rules are deterministic.
- Every send clears a 5-gate check: template compliance, send timing, regulatory citation, quiet hours and channel match.
Related reading: How KredosAi applies the multi-armed bandit and Multi-armed bandit is an efficient way to run experiments in collections.
What continuous testing produces.
Optimized in 30 to 60 days, not 18 to 24 months.
$100M+ annual EBITDA improvement in Year 1 at one Tier 1 telco.
16% improvement in cure rate.
Payments land 3 to 6 days sooner, on average.
20x documented ROI at Fortune 50 scale.
Questions teams ask first.
What is a multi-armed bandit?
A multi-armed bandit is a method for choosing among several options when you don’t yet know which works best, by learning from results as they arrive. The name comes from a gambler choosing between slot machines (“one-armed bandits”) with unknown payouts. An A/B or champion/challenger test splits volume evenly and waits months for a winner. A bandit shifts volume toward options that perform well while it keeps testing the others, in case the early leader is wrong. KredosAi uses a contextual bandit: it scores approved messages and calls to action against each account’s characteristics and allocates volume with Thompson sampling. In collections, this lets KredosAi test 50+ variables at once and reach optimized performance in 30 to 60 days, versus 18 to 24 months of A/B testing.
How is it different from A/B or champion/challenger testing?
An A/B test splits volume evenly and changes nothing until it reaches a conclusion. A bandit shifts volume as results come in, so fewer accounts get a weaker message while the test runs.
Do we have to stop running champion/challenger tests?
No. Many teams keep a classic test where they need a clean before-and-after record, and use the bandit to optimize among options that are already approved.
Can the engine write its own messages?
No. It only chooses among templates your legal team has approved, within your scenario and sequence rules.
What is a multi-armed bandit?
A multi-armed bandit is a method for choosing among several options when you don’t yet know which works best, by learning from results as they arrive. The name comes from a gambler choosing between slot machines (“one-armed bandits”) with unknown payouts. An A/B or champion/challenger test splits volume evenly and waits months for a winner. A bandit shifts volume toward options that perform well while it keeps testing the others, in case the early leader is wrong. KredosAi uses a contextual bandit: it scores approved messages and calls to action against each account’s characteristics and allocates volume with Thompson sampling. In collections, this lets KredosAi test 50+ variables at once and reach optimized performance in 30 to 60 days, versus 18 to 24 months of A/B testing.
Do we have to stop running champion/challenger tests?
No. Many teams keep a classic test where they need a clean before-and-after record, and use the bandit to optimize among options that are already approved.
How is it different from A/B or champion/challenger testing?
An A/B test splits volume evenly and changes nothing until it reaches a conclusion. A bandit shifts volume as results come in, so fewer accounts get a weaker message while the test runs.
Can the engine write its own messages?
No. It only chooses among templates your legal team has approved, within your scenario and sequence rules.