The business decision engine: policies in hours, strategies tested in production, every decision explainable.
Kavuka Decision Engine automates decisions — credit, pricing, eligibility, routing, limits — combining policies as code, in-house scores and your own models into strategies that decide in milliseconds, with the explainability that regulators, auditors and customers demand.
- Milliseconds
- per decision
- Hours
- to publish a policy
- Champion/Challenger
- strategy tested in production
- Explainable
- audit trail per decision
The same decision core that already runs inside Kavuka Fraud Prevention, Onboarding and RMS — now exposed as a product, deciding in milliseconds with an explainable trail for every decision.
Your credit policy needs to change today. The IT queue says: next quarter.
The policy stuck in the IT queue
The appetite change that would take hours takes the whole quarter, stuck in a sprint; and the hard-coded rule nobody remembers the reason for keeps deciding in the dark.
Change as a bet in the dark
Without backtesting and champion/challenger, every strategy tweak is a bet: you change it, you pray, and the risk-vs-conversion trade-off is left to intuition.
The automation legal blocked
An unexplainable automated decision becomes a black box — and data-protection law grants the data subject the right to review. A production policy diverging from the approved one is the audit finding waiting to happen.
Cost A policy stuck in IT costs the window: the appetite adjustment that would take hours takes a quarter, and the market does not wait. A rule in the dark costs the learning: without champion/challenger, every change is a bet. And an unexplainable decision costs the veto: automation that legal blocks is worth zero.
From policy to explainable decision, in one pipeline.
- 01
Codify
The business analyst writes the policy in readable rule language — versioned, tested and published with approval. The policy that changes in hours, not sprints.
- 02
Compose
Rules + in-house scores + your models + platform data combined into a decision strategy: approve, deny, escalate, price.
- 03
Test
Backtesting of the new policy against history before publishing; then the challenger against the champion in production, on a controlled sample.
- 04
Decide and explain
Milliseconds per decision and a trail for each one: the rules triggered, the scores used, the data queried — native auditability.
The engine behind every decision
Policies, models, strategies and experimentation in one auditable system — decisions at machine speed, under business control.
Policies as code
Written by analysts, versioned and approved
Models as input
In-house scores and your own models plugged in
Decision strategies
Rules + models + thresholds + data
Champion/Challenger
The new strategy against the current, in production
Explainability and trail
Rules, scores and data behind each decision
Simulation (backtesting)
The new policy against history before publishing
Drift monitoring
Model performance and drift in production
Policy governance
Version, owner, rationale and approval
Who decides with Kavuka Decision Engine
Credit & Limits
The appetite policy that changes in the right window, the counterpart of Credit Score: approval, limit and risk-based pricing, in milliseconds.
Anti-fraud & Onboarding
The strategies of the in-house engines — the same engine that decides in Fraud Prevention and Onboarding, exposed for your flow.
Pricing & Offers
Dynamic pricing that combines rule and model and explains each price — the right offer for the right profile, at scale.
Eligibility & Routing
Benefits, insurance and plans: the consistent decision across channels and analysts, at any high-volume repetitive scale.
The automated decision your reviewers can sign off on
Kavuka Decision Engine was designed for the explainability that data-protection law requires and the governance that audit demands. Compliance is not a report at the end — it is how the engine decides and records.
- Explainability for data-protection law: the data subject's right to review automated decisions is met — human review finds a documented decision, not a black box.
- Policy versioning and approval: the production policy is always the approved one, with a history of who changed what, when and why.
- Per-decision trail: each decision stores the rules triggered, the scores used and the data queried — native auditability.
- Backtesting: the impact of every new policy is known before the risk, with documented simulation.
- Model monitoring: performance and drift tracked in production, so the decision never degrades without an alert.
The policy change that used to wait a quarter for IT now ships in an afternoon — written, simulated and approved by the credit team itself.
We stopped changing strategy in the dark. The challenger runs in production, measures the result, and only then do we switch.
In the last audit, every automated decision came with its reasons. Legal signed off on the automation.
Bring a real policy: we return it codified and simulated on your history.
With the impact measured — the definitive proof, on your scenario and with your volume.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What a decision engine is and how to control it
A decision engine is the system that automates business decisions with control. It articulates four layers: policies — the business rules, written by those who understand the business and versioned as code; models — the scores and classifiers (from Risk Scoring to the client's own models) used as inputs to the decision; strategies — the combination of rules, models and thresholds that actually decides (approve, deny, escalate, price); and experimentation — champion/challenger, where the new strategy is tested against the current one in production, with the result measured before any switch.
The decision engine is the decision-making core that beats inside every engine of a risk platform: anti-fraud decides, onboarding decides, the risk-management system decides. Exposed as a horizontal product, it serves any company that wants to automate its own decisions — credit, pricing, eligibility, routing, limits — at machine speed and with the explainability that regulators, auditors and customers demand. The buyer is the owner of the policy (the CRO, the credit team, pricing), not IT: their autonomy to change the rule without filing a ticket is the central promise.
The difference between automation that scales and automation that turns into a bet lies in the discipline of evolution. Backtesting runs the new policy against history before publishing — the impact known before the risk. Champion/challenger keeps the current strategy deciding most of the flow while the candidate decides a controlled sample, and the results are compared with statistical rigor before any switch. It is evolution by evidence, not opinion: every strategy change stops being a bet in the dark and becomes a hypothesis tested in production. IT governs the platform; the business governs the decision.
Automated decision-making and data-protection law are not opposites: the law grants the data subject the right to review automated decisions, and the engine's trail delivers exactly that — the reason for each decision, with the rules triggered, the scores used and their factors, and the data queried. Human review finds a documented decision, not a black box. Explainability has stopped being a courtesy and become a requirement: it is what separates approved automation from blocked automation. The result is decisions at market speed, evolution without betting, consistency across all channels — and automation your reviewers can sign off on.
What is the difference between a Decision Engine and Risk Scoring?
Risk Scoring produces the number (the tailored score); the Decision Engine orchestrates the decision: the rules, the scores (in-house and the client's), the thresholds and the flow — approve, deny, escalate, price. The score is the input; the decision is the product.
Can the business analyst really change the policy alone?
Yes — policies are written in readable rule language, with the governance cycle built in: version, test, simulation, approval and publishing. IT governs the platform; the business governs the decision.
What is champion/challenger?
It is the controlled test in production: the current strategy (champion) keeps deciding most of the flow while the candidate (challenger) decides a sample — and the results are compared with statistical rigor before any switch. Evolution by evidence.
How does explainability meet data-protection law?
The law grants the data subject the review of automated decisions — and the engine's trail delivers the reason for each one: the rules triggered, the scores and their factors, the data queried. Human review finds a documented decision, not a black box.
Does it work with my own models?
Yes — your models plug in as inputs alongside the in-house scores, with performance and drift monitoring; the strategy combines the best of both without rebuilding anything.
What is policy simulation (backtesting)?
It is running the new policy against the historical decisions before publishing it, to know the impact (approvals, denials, risk, conversion) without exposing the operation to risk. The impact known before the switch.
How long does it take to put a decision live?
Integration is via a documented API, and the platform's scores and data are already natively available as inputs. Once integrated, publishing new policies becomes a matter of hours — done by the business team itself.
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