Decisions that compound
Simulate credit policy cut-offs against approval, default and expected-loss outcomes, set risk-based prices, and push the winning strategy live through champion–challenger testing — without re-keying rules between teams.
NewGoverned AI agents for financial institutions
The AI decision layer for new-world finance
Nubazo unifies data, machine learning, BI, pricing, credit policy, next-best-action, campaigns and governed AI agents in one suite — so banks, NBFCs and fintechs can turn every signal into a decision, and every decision into a conversion.
- Nine integrated modules
- Governance built in
- Built for banks, NBFCs & fintechs
N-PolicyOp · try it
Illustrative data
Find the credit policy that pays
Applicants approved94%
Expected default rate10.7%
Portfolio profit vs. best16%
← Approve moreDrag to simulate before go-liveApprove fewer →
Too loose. Approving almost everyone lets defaults eat the margin. Raise the cut-off.
Works with the stack you already run
Databricks
MLflow
Amazon S3
Amazon EKS
Amazon CloudWatch
AWS Secrets Manager
MongoDB
Python
FastAPI- REST APIs
- Webhooks
- SFTP batch
- Event streams
Why Nubazo
Your best decisions shouldn’t be stuck between disconnected tools
Data lives in one system, models in another, policies in spreadsheets and campaigns in a separate marketing stack. Nubazo connects the whole chain — the model you build is the policy you simulate, the price you set, the action you recommend and the result you measure.
Agents with guardrails
AI agents use scoped tools and step limits, and route outputs to a human approver.
- Pull portfolio data
- Draft insight summary
- Awaiting risk approval
One platform, not ten
Data, ML, BI, decisioning, engagement and agents share one data foundation and one governance layer — so nothing has to be rebuilt as you expand.
Governed by design, provable by default
Every model, policy, price, campaign and agent carries versions, maker–checker approvals and an exportable audit trail, and holdouts keep simulated benefits apart from realised impact.
The full suite
Nine modules. Three layers. One governed platform.
Start with the module that solves today’s priority and expand across the suite as you grow — every module plugs into the same data, models and controls.
ACategory A
Orchestration & Automation
BCategory B
Business Decision Intelligence
CCategory C
Data, ML & Analytics Foundation
9Integrated modules
3Capability layers
9Built-in enterprise roles
1Shared governance layer
How it works
From raw data to measurable conversions
One continuous, governed loop — each step feeds the next, and every outcome feeds back into better models and decisions.
Unify
Catalog sources, define data models and publish governed features.
Model
Build, validate, deploy and monitor ML models with full lineage.
Decide
Optimise credit policy, pricing and next-best-actions within risk appetite.
Engage
Orchestrate multi-channel journeys and automate work with governed agents.
Measure
Track KPIs, uplift and ROI against baselines and holdouts.
Solutions
Built for how financial institutions actually work
Designed around retail and MSME lending, deposits, cards and collections — and around the controls your risk, compliance and audit teams expect.
Banks
Grow wallet share across your existing base, optimise risk-based pricing and govern a large model estate end to end.
Solutions for banks
NBFCs
Scale underwriting with sharper credit policy, smarter pricing and collections intelligence across diverse segments.
Solutions for NBFCs
Fintechs
Launch data-driven products quickly with ML, decisioning and engagement that are enterprise-ready from day one.
Solutions for fintechs
Credit risk & decisioning
Optimise approvals, pricing and losses — together
Simulate policy cut-offs against approval rates, default rates and expected credit loss, then set risk-based prices that balance volume and profitability.
- Approval-rate and default-rate simulation before go-live
- Expected credit loss and risk-adjusted profitability views
- Champion–challenger testing for policies and pricing
- Maker–checker approvals with complete audit history
Credit risk optimisation workflow
- 1N-DataModelsGoverned loan data
- 2N-e2eMLValidated PD model
- 3N-PolicyOpPolicy simulation
- 4N-PriceOpRisk-based pricing
- 5N-ActionsRecommended action
- 6N-e2eBIRisk-adjusted profit
Marketing & engagement
Reach the right customer with the right offer
Score propensity, rank next-best-offers and orchestrate journeys across SMS, email, WhatsApp, app, branch and relationship managers — with test and control groups built in.
- Propensity-driven audiences and next-best-offers
- Multi-channel journeys with A/B testing
- Consent-aware eligibility and contact rules
- Incremental conversion and marketing ROI
Marketing optimisation workflow
- 1N-DataModelsCustomer data
- 2N-e2eMLCross-sell propensity
- 3N-ActionsNext-best-offer
- 4N-CampOrchTest & control campaign
- 5N-e2eBIIncremental contribution
Trust & governance
Enterprise controls your risk and audit teams will recognise
Governance isn’t a bolt-on. It is the shared layer every module runs on.
Tenant isolation
Each institution’s users, data, models, strategies and records are logically separated.
Role-based access
Nine enterprise roles, from data scientist to auditor, with least-privilege permissions.
Auditable lifecycle
Versioning, maker–checker approvals, evidence and tamper-evident audit logs.
Responsible AI
Agents run with guardrails, scoped tools and human-in-the-loop approval.
FAQ
Frequently asked questions
Do we need to adopt all nine modules at once?
No. Most institutions start with one or two modules aligned to a priority — for example credit policy optimisation or cross-sell — and expand. Every module shares the same data foundation and governance layer, so nothing has to be rebuilt later.
Who is Nubazo built for?
Banks, NBFCs, housing finance companies, fintechs and other financial institutions — and the teams inside them: data science, credit risk, marketing, model validation, compliance, audit and business leadership.
How does Nubazo handle governance and model risk?
Every model, policy, pricing strategy, campaign and AI agent has a versioned, auditable lifecycle with maker–checker approvals, validation checklists, documentation and exportable audit records. Nubazo is designed to support your model-risk and compliance processes; it does not replace your institution’s own approvals.
Can it work with our existing data and ML tools?
Yes. Nubazo integrates through connectors and REST APIs and works alongside tools such as Databricks and MLflow, with storage and deployment options on AWS.
How are AI agents kept under control?
Agents use approved, scoped tools, run within step limits, redact sensitive data and route their outputs for human approval by a designated role. Every action is logged.
How do we measure the business value?
Define baseline KPIs, run champion–challenger and holdout experiments, and track incremental revenue, cost savings and risk-adjusted profit — with simulated and realised benefits always reported separately.
Ready to turn intelligence into conversions?
Book a tailored walkthrough of the suite for your institution’s portfolio, products and priorities.