If your institution is considering replacing its decision management platform, the RFP is where that process starts. The platform you choose will run your credit, fraud, and customer decisions for years, so the questions you ask up front matter.
Below are 10 questions we recommend every bank and financial institution include in their RFP, along with how Provenir answers each one.
1. Can a single platform support decisioning across the full customer lifecycle?
Yes. Provenir supports origination, account management, collections, and fraud on one platform, with shared data integrations, models, attributes, and governance.
Institutions can implement each use case as its own service while reusing common components, which removes duplicate logic and inconsistent treatment across products and channels. Customers avoid stitching together separate engines for each stage of the lifecycle.
2. Are we locked into a single data provider?
No. Provenir is vendor-neutral by design. The Provenir Data Marketplace provides pre-built, fully managed integrations to more than 150 global and local data providers, covering credit bureaus, fraud, identity, KYC/KYB, open banking, and alternative data.
Institutions can use their existing provider contracts and credentials. New sources are onboarded through a no-code connector framework rather than custom development, so your data strategy is never dictated by your decisioning vendor.
3. Can business users change strategies without depending on IT?
Yes, Business users have the full ability to manger their strategies, within a governed framework. Authorized risk and strategy teams can build, test, and deploy rules, decision trees, scorecards, and champion/challenger strategies through a low-code studio. Every change is controlled by:
- Version control with check-in/check-out, user attribution, and rollback
- Controlled promotion across Development, QA, and Production, with no direct production edits
- Segregation of duties enforced through role-based access
The result is business agility without compromising audit or regulatory control.
4. How do you prove the impact of a policy change before it goes live?
Provenir’s native Simulation replays historical production decisions through proposed strategies using the original inputs and stored third-party responses. Teams can measure the impact on approval rates, risk distribution, and segment performance without re-calling data providers or incurring additional data costs.
Combined with champion/challenger testing in both real-time and batch, institutions can reduce strategy testing cycles from months to weeks.
5. Can the platform handle real-time and high-volume batch decisioning at the same time?
Yes. Provenir runs real-time and batch workloads on separate, independently scaled engines, so a large overnight portfolio run never affects latency-sensitive origination traffic. In production, the platform supports more than 10,000 real-time transactions per second and more than 50 million batch records per hour.
6. Can we deploy our own models, and how are they governed?
Yes. Provenir supports bring-your-own-model deployment for Python, R, PMML, Java, and Excel-based models. Models execute natively within the decision flow and are versioned alongside the data attributes that feed them.
Built-in model monitoring, drift detection, and champion/challenger testing support model risk management. Institutions without dedicated data science teams can also build and retrain models in-platform with Provenir AI.
7. How does the platform support explainability and regulatory compliance?
Every execution produces a complete decision trace capturing inputs, data sources, rules, model outputs, reason codes, and final outcomes. Provenir supports SHAP- and LIME-based local and global explainability and automated adverse action and reason code generation, mapped to the institution’s own taxonomy.
Combined with full change-management logging, this gives institutions audit-ready evidence for regulators, model validators, and fair lending reviews.
8. How is the platform using AI, and how is that AI governed?
Provenir embeds AI with human-in-the-loop controls by design. The platform’s AI assistant lets users query data, interpret decision logic, and automate tasks in natural language.
Provider-agnostic LLM integration (including OpenAI, Anthropic, and privately hosted models via AWS Bedrock) lets institutions choose the right model for each workload without re-platforming. Every AI-assisted decision remains transparent, explainable, and subject to the same governance as any other strategy change.
9. What is your architecture, availability, and security posture?
Provenir is a cloud-native platform built on AWS, fully containerized on Kubernetes (Amazon EKS), with multi-availability-zone deployment, self-healing infrastructure, and zero-downtime upgrades. Key commitments include:
- Availability SLAs of up to 99.99%
- SOC 2 Type II and ISO 27001 certification
- AES-256 encryption at rest with AWS KMS, and TLS/mTLS in transit
- SSO, MFA, and role-based access integrated with enterprise identity providers
10. How long does implementation take, and how do you reduce migration risk?
Provenir’s average implementation timeline is 6 to 12 months. For example, a top-15 U.S. credit card issuer went live on onboarding, account management, and pre-delinquency decisioning in nine months.
Migration risk is managed through parallel testing against the incumbent system, historical replay to prove decision parity, and version-controlled rollback. Institutions can then cut over with confidence rather than relying on a big-bang switch.
Choosing the right partner
Replacing a decision engine is a big decision, and the right platform should hold up to detailed scrutiny. Use these questions with every vendor in your evaluation, and ask each one to show you the answers live, not just describe them. The differences become clear quickly. If your team is starting to scope a replacement, we’re happy to walk through how Provenir handles each of these in a tailored demo, or act as a sounding board on your requirements.
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