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Where are you on your AI Journey?

Pick the stage that best matches where you are in your AI journey.
We’ll show you the right content, tools, and use cases to help you move forward.

Crawl

AI is on your radar-and you’re ready to begin.

Explore

Walk

AI is starting to support decisioning.

Explore

Run

AI is driving real-time, strategic decisions.

Explore

Not Sure?

Take the AI Maturity Assessment to Find Out

AI Maturity Assessment

1 / 14

Category: STRATEGIC ALIGNMENT & GOVERNANCE

How clearly defined and aligned is your organization’s AI decisioning strategy?

A) We don’t have a formal strategy—initiatives are ad hoc

B) Strategy exists but varies across teams/functions

C) Strategy is defined and aligned across teams, data, and tech

2 / 14

Category: STRATEGIC ALIGNMENT & GOVERNANCE

How do you ensure decisions are ethical, fair, and compliant with regulations?

A) Basic compliance reviews with limited transparency

B) Some explainability and bias testing

C) Governance and explainability are built in

3 / 14

Category: STRATEGIC ALIGNMENT & GOVERNANCE

How integrated is AI decisioning within your credit and fraud teams?

A) AI is not actively used or limited to isolated pilots

B) AI is used in some processes but not fully embedded

C) AI decisioning informs real-time decisions across workflows

4 / 14

Category: DATA READINESS & INFRASTRUCTURE

What types of data are used in your decisioning process?

A) Primarily structured internal data

B) Structured + third-party data

C) Broad mix of structured, unstructured, and alternative data

5 / 14

Category: DATA READINESS & INFRASTRUCTURE

How would you describe your approach to data preprocessing and quality management before decisioning?

A) We use raw data as-is, with minimal review

B) Some data cleaning and validation, but not standardized

C) Automated pipelines and validation ensure consistent, high-quality data

6 / 14

Category: AI & ANALYTICS CAPABILITIES

What kind of analytics drive your decisioning today?

A) Descriptive analytics and business rules

B) Predictive analytics to estimate outcomes

C) Predictive + prescriptive analytics working together

7 / 14

Category: AI & ANALYTICS CAPABILITIES

How are your models and decisioning strategies updated over time?

A) Rules and models are reviewed manually

B) Predictive models are retrained periodically

C) Models are self-learning and continuously optimized

8 / 14

Category: AI & ANALYTICS CAPABILITIES

How quickly can your system adapt to new data, threats, or changes in customer behavior?

A) Requires manual review and intervention

B) Predictive models adjust within hours or minutes

C) Prescriptive decisioning adapts in real time

9 / 14

Category: LIFECYCLE APPLICATION OF AI

How well is AI decisioning applied across the customer lifecycle (from onboarding to collections)?

A) AI is used in isolated stages

B) AI is applied in several lifecycle stages, but in silos

C) AI decisioning is integrated across the full lifecycle

10 / 14

Category: LIFECYCLE APPLICATION OF AI

How is AI used to inform strategic decisions (e.g., product design, pricing, credit policy)?

A) Strategic decisions are based on historical reporting

B) Predictive models support analysis but aren’t fully operationalized

C) AI-driven insights actively shape strategic decisions

11 / 14

Category: LIFECYCLE APPLICATION OF AI

How is AI used to manage and optimize your customer or credit portfolio?

A) Portfolio management is reactive, based on lagging indicators

B) Predictive analytics are used for monitoring. Optimization is manual

C) AI continuously monitors and prescribes optimization strategies

12 / 14

Category: USE CASE EXECUTION ACROSS LIFECYCLE

How is AI used during onboarding?

A) Onboarding relies on manual reviews and static rules

B) AI supports risk assessment or document checks in some cases

C) AI dynamically scores risk and flags potential fraud in real time during onboarding

13 / 14

Category: USE CASE EXECUTION ACROSS LIFECYCLE

How is AI used to detect and prevent application fraud?

A) Detection is based on rules and historical patterns

B) Predictive models help score risk, but rules still dominate action

C) AI proactively identifies and blocks fraudulent applications in real time

14 / 14

Category: USE CASE EXECUTION ACROSS LIFECYCLE

How is AI used in customer management and collections?

A) Strategies are reactive and follow static rules or risk bands

B) Predictive models help with prioritization, but actions are predefined

C) AI dynamically adjusts treatments based on behavior and risk

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Where are you on your AI Journey?

Not Sure?

AI Maturity Assessment

How clearly defined and aligned is your organization’s AI decisioning strategy?

How do you ensure decisions are ethical, fair, and compliant with regulations?

How integrated is AI decisioning within your credit and fraud teams?

What types of data are used in your decisioning process?

How would you describe your approach to data preprocessing and quality management before decisioning?

What kind of analytics drive your decisioning today?

How are your models and decisioning strategies updated over time?

How quickly can your system adapt to new data, threats, or changes in customer behavior?

How well is AI decisioning applied across the customer lifecycle (from onboarding to collections)?

How is AI used to inform strategic decisions (e.g., product design, pricing, credit policy)?

How is AI used to manage and optimize your customer or credit portfolio?

How is AI used during onboarding?

How is AI used to detect and prevent application fraud?

How is AI used in customer management and collections?

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The Power of
Provenir AI