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AI is driving real-time, strategic decisions.

AI is fully embedded across onboarding, fraud, customer management, and collections—enabling simulation of scenarios, continuous optimization, and adaptive decisions at scale. You’re using AI not just to automate, but to learn, test, and evolve strategies in real time.

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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

AI Assessment

Take the AI Maturity Assessment Quiz

1.

Explore Scaling Strategies

Learn how top financial institutions are scaling AI from isolated wins to enterprise-wide decisioning, using simulation, shared KPIs, and continuous optimization to drive better outcomes across the customer lifecycle.

2.

Global Expansion: Know the Rules

Australia

AI Compliance: No AI Act yet; legislation is under discussion.
Data Protection: No specific notes on AI-related data protection outlined.

Brazil

AI Compliance: AI Bill proposed; approved by the Senate, pending Chamber of Deputies review and potential amendments.
Data Protection: Strong rights to review automated decisions, data access, correction, erasure, portability, objection, anonymization, and lodge complaints.

Canada

AI Compliance: AIDA within Bill C-27 proposed to regulate safe, fair AI use, focusing on business accountability and high-impact systems.
Data Protection: No specific AI data protection rights outlined separately from general frameworks.

Chile

AI Compliance: No AI Act; proposals at early development stage.
Data Protection: Early-stage proposals for rights concerning AI, automated decisions, and profiling.

Colombia

AI Compliance: Early-stage proposals for AI regulation.
Data Protection: Qualified rights against automated decision-making, transparency requirements, and the right to human review.

European Union

AI Compliance: The AI Act is in force (since August 2024); risk-based classification and strict obligations begin in 2025.
Data Protection: Profiling and automated decision-making contribute to system classification and obligations under the AI Act.

India

AI Compliance: No specific AI legislation; Digital India Act draft expected to address AI and privacy.
Data Protection: No current specific regulations on automated decision-making or profiling.

Indonesia

AI Compliance: Ethical guidelines for AI issued (non-binding, 2023).
Data Protection: Draft Bill regarding AI, profiling, and automated decision-making in progress.

Malaysia

AI Compliance: National AI Governance and Ethics Guidelines published (Sept 2024) – non-legally binding.
Data Protection: Guidance and principles available, but no overarching law for AI-related data processing.

New Zealand

AI Compliance: Algorithm Charter for government agencies; additional OPC AI guidance for businesses.
Data Protection: Guidance on data privacy and AI issued; no specific AI data protection law yet.

Singapore

AI Compliance: No AI Act; guidance issued for AI-based personal data usage.
Data Protection: Non-binding principles and guidance provided.

Thailand

AI Compliance: Two AI-related draft legislations introduced (still under development).
Data Protection: Draft regulations regarding profiling and automated decisions in progress.

Philippines

AI Compliance: Advisory No. 2024-04 published; draft bills pending, including AI Regulation Act.
Data Protection: Strict consent and transparency requirements; qualified rights against automated decisions, and human review rights.

United States (USA)

AI Compliance: No comprehensive federal AI regulation yet; regulatory initiatives developing.
Data Protection: Varies by state; consult compliance team for specific local laws.

United Kingdom (UK)

AI Compliance: AI legislation expected in 2025.
Data Protection: Qualified rights against automated decision-making, with specific exemptions; rights to transparency and human review.

Vietnam

AI Compliance: AI standards and guidance issued; Draft DTI Law focuses on AI classification and ethical principles.
Data Protection: Consumer Protection Law requires periodic assessments of AI systems; voluntary national AI standards introduced.

Activate Full Lifecycle Decisioning at Scale

Pull the right AI levers—like segmentation, thresholds, and decision paths—to reduce friction, boost performance, and tailor every customer interaction across the lifecycle.

Intelligence

Improved predictions, segmentation, insights

Growth

Revenue uplift, conversion, customer acquisition

Efficiency

Operational speed, automation, cost reduction

Risk

Fraud prevention, credit risk, compliance

Trust

Customer experience, transparency, satisfaction

Onboarding
Application Fraud
Customer Management
Collections

Lever	Outcomes	When to Use It
Personalized Data Sources	Use alt-data (open banking, telco, mobile) Improve decision coverage Reduce onboarding friction	When targeting underserved or thin-file applicants
Tailor Risk Thresholds	Approve more good customers Minimize false declines Match risk to product intent	When rejection rates are high or growth is stalling
Custom Decision Paths	Fast-lane low-risk applicants Route higher-risk profiles Reduce onboarding SLAs	When onboarding delays impact CX or ops load

Lever	Outcomes	When to Use It
Behavioral Pattern Detection at Application	Spot bots, form manipulation, and scripting Detect synthetic IDs based on device or usage patterns Reduce friction for legitimate customers	When identity appears valid but behavior is suspicious
Intent Scoring Through Multi-Signal Fusion	Flag high-risk applicants with ‘no intent to repay’ Identify bust-outs and first-party fraud early Improve decision confidence for thin-file profiles	When first-party fraud risk is rising or credit files are thin
Adaptive Model and Rule Optimization	Improve detection accuracy over time Reduce manual rule tuning and false positives Adapt to new fraud tactics and promo abuse	When fraud patterns are shifting or analyst workload is high

Lever	Outcomes	When to Use It
Dynamic Limits or Pricing	Personalized credit or pricing Reward loyalty Boost engagement and retention	When optimizing CLTV or running loyalty incentives
AI-Driven Recommendations	Suggest upsells, cross-sells, next best product Align offers to behavior Improve portfolio ROI	When targeting wallet share or lifecycle value
Behavior-Based Segmentation	Tailor treatments based on usage Improve personalization Elevate relevance of outreach	When generic journeys limit ROI or engagement

Lever	Outcomes	When to Use It
Segment by Repayment Behavior	Tailor contact strategy Improve engagement and recovery Minimize losses	When collections feels ‘one-size-fits-all’
Context-Aware Messaging	Deliver empathetic, relevant nudges Preserve trust and self-resolution Reduce escalations	When tone and approach are harming brand or causing churn
Custom Decision Paths	Automate low-risk workflows Prioritize high-risk escalations Optimize collector workload	When collections ops are overburdened or not scalable

Run

AI is driving real-time, strategic decisions.

Book a Meeting to Discuss AI

Book An Intro Call

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?

1.

2.

Australia

Brazil

Canada

Chile

Colombia

European Union

India

Indonesia

Malaysia

New Zealand

Singapore

Thailand

Philippines

United States (USA)

United Kingdom (UK)

Vietnam

Activate Full Lifecycle Decisioning at Scale

Intelligence

Growth

Efficiency

Risk

Trust