CIB Strategy: Phase 2

AI-Integrated Credit Management for the Caribbean: Product and Services Strategy

This document outlines the strategic integration of AI into product offerings and consulting services to enhance credit management in the Caribbean. It distinguishes between AI training workshops and AI-driven solutions for businesses and consumers targeting improved customer relationship management, reduced credit risk, and increased customer engagement.

Goals

Business Goals

  1. Launch AI-driven credit management products to secure new client sectors in Trinidad and Barbados.

  2. Establish leadership in AI consultancy and training for enhanced credit risk management.

  3. Develop strategic alliances with key local business influencers and networks.

User Goals

  1. Provide AI-enhanced credit management tools that offer precision, speed, and insight.

  2. Deliver actionable AI workshops and consulting services to improve participants' risk management skills.

  3. Foster trust and reliability in data through improved reporting standards.

Non-Goals

  1. This strategy does not cover AI research and development outside credit management.

  2. Existing legacy credit systems remain out of focus.

 

 

User Stories

User Personas:

  • Business Subscribers

    • As a business owner in Trinidad, I want AI-enhanced analytics to better understand credit trends, reducing default risks.

    • As a subscriber, I want streamlined credit processing with faster decision-making.

  • Consumers

    • As a loan applicant in Barbados, I want a simpler, faster credit assessment to improve loan approval chances.

    • As a customer, I want personalized financial advice using AI insights.

 

 

Functional Requirements

  1. AI Workshop Features (Priority: Medium)

    • Detailed curricula targeting risk management and customer engagement.

    • Incorporation of local market case studies.

  2. AI-Driven Products (Priority: High)

    • Credit Scoring Enhancements:

      • Real-time data integration and scoring updates.

      • Predictive analytics for creditworthiness.

    • Reporting Tools:

      • Enhanced data visualization for business clients.

    • Consumer-Focused Services:

      • Personalized credit advisory based on AI insights.

 

 

User Experience

Entry Point & First-Time User Experience

  • Users begin with a personalized assessment of credit needs and AI consultation.

  • Interactive onboarding tutorials focusing on system navigation.

Core Experience

  • Step 1: Initiating a credit check or workshop enrollment via online portal.

    • Focus on minimal navigation steps, instant data feedback.

  • Step 2: Interacting with AI analytics for customized reports.

    • Data accuracy and visualization.

  • Step 3: Receiving alerts and recommendations.

    • Real-time updates on credit states and personalized ways to improve.

Advanced Features & Edge Cases

  • Handling data anomalies and incomplete user data input.

  • Integration with third-party credit systems and APIs.

UI/UX Highlights

  • Consistent design language with adaptive mobile formats.

  • High accessibility standards ensuring broader reach across client demographics.

 

 

Narrative

Imagine a bustling entrepreneur in Trinidad, navigating a tricky credit landscape. With our AI-driven tools, a once-tedious credit check is now streamlined, offering not just scores, but solutions. Participants from our workshops return empowered, their newfound insights applied, and tangible business growth evident. They become avid advocates, reshaping dialogues around credit management in their networks. Our platform seamlessly combines learning with actionable intelligence, catalyzing change one interaction at a time.

 

 

Success Metrics

User-Centric Metrics

  • Adoption Rate: Percentage of subscriber conversions post-implementation.

  • User Satisfaction: Measured through post-interaction feedback and surveys.

Business Metrics

  • Revenue Growth: Quarterly revenue increase from AI products.

  • Market Expansion: User base increase in target areas.

Technical Metrics

  • System Uptime: Maintaining over 99.9% availability.

  • Data Accuracy: Error rates in automated scoring processes.

Tracking Plan

  • Engagement metrics from workshop participation.

  • Usage patterns of AI-driven product features.

 

 

Technical Considerations

Technical Needs

  • APIs for real-time data analysis and scoring.

  • AI engines for predictive analytics and insights generation.

Integration Points

  • Compatibility with existing credit systems.

  • Partnerships with local data providers for enriched data sets.

Data Storage & Privacy

  • Adherence to local and international data protection standards.

  • Secure cloud infrastructure for user data.

Scalability & Performance

  • Solutions architectured to handle increased user loads smoothly.

  • High performance during peak data access times.

Potential Challenges

  • Challenges with local data integration.

  • Meeting heterogeneous compliance standards across regions.

 

 

Milestones & Sequencing

Project Estimate

  • TBD for initial AI product rollout.

Team Size & Composition

  • Medium Team: 3–5 total people for effective delivery.

Suggested Phases

  1. Phase 1: Market Prep & Initial Workshops 

    • Deliverables: Market analysis, initial workshop schedules.

  2. Phase 2: AI Product Design & Testing 

    • Deliverables: AI tool prototypes, market trials.

  3. Phase 3: Full Scale Deployment & Feedback 

    • Deliverables: Launch, initial user feedback analysis.