1. Which NBAs best address my current sales objective?

Current Urgent Pain Points AI-Driven Hackathon Solution
Advisors struggle to find the most relevant NBA and must navigate static campaign catalogs. AI Goal-to-NBA Copilot: Users enter a sales objective ("Increase mortgage sales"), and AI generates ranked NBAs with expected impact.
Too many irrelevant recommendations reduce trust and adoption. Intelligent ranking based on client fit, conversion probability, business value, urgency, and contact history.
Existing NBAs are difficult and slow to create. Natural-language NBA generation: "Create an NBA for affluent clients likely to invest excess cash."
Limited transparency behind recommendations. Explainable AI showing key signals, confidence level, expected value, and exclusion criteria.
Static NBAs quickly become outdated. Continuous monitoring and automatic optimization based on performance trends and market changes.

2. Which clients should I target for a specific campaign?

Current Urgent Pain Points AI-Driven Hackathon Solution
Campaign targeting relies on manual segmentation and specialist support. Natural-language audience builder: "Find SME clients with strong cash positions and financing potential."
Broad campaign segments result in low conversion rates. AI-driven micro-segmentation using behavioral and transactional signals.
Advisors cannot easily understand why clients were selected. Explainable targeting showing eligibility criteria and matching signals for each client.
Customer data is spread across multiple systems. Unified client intelligence layer combining products, transactions, CRM interactions and meeting notes.
Targeting ignores timing and channel preferences. AI automatically considers contact history, channel preferences, consent and recent activities.

3. Can I create or adapt an NBA on the fly?

Current Urgent Pain Points AI-Driven Hackathon Solution
Creating new NBAs requires analytics and data-science resources. Conversational NBA Builder allowing business users to create NBAs using natural language.
Adapting existing campaigns is slow and cumbersome. AI-assisted NBA editing: "Exclude recently contacted clients and focus on premium clients."
Business users depend on specialists for implementation. AI converts intent into audience criteria, offer logic, timing and channel recommendations.
Lack of reusable NBA patterns. AI-generated NBA templates and marketplace of reusable actions.
Limited explainability of generated actions. Human-readable summary of generated rules, assumptions, and expected outcomes.

4. Can I create a predictive NBA on the fly?

Current Urgent Pain Points AI-Driven Hackathon Solution
Predictive analysis requires data scientists and complex tooling. AI translates business questions into approved predictive models automatically.
Advisors cannot identify future opportunities or risks themselves. Natural-language predictive requests: "Find clients likely to churn within 90 days."
Predictions are difficult to validate and trust. Explainable AI with risk factors, confidence scores and contributing signals.
Predictive use cases are siloed and difficult to operationalize. One interface for churn, loan default, liquidity, investment and product propensity predictions.
Predictions often remain insights rather than actions. Automatically generate actionable NBAs from prediction results.

5. Can all relevant client and bank context be considered automatically?

Current Urgent Pain Points AI-Driven Hackathon Solution
Client information is fragmented across systems. Unified Client Context Layer aggregating customer, product, transaction and interaction data.
Recommendations overlook relationship history and meeting notes. AI retrieves structured and unstructured context automatically.
Advisors must manually determine relevant information. Context Agent automatically identifies relevant information for each NBA.
Recommendations do not reflect recent customer events. Real-time event processing and timeline-based decisioning.
Regulatory and suitability checks are manually verified. Policy Agent validating consent, suitability, eligibility and contact restrictions automatically.

6. Can campaign outcomes and feedback improve future recommendations?

Current Urgent Pain Points AI-Driven Hackathon Solution
Campaign learnings are rarely reused effectively. Closed-loop learning from campaign outcomes and advisor actions.
Rejected recommendations continue to appear. Feedback Agent suppresses repeatedly dismissed or irrelevant NBAs.
Advisors have few ways to improve recommendations. Feedback controls: Accept, Edit, Snooze, Dismiss, Incorrect.
Customer responses are not systematically analyzed. AI captures objections, outcomes and preferences as learning signals.
Teams cannot see whether recommendations improve over time. Performance dashboard tracking conversion lift, acceptance rates and recommendation quality.

Recommended Advisor Copilot Output Card

Information Description Recommended Action What should happen next
Target Client Which client should be contacted and why
Key Signals Most important triggers supporting the recommendation
Conversation Starter AI-generated opening tailored to the client
Recommended Channel Call, meeting, email or advisory review
Confidence Score Confidence and expected business value
Guardrails Consent, suitability and compliance checks
Feedback Actions Accept, Edit, Snooze, Dismiss, Mark Incorrect

Example Prompt

"Suggest a portfolio review for business clients with persistent excess cash balances, no investment mandate and no relationship review during the last 12 months. Exclude clients contacted regarding investments within the last 30 days."

AI Output