Methodology
How DepositIQ frames product, pricing and customer decisions using public market reference data, synthetic behavioural data and deterministic scoring logic.
Methodology overview
Understand the data sources, scoring models, caveats and governance logic behind DepositIQ before using the market, pricing, complexity, primacy and prioritisation workbenches for product decisions.
1. Product thesis
DepositIQ connects market signals, product economics, customer behaviour and roadmap prioritisation into one product operating rhythm for deposits and payments teams.
2. Target users
- Deposit product managers
- Payments product managers
- Pricing teams
- Treasury and funding teams
- Digital banking teams
- Customer experience teams
- Risk and compliance partners
3. Data sources
DepositIQ uses public product reference data where available, RBA cash rate context, fallback benchmark data, and synthetic customer and feedback datasets.
4. Why CDR Product Reference Data matters
Public CDR Product Reference Data can help compare product features, rates, fees and eligibility without accessing customer data.
5. Scoring models
- Rate competitiveness score
- Product simplicity score
- Product conditionality score
- Primary banking score
- Friction severity score
- Prioritisation score
6. Decision governance
Final product decisions should involve Product, Finance, Treasury, Risk, Compliance, Technology and Operations.
7. Caveats and limitations
Deposit products can include tiers, introductory rates, bonus conditions, eligibility rules and special conditions. All outputs are indicative and require human review.
8. Future roadmap
- Historical rate tracking
- Rate movement alerts
- CSV upload for customer behaviour
- CSV upload for complaints and feedback
- AI-generated product committee memo
- Segment-level elasticity modelling
- Term deposit maturity analytics
- Governance workflow and audit trail
- Vendor resilience module
- Exportable pricing committee pack