The ACCELERAID Platform
AI Prediction Engine
Propensity, churn, CLV and next-best-action from transaction signals. Explainable ML at enterprise scale — aligned with MaRisk model-documentation requirements.
+15%
Avg uplift
3.5bn
Transactions analysed
Real-time
Scoring
Explainable
AI
Key Capabilities
Prediction Engine: what it delivers
Capability 1
Propensity Scoring
Churn, purchase and product affinity outputs trained on your banking data. Scores that ops teams can actually use — propensity models that adapt continuously to changing customer behaviour patterns.
Purchase propensity per product category
Churn probability with early warning signals
Product affinity scoring across the full portfolio
Capability 2
Next Best Action
Real-time decisioning that recommends the next best product, offer, channel or service action — at the right moment for each customer. Move beyond segment logic to true 1-to-1 orchestration.
Context-aware recommendations per customer
Optimal channel & timing selection
Integrated with the CLM/CVM orchestration engine
Capability 3
Explainable ML
Model documentation, feature-level explainability and model registry – aligned with MaRisk model-documentation requirements. Every score has a traceable explanation — making model outputs transparent for operational adoption and regulatory review in regulated financial environments.
Feature importance for every prediction
Model registry with full version history
Governance documentation on demand
Why ACCELERAID
Built for regulated financial services
Our Prediction Engine is purpose-built for the financial services industry — trained on real banking data and designed for regulatory compliance from day one.
Trained on FSI data
Models designed for financial services transaction patterns, not generic datasets
Explainable outputs
Every prediction carries feature-level explanations for audit and review
Real-time + batch modes
Score at event time or in scheduled batch runs depending on the use case
GDPR-compliant scoring
Consent-aware processing, data minimisation and audit-proof logging
Use Cases
What banks use the Prediction Engine for
Card Acquisition
Identify high-propensity prospects and optimise application funnels. AI-scored lead prioritisation and personalised checkout flows for card programmes.
Mortgage Advisory
Score mortgage propensity from transaction patterns and life-event signals. Prioritise the right customers for advisors at the right time. All scores serve marketing, sales and service – no creditworthiness or lending decisions.
Churn Prevention
Early warning signals trigger retention journeys before customers churn — across cards, accounts and insurance products.
Cross-Sell
Identify eligible customers at the right lifecycle stage for additional products. Signal-driven decisioning replaces batch segment campaigns.
Upsell
Upgrade timing models for premium cards, higher-tier accounts and insurance products — driven by spend patterns and engagement signals.
Segmentation
Dynamic, behaviour-based segmentation that updates in real time — replacing static demographic groups with actionable, data-driven clusters.
AI Model Library
20+ pre-built scores and segments — 3, 10 or 20 included per tier
From churn prediction to customer lifetime value — all models are designed for financial services data and fully explainable for regulatory requirements.
Blog
Related insights
Regulation & Compliance
01 Sept 2026
CCD2 exposes operational breaks in credit journeys. See how to connect data, decisions, explanations and customer support.
CLM & CVM
31 Aug 2026
The lead article to the five-part series: Acquire, Activate, Engage, Retain and Reactivate as one operating system for retail banking.
CLM & CVM
31 Aug 2026
Part 1 of the five-part series: How banks optimise acquisition for qualified completion and later activation.
+15%
Avg. conversion uplift
250+
Enterprise projects
3.5bn
Transactions analysed
6–9 mo
Avg. time to ROI
See how secure customer data and responsible AI improve growth and compliance
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