The ACCELERAID Platform
Experience Optimisation & Delivery
Bayesian bandit optimisation, 150+ co-branded landing pages and real-time personalisation — delivered on web, app, email, push and in the branch. Every experience measurable, every result traceable.
+15%
Avg conversion uplift
+120%
Card applications (one issuer)
Real-time
Sub-100ms API decisions
All channels
Web, app, email, branch
Why ACCELERAID
Stay ahead with smarter optimisation
+15% Avg Conversion
Proven average conversion uplift across ACCELERAID customer journeys — measured rigorously with incrementality testing.
No-code Variant Builder
Non-technical users can set up, launch and monitor experiments without engineering resources — full visual editor included.
How It Works
From transaction data to personalised offer — in milliseconds
The AI layer reads every signal from the banking app, runs it through ML models, and delivers the right offer before the customer closes the screen.
Transaction Data → AI Models → Personalised Offer
Intelligence Layer
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Transaction Data Analysis
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AI Personalisation Engine
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Customer Lifecycle Models
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Real-time Decisioning
Outcomes
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Real-time Personalisation
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Next Best Action
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Cross-Sell & Up-Sell
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Customer Retention
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Financial Insights
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Revenue Growth
Key Capabilities
Experience Optimisation: what it delivers
Journey Optimisation
Optimise application flows, onboarding and landing experiences. Identify and fix conversion drop-off points with AI-driven recommendations.
AI allocation
Route traffic to stronger variants based on evidence. Multi-armed bandit algorithms outperform traditional A/B testing on speed and efficiency.
Measurement & Lift
Track uplift in conversion and operational outcomes. Rigorous incrementality testing ensures every improvement reflects real business impact.
Personalised Flows
Adapt content, form fields and calls-to-action in real time based on customer profile and context from the CDP.
Safe Experimentation
Guardrail metrics prevent harmful experiments. Automated rollback if performance falls below defined thresholds.
No-code Editor
Non-technical users can set up, launch and monitor experiments — without engineering resources.
Omnichannel Delivery
One platform for every channel
Stop building separate personalisation stacks for each channel. ACCELERAID’s decision layer connects web, app, email, push, branch and call centre — from one governed data and AI foundation.
Real-time decision API
Sub-100ms response times for in-session personalisation. No batch jobs, no stale offers at the point of interaction.
Cross-channel attribution
See the real impact of each touchpoint on conversion and retention. No more last-click attribution.
Channel Details
Deep dive into each delivery channel
Proof
What optimisation achieves in practice
A leading European credit card issuer achieved +120% more card applications with ACCELERAID AI allocation and application flow optimisation. The AI found the winning variant and scaled it — without waiting for statistical significance.
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AI allocation converges faster than traditional A/B testing
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Personalised flows adapt to individual customer context
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Guardrail metrics protect against harmful experiments
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Full integration with CDP data — no separate data layer needed
+120%
Card applications at one issuer
+15%
Average conversion uplift
AI
Allocation (not manual A/B)
6–9 mo
Time to ROI
Visual Editor
See the optimisation engine in action
Set up variants, allocate traffic and measure results — all through an intuitive interface.
Technical Annex — AI / ML Components
Pre-configured AI & Machine Learning Models
The ACCELERAID platform includes preconfigured predictive scoring and analytics components as part of the licensed modules. All models operate exclusively as decision support — the system does not make fully autonomous financial decisions. Campaign activation and business decisions remain under the customer’s control.
2.1 Behavioural Scoring Models
Model family: QuantileTransformer (self-optimising quantile transformation)
Applies a quantile transformation to user-level transaction data to normalise behavioural distributions and derive a standardised behavioural score — ensuring comparability across users with different transaction volumes and spending patterns.
Data: Transaction history · aggregated spending patterns · frequency & recency metrics
Retraining: Automatic recalculation with every data refresh cycle (typically daily)
Baseline version: SaaS v4.23 (Feb 2026)
2.1.1 Activity Scoring Model
Purpose: Engagement probability estimation
Estimates customer engagement probability based on historical transaction behaviour. Scores are recalculated automatically with each data refresh cycle.
Data: Transaction recency · frequency · aggregated spend
Monitoring: Regular score distribution & stability checks
Baseline version: SaaS v4.23 (Feb 2026)
2.1.2 Predictive Churn Model
Purpose: Early churn & inactivity detection
Estimates the probability of future customer inactivity or churn based on historical behavioural and transaction patterns. Identifies early behavioural signals of declining engagement.
Data: Recency & frequency · spending trends · engagement indicators · card usage metrics
Retraining: Periodic — typically weekly or in sync with the customer’s data refresh
Baseline version: SaaS v4.23 (Feb 2026)
2.2 Merchant Category Correlation Model
Model family: Statistical correlation analysis / scoring
Identifies behavioural affinities between merchant categories to support targeting and cross-category insights. Enables “customers who spend here also spend there” logic for campaigns.
Data: Categorised transaction data (MCC) · frequency & co-occurrence metrics
Retraining: Automatic recalculation with updated transaction data
Baseline version: SaaS v4.23 (Feb 2026)
2.3 Variety Score
Model family: Statistical diversity scoring
Measures diversity of spending behaviour across categories. Provides a single metric reflecting how broadly a customer engages across merchant categories — used for segmentation and targeting.
Data: Aggregated transaction category metrics
Retraining: Automatic recalculation with every data refresh
Monitoring: Distribution consistency checks
2.4 Recommendation / Next-Best-Action
Model family: Correlation-based scoring & Bayesian multi-armed bandit
Identifies relevant product or campaign opportunities based on transaction patterns. Outputs are recalculated dynamically — core logic is periodically reviewed and validated.
Data: Transaction history · category spend · card usage indicators
Monitoring: Ongoing monitoring of campaign response trends
Baseline version: SaaS v4.23 (Feb 2026)
Model 2.5 — Flagship Algorithm
Contextual Bayesian Bandit — Optimisation
Model family: Multi-armed bandit (Reinforcement Learning, Thompson Sampling)
A separate model can be trained for each audience segment. The algorithm optimises the allocation of content variants within a defined target segment by dynamically adjusting distribution weights — based on observed conversion performance. Traffic is shifted incrementally to better-performing variants, with controlled exploration of alternatives.
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Per-segment model training — each audience gets its own optimised allocation
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Thompson Sampling for efficient exploration vs. exploitation trade-off
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Updates continuously during campaign runtime based on incoming feedback
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Outperforms traditional A/B testing in speed and statistical efficiency
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Guardrail metrics prevent harmful experiments from scaling
Thompson
Sampling — proven RL approach
Per segment
Individual model per audience
Real-time
Updates during campaign runtime
Data: Campaign response data (clicks, conversions) · performance metrics per variant · aggregated behavioural interaction signals
Baseline version: SaaS v4.23 (Feb 2026)
Model Governance
Responsible AI — Governance & Oversight
All ACCELERAID models operate transparently, within client-controlled boundaries and with no autonomous financial decision-making.
Client Data Control
All models operate exclusively on data provided and controlled by the Client. No data is shared across clients. No external model training on client data.
Decision-Support Only
No automated credit approval, pricing decision or financial risk decision is performed by any model. All outputs serve exclusively as decision support — final actions remain under Client control.
Client Regulatory Responsibility
The Client remains responsible for regulatory compliance regarding the use of model outputs in their jurisdiction. ACCELERAID provides audit documentation on request.
Performance Monitoring
Each model is regularly monitored for score distribution stability, statistical consistency and predictive accuracy. Drift detection triggers review cycles.
Proprietary AI Framework
Built on a TensorFlow-based framework enabling development of additional client-specific models. Custom models are implemented separately under Professional Services scope.
Model governance contact
Questions about model design, governance documentation or regulatory compliance support:
AI & Model Governance
Blog
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+15%
Avg. conversion uplift
250+
Enterprise projects
3.5bn
Transactions analysed
6–9 mo
Avg. time to ROI
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