Experience Delivery
& AI Optimisation
Bayesian Bandit optimisation, 150+ co-branded landing pages and real-time personalisation on web & app. Every experience measurable, every outcome traceable.
Stay ahead with smarter optimisation
Run sophisticated experiments across application flows, landing pages and checkout funnels — with statistical rigour and automated analysis.
Multi-armed bandit algorithms automatically route traffic to winning variants — converging faster than traditional A/B tests.
Proven average conversion uplift across ACCELERAID customer journeys — measured rigorously with incrementality testing.
Non-technical users can set up, launch and monitor experiments without engineering resources — full visual editor included.
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 Analysis
- AI Personalization Engine
- Customer Lifecycle Models
- Real-time Decisioning
- Real-time Personalization
- Next Best Action
- Cross-Sell & Up-Sell
- Customer Retention
- Financial Insights
- Revenue Growth
Experience Optimisation
Optimise application flows, onboarding and landing experiences. Identify and fix conversion drop-off points with AI-driven recommendations.
Route traffic to stronger variants based on evidence. Multi-armed bandit algorithms outperform traditional A/B testing on speed and efficiency.
Track uplift in conversion and operational outcomes. Rigorous incrementality testing ensures every improvement reflects real business impact.
Adapt content, form fields and calls-to-action in real time based on customer profile and context from the CDP.
Guardrail metrics prevent harmful experiments. Automated rollback if performance degrades beyond defined thresholds.
Non-technical users can set up, launch and monitor experiments without engineering resources.
What optimisation achieves in practice
The Hanseatic Bank pilot achieved measurable uplift in campaign conversion using ACCELERAID's AI allocation and application flow optimisation. The AI found the winning variant and scaled it — without waiting for statistical significance.
- AI allocation converges faster than traditional A/B testing
- Personalised flows adapt to individual customer context
- Guardrail metrics protect against harmful experiments
- Full integration with CDP data — no separate data layer needed
Pre-configured AI & Machine Learning Models
The ACCELERAID platform includes a set of pre-configured predictive scoring and analytics components as part of the licensed modules. All models operate exclusively as decision-support tools — no fully autonomous financial decisions are made by the system. Final campaign activation and business decisions remain under the control of the Client.
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.
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.
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. Designed to identify early behavioural signals associated with declining engagement.
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.
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.
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.
Contextual Bayesian Bandit Optimisation
Model family: Multi-armed bandit (Reinforcement Learning, Thompson Sampling)
For each audience segment a unique model can be trained. The algorithm optimises the allocation of content variants within a defined target audience segment by dynamically adjusting distribution weights based on observed conversion performance — incrementally shifting traffic towards better-performing variants while maintaining controlled exploration of alternatives.
- Per-segment model training — each audience gets its own optimised allocation
- Thompson Sampling for efficient exploration vs. exploitation trade-off
- Updates continuously during campaign runtime based on incoming feedback
- Outperforms traditional A/B testing in speed and statistical efficiency
- Guardrail metrics prevent degrading experiments from scaling
Baseline version: SaaS v4.23 (Feb 2026)
Responsible AI — Governance & Oversight
All ACCELERAID models are built to operate transparently, within client-controlled boundaries, with no autonomous financial decision-making.
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.
No automated credit approval, pricing decision or financial risk decision is performed by any model. All outputs serve as decision-support inputs — final actions remain under Client control.
The Client remains responsible for regulatory compliance regarding the use of model outputs in their jurisdiction. ACCELERAID provides audit documentation on request.
Each model is subject to periodic monitoring of score distribution stability, statistical consistency and predictive accuracy. Drift detection triggers review cycles.
Built on a TensorFlow-based framework enabling development of additional client-specific models where required. Custom models are implemented separately under Professional Services scope.
Questions about model design, governance documentation or regulatory compliance support:
Simon Greiner
AI & Model Governance
sales@acceleraid.ai
See how governed customer data and safe AI improve both growth and compliance.
One session. Your use case. Real numbers.