Description

A focused course, tailored for you

Building a Wealth-Tech Real-Time Personalisation Engine (Next-Best-Action + Compliance + Latency + Bias Monitoring)

Build the wealth-tech real-time personalisation engine in 10 weeks. Next-best-action model + sub-100ms latency architecture + Reg BI compliance + bias monitoring + executive engagement.

Wealth-management platforms compete on real-time personalisation: serving the right offer, the right insight, the right advisor connection at the right moment. Engineers who build the production-grade real-time personalisation engine take the senior platform work. Here is the 10-week build.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Wealth-management platforms shifted to real-time personalisation in 2024-2026. Account-tier-aware offers, life-event-triggered advisor connections, market-event-triggered insights, behavioural-trigger nudges, and tax-aware recommendations all require sub-100ms inference, full compliance overlay, and bias monitoring across protected classes.

Most wealth-tech platforms ship batch-personalisation that updates daily. The shift to real-time is structurally different from a tech and compliance perspective. Engineers who can build the production-grade real-time personalisation engine take the senior platform work.

This course teaches the 10-week build of a wealth-tech real-time personalisation engine: next-best-action model, sub-100ms latency architecture, compliance overlay (Reg BI, fiduciary, FINRA suitability, state-DOI for affiliated insurance), bias monitoring, observability, and the executive engagement model. Twelve modules with deliverables. Plus a hand-built implementation playbook for your specific platform.

What you walk away with

A documented next-best-action model design.
A sub-100ms latency architecture.
A compliance overlay (Reg BI + fiduciary + FINRA suitability + state DOI).
A bias monitoring framework across protected classes.
An observability architecture.
An executive engagement model.
A 10-week build plan.

The 12 modules

Module 1. Wealth-tech real-time personalisation landscape 2026

Detailed walkthrough of wealth-tech personalisation state-of-art: account-tier offer engines, life-event detection (job change, marriage, retirement, inheritance), market-event triggers, behavioural triggers, tax-loss-harvesting opportunities, and the regulatory landscape (SEC Reg BI 2019, DOL fiduciary rule 2024, FINRA suitability Rule 2111). What competing platforms are shipping.

Module 2. Next-best-action model design

Build the next-best-action model design: feature engineering (account, holdings, demographics, behavioural, market), model architecture (gradient-boosted ensemble + neural ranking + reinforcement learning), action-space definition, reward modelling, calibration, and the model-validation framework. Three NBA model patterns with technical detail.

Module 3. Sub-100ms latency architecture

Build the sub-100ms latency architecture: feature store with real-time materialisation (Feast, Tecton, in-house), model serving (Vertex AI, SageMaker, Triton, BentoML, in-house), caching strategy, edge deployment, model selection routing (fast model for triage, accurate model for complex cases), and the latency-budget framework. Three production architectures with measured benchmarks.

Module 4. Compliance overlay

Build the compliance overlay: Reg BI (Best Interest standard) application to AI-generated recommendations, fiduciary-standard considerations, FINRA Rule 2111 suitability, FINRA Rule 2210 communications, state-DOI considerations for affiliated insurance products, audit-trail requirements, and the integration with broader compliance. The overlay that prevents enforcement action.

Module 5. Bias monitoring across protected classes

Build the bias monitoring framework: protected-class measurement infrastructure (race, ethnicity, gender, age, marital status, etc), disparate-impact metrics (demographic parity, equal opportunity, equalized odds), bias-investigation workflow, remediation patterns, and the executive-reporting cadence. Aligned to OCC + Fed expectations + CFPB UDAAP + EU AI Act if applicable.

Module 6. Real-time data infrastructure

Build the real-time data infrastructure: event-stream architecture (Kafka, Kinesis, Pub/Sub), real-time feature engineering, market-data integration, customer-event integration, batch-feature backfill, and the data-lineage integration. The infrastructure that feeds the model.

Module 7. Action delivery and orchestration

Build the action delivery and orchestration: in-app delivery (push notification, in-app banner, screen takeover), advisor-channel delivery (advisor dashboard, CRM integration), email/SMS delivery (Salesforce Marketing Cloud, Iterable, Braze), call-center surface, and the orchestration platform (Salesforce Personalization, Adobe Target, in-house). Three orchestration patterns.

Module 8. Customer journey and experimentation

Build the customer journey and experimentation: A/B testing infrastructure, multi-armed bandit patterns, journey-orchestration platform integration, holdout-group management, and the experiment-decision framework. The framework that learns and improves.

Module 9. Model risk management integration

Build the MRM integration: Fed SR 11-7 model risk management applied to personalisation models, ECB TRIM alignment, EU AI Act if applicable, validation cadence, challenger-model practice, ongoing-monitoring, model-decommissioning workflow. The MRM integration that wealth-tech AI requires.

Module 10. Observability and incident response

Build the observability and incident response: per-action logging, model-performance monitoring, drift detection, action-outcome telemetry, customer-feedback capture, complaint-aggregation, regulator-complaint tracking, and the incident-response workflow specific to personalisation models. Three observability patterns.

Module 11. Executive engagement and board reporting

Build the executive engagement: CTO partnership, CDO partnership, CCO partnership (Reg BI and fiduciary), CRO partnership (MRM), CMO partnership (CX), and the board-of-directors reporting cadence. Personalisation metrics the board reads: lift on next-best-action conversion, AUM contribution attributable to personalisation, complaint-rate trend, bias-monitoring posture.

Module 12. Your 10-week build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: wealth-tech personalisation landscape + next-best-action model design. Weeks 3-4: sub-100ms latency architecture + compliance overlay. Weeks 5-6: bias monitoring + real-time data infrastructure. Weeks 7-8: action delivery and orchestration + customer journey and experimentation. Weeks 9-10: MRM integration + observability + executive engagement. Deliverable: production-grade wealth-tech real-time personalisation engine.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers the landscape.

Modules 2 to 5 produce next-best-action model, latency architecture, compliance overlay, and bias monitoring.

Modules 6 to 8 cover real-time data infrastructure, action delivery, and customer journey experimentation.

Modules 9 to 10 cover MRM integration and observability.

Module 11 covers executive engagement.

Module 12 covers the 10-week build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates and working code examples for next-best-action model, sub-100ms latency architecture, compliance overlay, bias monitoring framework, real-time data infrastructure, action delivery and orchestration, customer journey experimentation, MRM integration, observability, executive engagement.
A hand-built implementation playbook generated for your specific platform.
Three worked examples of wealth-tech real-time personalisation engines at peer platforms.
Scripted talking points for the CDO and CCO engagement.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: Next-best-action model design scaffold drafted.

Week 4: Sub-100ms latency architecture + compliance overlay built.

Week 8: Bias monitoring + observability operational.

Week 10: Production-grade engine in operation.

Before and after

Before

Your wealth-tech platform ships batch personalisation that updates daily. Competing platforms ship real-time. Customer engagement lags. Regulators ask about bias monitoring.

After

A production-grade real-time personalisation engine is operating. Sub-100ms latency. Compliance overlay across Reg BI, fiduciary, FINRA suitability, state DOI. Bias monitoring across protected classes. Observability catches drift before customer complaints.

What happens if you do not address this

Wealth-tech platforms without real-time personalisation lose customer engagement to peer platforms. Regulators increasingly ask about AI personalisation bias monitoring.

Who it is for

For wealth-tech engineers, ML engineers, platform engineers, and engineering managers at wealth-management platforms.

Who this is NOT for. Pure research roles. Engineers at firms with no wealth-management business. Pure technology firms.

How it arrives

Text-based course via LMS, plus downloadable code examples and templates and the hand-built implementation playbook.

Time investment. Roughly 18 hours of reading and 100 to 200 hours building the first production engine.

Why $199 is the right number

External wealth-tech AI consultants charge $300K-$1.5M for engine builds. Big4 wealth advisory engagement runs $500K-$3M. Specialist AI firms (Personalisation specialists like Persado, Dynamic Yield) charge $200K-$1M. $199 buys the focused playbook plus the implementation document for your specific platform.

FAQ

Will this replace hiring a personalisation specialist?

Partially. It teaches the production pattern. You may still want specialist input for advanced reinforcement-learning approaches.

What if my platform is advisor-led (not direct-to-consumer)?

Module 7 covers advisor-channel delivery.

Does this cover affiliated insurance product personalisation?

Module 4 covers state-DOI considerations for affiliated insurance.

What about cross-border (US + international) considerations?

Module 4 covers international regulator overlap.

What is in the implementation playbook for me specifically?

Next-best-action model design tailored to your platform's customer base; latency architecture matched to your existing infrastructure; a 10-week build plan.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.