Robo-Advisory and Digital Wealth Management: Suitability Logic and Model Portfolios – Jeddah, 14–18 Nov 2027

Programme overview

Introduction:

Robo-advisory and digital wealth management platforms promise low-cost, always-on investment advice, yet many launches stall because questionnaires misclassify investors, allocation algorithms drift without oversight, unit economics never reach break-even and customers abandon onboarding halfway. This Core Concept course equips wealth, product and fintech staff to design and run an automated advice service: choosing between pure digital and hybrid models, building suitability logic, assembling ETF model portfolios, setting rebalancing rules, pricing the service and governing the algorithms. Participants produce a Digital Advice Proposition and Suitability Framework for a case bank.

Course Objectives:

• Compare pure digital, hybrid and adviser-assisted robo-advisory models and select the one that fits a target investor segment and cost base
• Design a digital onboarding journey with electronic identity checks and a scored investor questionnaire that maps each answer set to a suitability outcome
• Construct a ladder of ETF model portfolios using mean-variance optimisation and document the fund screening criteria behind each sleeve
• Specify automated rebalancing triggers, cash-flow handling and tax-aware harvesting rules and estimate their trading cost
• Build a unit economics model for an automated advice platform covering acquisition cost, assets per account, fee yield and break-even
• Set up algorithm governance with change control, back-testing, output monitoring and conduct risk indicators for digital advice

Target Audience:

• Wealth product staff who define digital investment propositions, pricing and service tiers
• Investment and portfolio staff who build and maintain model portfolios for automated mandates
• Digital channel and customer journey staff who own onboarding flows and conversion
• Fintech delivery and platform staff who integrate custody, order routing and data feeds
• Risk, conduct and oversight staff who review suitability logic and algorithm changes
• Advice operations staff who run hybrid adviser desks supporting app-based investors

Course Outline:

Day 1: Digital Advice Business Models and Market Landscape

• Robo-Advisory Operating Models: Pure Digital, Hybrid and Adviser-Assisted Platforms
• Digital Wealth Value Chain: Acquisition, Onboarding, Allocation, Execution and Reporting
• Investor Segments for Automated Advice: Mass Affluent, First-Time and Workplace Savers
• Build, White-Label or Partner Options for Launching a Robo Platform
• Digital Advice Readiness Scorecard for an Incumbent Wealth Business

Day 2: Onboarding, Investor Questionnaires and Suitability Logic

• Digital Onboarding Funnel: Identity Verification, Document Capture and Drop-Off Analytics
• Investor Questionnaire Design: Question Weighting, Scoring Bands and Inconsistency Flags
• Suitability Decision Tree: Loss Capacity, Investment Horizon and Knowledge Checks
• Mapping Questionnaire Scores to Model Portfolio Ladders
• Reprofiling Triggers: Life Events, Periodic Refresh and Answer Change Rules

Day 3: Model Portfolio Engineering and Automated Rebalancing

• Mean-Variance Optimisation, Efficient Frontier and Sharpe Ratio for Model Ladders
• ETF Screening Criteria: Tracking Difference, Liquidity, Replication Method and Total Expense Ratio
• Calendar Versus Threshold Rebalancing Bands and Cash-Flow Rebalancing Rules
• Tax-Loss Harvesting and Wash-Trade Controls at Overview
• Goal-Based Planning Engines: Retirement, Education and Home Purchase Projections

Day 4: Platform Economics, Architecture and Algorithm Governance

• Robo Platform Unit Economics: Customer Acquisition Cost, Assets per Account and Break-Even Model
• Platform Architecture: Custody, Order Aggregation, Fractional Units and Data Feeds
• Algorithm Change Control, Back-Testing and Output Monitoring Dashboards
• Conduct Risk Indicators for Digital Advice: Mis-Classification, Herding and Complaint Patterns
• Client Communication Design: Nudges, Drawdown Messaging and Plain-Language Disclosures

Day 5: Case Study: Digital Advice Proposition for a Case Bank

• Case Bank Diagnostic: Customer Base, Product Shelf and Channel Data Review
• Proposition Canvas: Target Segment, Service Tier, Fee Schedule and Hybrid Escalation Points
• Suitability Framework Build: Questionnaire, Scoring Matrix and Portfolio Mapping Table
• Algorithm Oversight Charter with Testing Calendar and Conduct Metrics
• Proposition and Suitability Framework Defence Before a Peer Product Committee

Skills You Will Gain:

• Digital Advice Model Selection
• Investor Questionnaire Scoring
• Suitability Rule Design
• ETF Model Portfolio Construction
• Rebalancing Rule Specification
• Robo Unit Economics Modelling
• Algorithm Oversight
• Digital Investor Communication

Why Attend This Course:

• Return with a Digital Advice Proposition and Suitability Framework built on a case bank and challenged by peers
• Test whether a questionnaire, allocation algorithm or fee schedule will hold up before it reaches customers
• Speak the same language as portfolio, technology and oversight colleagues when a robo platform is specified or changed
• Compare automated advice practice with staff from banks, asset managers, insurers and fintech firms

Conclusion:

An automated advice service earns trust only when its questionnaire classifies investors correctly, its portfolios and rebalancing rules behave as documented and its economics reach break-even without cutting oversight. The course moves from robo-advisory business models and digital onboarding, through suitability logic, ETF model portfolios, rebalancing and goal-based planning, to unit economics, platform architecture, algorithm governance and client communication. The final day turns this work into a Digital Advice Proposition and Suitability Framework for a case bank.