From Bethaney Rubin Quesnelle [Updated] 

Senior Business Systems Analyst: Wealth Management

Role Overview
Support design and delivery of AI-enabled applications in wealth management.

Key Responsibilities

• Lead discovery sessions, workshops, and process analysis with business, technology, data, risk, compliance, privacy, and operations.
• Identify where generative AI, agentic AI, automation, analytics, or traditional technology fits; define AI use cases across advisory, operations, client servicing, onboarding, reporting, compliance, and productivity.
• Gather AI-aware requirements: data needs, permissions, human oversight, confidence thresholds, exception handling, explainability, auditability, fallback processes.
• Document current/future state processes, business rules, roles, controls, impacts, and non-functional requirements (security, performance, retention, privacy); validate with SMEs.
• Write epics, user stories, use cases, process flows, and acceptance criteria.
• Profile data (quality, completeness, lineage, accessibility, AI suitability) and produce data mappings.
• Define positive, negative, exception, and misuse scenarios; support UAT, backlog refinement, and delivery planning.
• Define evaluation and success measures (response quality, accuracy, usability, business outcomes); maintain end-to-end traceability.

Required Qualifications

• 7+ years as a BSA, Senior BA, or Product Analyst in a large or regulated organization.
• Led discovery and requirements for complex business and technology initiatives.
• Strong user stories, acceptance criteria, process models, and functional requirements.
• Practical data profiling, quality, mapping, and lineage experience.
• Experience supporting digital, automation, analytics, ML, or generative AI solutions.
• Agile, hybrid, or scaled delivery; strong facilitation and stakeholder management.
• Tools: Jira, Confluence, Azure DevOps, SQL, Visio, or equivalent.

Wealth Domain Knowledge

• Business model: full-service advisory, discretionary/managed, self-directed, private wealth; segments (retail affluent, HNW, UHNW, business owners, family offices); revenue drivers (advisory fees, commissions, asset-based fees, margin/interest spread, distribution fees).
• Products and accounts: equities, fixed income, mutual funds, ETFs, structured notes, GICs, alternatives; cash, margin, registered, non-registered, corporate/trust/estate, joint accounts; Canadian registered plans (RRSP, RRIF, TFSA, RESP, FHSA, LIRA/LIF); managed solutions (SMAs, model portfolios, discretionary mandates).
• Client lifecycle: prospecting, onboarding, KYC, account opening, funding/transfers, advice and trading, review, offboarding; ID, suitability, risk profiling, AML/source of funds, tax residency, beneficial ownership checks; periodic KYC refresh and suitability reassessment.
• Advisor and branch workflow: advisor, associate, branch manager, compliance, operations, trading, and product specialist roles; books of business; account opening, client notes, orders, rebalancing, reviews, transfers, cash movements.
• Operations: trade lifecycle (entry to settlement and confirmation); deposits/withdrawals, journals, security and in-kind transfers, corporate actions, estate processing.
• Compliance and risk: KYC, suitability, best-interest obligations, documentation, record retention, restricted activities, regulatory landscape; market, credit/margin, operational, conduct, reputational, regulatory risk.
• Suitability inputs: objectives, time horizon, risk tolerance/capacity, net worth, liquidity, knowledge.
• Data, reporting, systems: client, household, account, advisor, holdings, transaction, order, fee, risk, compliance data; statements, performance, tax, compliance, advisor productivity, AUM/revenue reports; CRM, account opening, trading, portfolio management, document management, surveillance, data warehouse.
• Fees and documents: commission, asset-based, household pricing, waivers; revenue attribution by advisor, branch, product; account applications, KYC forms, managed account agreements, fee schedules, confirmations, statements, tax documents.
• Vocabulary: AUM, discretionary vs. non-discretionary, margin, cost base, householding, beneficial owner, rebalancing, corporate action, settlement.

AI Knowledge and Experience

• Understands generative AI and LLM concepts (prompts, grounding, RAG, embeddings, hallucinations, evaluation) and can write requirements for AI applications: data needs, human oversight, confidence thresholds, guardrails, and evaluation criteria.
• Hands-on user of Copilot, OpenAI, or Claude to extract requirements from multiple documents and data sources, and to generate analytical tools, requirements artifacts, and presentations through effective prompting.
• Validates AI outputs for accuracy and quality; defines acceptance criteria and test scenarios for AI-enabled applications.