Infrastructure for Next-Best-Action Recommendation Engine

The recommendation engine requires explicit business logic defining what constitutes a "relevant" product recommendation, which life events trigger which product needs, and how to evaluate propensity. This isn't "we generally offer mortgages to people buying homes" - it's formalized rules: IF (Customer.Age BETWEEN 28-45 AND Customer.LifeEvent.HomePurchase WITHIN 6mo AND Customer.AUM > $250K) THEN Recommend.Mortgage.Jumbo WITH Propensity.Score.Calculation. Without this explicit logic, the AI generates recommendations that contradict business strategy.

The system needs automated capture of life events (from transaction patterns, external data), engagement outcomes (which recommendations converted, which were ignored), and contextual signals (recent calls, email clicks, website visits). This must be event-driven and real-time - waiting for quarterly CRM updates means the AI recommends mortgage products 3 months after the client bought a house.

ML models require formal ontology mapping Clients to Life Events to Products to Propensity Scores. Without explicit entity definitions and relationship mappings (Client.LifeEvent.HomePurchase → Product.Mortgage WITH Conditions: Client.CreditScore, Client.DebtToIncome, Market.InterestRate), the AI can't generate contextually appropriate recommendations. This is knowledge graph work - entities, relationships, constraints in machine-readable form.

The recommendation engine must access client demographic/financial data (CRM), transaction history (core banking), product catalog (product system), life event signals (multiple sources), and engagement history (marketing automation, call center). This requires unified API access layer - not just accessing systems but accessing consistent, real-time client context across all of them. Without L4, the AI operates on partial client view and generates recommendations based on incomplete information.

Product offerings change. Market conditions shift. Client behavior evolves. The recommendation engine needs near-real-time sync: when mortgage rates drop 0.5%, refinance recommendations should trigger within hours, not weeks. When regulatory changes restrict product eligibility, recommendations must update immediately to avoid compliance violations. Stale propensity models (trained on pre-pandemic behavior) generate irrelevant recommendations.

This capability requires integration platform (iPaaS) orchestrating data flows between CRM, core banking, product catalog, marketing automation, call center, external data sources, and RM interfaces. Context must unify: when RM opens client profile, AI recommendation is based on demographic + transaction + engagement + life event + market data assembled in real-time. Point-to-point integrations create data latency and inconsistency - AI recommendation is based on yesterday's CRM export while client withdrew $100K this morning.