AI Agent for Next Best Product in Retail Banking
An AI agent for Next Best Product continuously observes customer behaviour and account activity, reasons over customer intent and product eligibility, and then acts by proposing the most relevant product or offer at the right moment.
Book DemoSummary
An AI agent for Next Best Product continuously observes customer behavior and account activity, reasons over customer intent and product eligibility, and then acts by proposing the most relevant product or offer at the right moment.
As shown in the demo, the agent learns from customer responses and RM feedback to improve future recommendations, turning everyday banking activity into timely, individualized cross-sell and upsell opportunities for existing retail banking customers.
The Problem We Are Solving
As illustrated in the video, banks already capture rich behavioral data across core banking, cards, digital channels, and CRM systems. However, this data is fragmented and under-utilized in day-to-day customer engagement.
As a result:
- There is no systematic, per-customer prediction of product opportunities; timing and fit depend heavily on manual analysis and static campaigns.
- Clear behavioral signals such as large deposits, recurring income, overdrafts, and regular bill payments are not translated into specific, eligible next-best offers.
- Outreach remains generic and campaign-driven, leaving RMs and contact centers without clear, data-backed guidance on what to offer next and why.
- Suitability, consent, and audit requirements introduce friction, making teams cautious and slowing experimentation with personalized offers.
What Is an AI Agent for Next Best Product?
In this use case, the AI Agent operates as an agentic layer, running a continuous closed loop rather than a one-time scoring model.
This is reflected in the demo through four core capabilities:
Perception
The agent continuously listens to real-time and batch events, including large or recurring deposits, overdrafts, and utility or subscription payments across core banking, card systems, and digital channels.
Reasoning
It evaluates customer need, eligibility, and product propensity, and generates short, human-readable rationales such as:
“Consistent surplus after payroll indicates suitability for high-yield savings or short-term CDs.”
These rationales are surfaced directly to RMs and, where appropriate, to customers.
Action
The agent pushes next-best-product recommendations into RM and CRM workbenches, contact-center desktops, mobile and web journeys, and campaign systems, with configurable autonomy and approval workflows.
Learning
Customer responses, RM decisions, and product performance are continuously fed back into the system, refining product selection, timing, and messaging over time.
Example Data Sources Used in the AI Agent
As visualized in the demo, the agent draws from multiple behavioral and contextual sources, including:
- Transactional events: Large one-time deposits, recurring payroll or transfers, overdraft events and fees
- Card and payment streams: Utility and telecom bills, subscriptions, rent, and other regular outflows
- Digital engagement: App logins, session recency, offer views and clicks, channel usage patterns
- CRM and product context: Existing product holdings, lifecycle stage, householding, risk profile, and eligibility rules
- Campaign and interaction history: Prior offers, accept/decline outcomes, communication preferences, and opt-in status
How the Solution Works (3-Phase Framework)
1. Data Collection
Real-time events and nightly batches from core banking, cards, digital channels, and CRM systems are streamed into a normalized customer behavior graph.
Data minimization and masking policies ensure the agent accesses only the attributes required for product decisioning and explanation.
2. Model Training & Scoring
Behavioral features are engineered, including surplus after deposits, stability of recurring credits, overdraft frequency, and regularity of bill payments.
Models estimate product propensity and suitability at the customer level across product families such as savings, investments, credit, cards, and insurance.
Explanation tokens are generated alongside scores and can be surfaced to RMs or customers, for example:
“You regularly maintain a surplus above X, so Y product may be beneficial.”
3. Action Plan & Engagement
As demonstrated in the video, behavioral signals are mapped to next-best-product actions, such as:
- Large deposit: High-yield savings accounts, short-term CDs, starter investment portfolios
- Recurring deposits: Auto-savings plans, goal-based or retirement accounts
- Overdraft occurrences: Overdraft protection, small revolving lines of credit, cash-flow tools
- Utility bill payments: Rewards credit cards, home or renter’s insurance, bill-management insights
Delivery Pathways
- CRM / RM workbench: Daily prioritized customer lists with recommended products and “why now” explanations
- Mobile and web: In-session prompts and post-event nudges triggered immediately after relevant behavior
- Campaigns: Agent-driven email and SMS journeys aligned to consent and frequency caps
- AI chat assistant: Tailored cross-sell suggestions and explanations during live customer conversations
Autonomy Levels (Configurable Guardrails)
- Recommend-only mode: The agent proposes offers; RMs or marketers approve execution
- Hybrid mode: Low-risk educational or savings nudges are auto-executed, while regulated products are routed for human approval
Continuous Improvement
The system captures the full closed loop — from event to recommendation, response, and product activation.
Models are retrained and recalibrated based on acceptance and decline patterns, channel performance, and time-to-conversion.
Drift, channel fatigue, and suitability constraints are monitored continuously, with automated threshold adjustments or pause mechanisms when required.
