AI Agents

Context

Built for banking as a Stand-alone Quick Win and used by Retail/product teams, this agent focuses on next-best-product recommendations for existing customers. It’s designed for Tier 2–3 banks that have a decent portfolio of products but struggle to translate customer data into timely, relevant offers that actually get used.

What it does

The agent builds a behavioural and product-usage profile for each customer from the data you already hold—accounts, cards, transactions, channels, simple demographics—then generates next-best-product suggestions that fit that profile. Instead of generic campaigns, it highlights which products are most relevant for a given customer and when to surface them (for example, in app, via RM, or during service interactions). The recommendations are exposed directly in your CRM, RM tools, or digital channels so they can be acted on during normal customer touchpoints rather than in one-off campaigns only.

Core AI functions

The core capability is recommenders: the agent uses recommender models tuned for banking (co-occurrence, similarity, and simple propensity patterns) to rank products for each customer. It turns raw usage and interaction data into a short, ordered list of offers with a clear rationale that can be consumed by front-end channels and RMs, rather than requiring analysts to hand-craft segments and rules for every campaign.

Problem solved

Many banks have low activation on existing customers: they open an account or card and then do little else, because offers are generic, poorly timed, or irrelevant. Campaign teams rely on coarse segments and static rules, and RMs don’t have a simple view of “what to talk about next” for each customer. This agent addresses that by providing a consistent, data-driven next-best-product signal at customer level, so outreach feels targeted instead of random.

Business impact

The primary impact is higher CLV: more customers adopt additional products that genuinely fit their profile, balances and usage deepen over time, and relationship value increases without needing aggressive pricing or blanket campaigns. Activation improves because the right offers show up for the right customers, supporting cross-sell and up-sell in a structured way rather than relying on opportunistic selling. For Tier 2–3 banks, it’s a practical way to unlock more value from the existing book.

Integration and adjacent use cases

Integration complexity is Low–Med: the agent needs access to basic customer, product, and transaction data and a way to surface recommendations into your CRM, RM desktop, and/or digital channels; it then writes recommended products or offer IDs back into those same environments.

Common combinations in this stack:

• Credit Score Improvement Assistant to suggest concrete actions customers can take to raise their credit score, which can then be paired with tailored product offers;

• Alternative Risk Assessor to provide an alternative-data risk view on thin-file customers so recommendations respect risk appetite;

• Customer 360 Summary Generator to give RMs a unified, at-a-glance snapshot of each customer that includes current holdings plus the top recommended next products;

• Adverse Media Screening Agent to overlay quick adverse-news checks on higher-value or higher-risk profiles before targeted outreach;

• Inbound Email Agent (RM Copilot) to triage client emails and surface relevant next-best-product suggestions directly into RM replies; and

• Legal Contract Drafting Assistant to generate compliant contracts and confirmation letters quickly once a recommended product is accepted, keeping the end-to-end experience fast for both the bank and the customer.