AI Agents

Context

Built for banking as a Stand-alone Quick Win and used by RM and Servicing teams, this agent tackles a very concrete pain: inbox overload. Relationship managers and service staff spend far too much time reading and sorting client emails instead of actually solving issues or having conversations. The agent sits in the RM’s or service team’s mailbox workflow as a lightweight copilot, focused on making inbound mail manageable and actionable.

What it does

The agent reads client emails coming into RM and servicing inboxes, understands what each message is about, and extracts the key tasks, data points, and intents. It distinguishes, for example, between “please change this address,” “where is my payment?”, “can I get a term sheet?”, and “I want to complain,” then surfaces the core request in a structured way and can trigger or pre-fill the right case in downstream systems. Instead of RMs wading through long threads, the agent presents each email as a concise request with suggested next action, so the human can confirm and move on.

Core AI functions

Its core capability is email understanding: classifying the type of request, extracting entities (accounts, products, amounts, dates), and turning unstructured text into a simple task or case description that systems and people can use. The emphasis is on robustness with real-world client email (informal language, forwards, replies), not on fancy generative text.

Problem solved

Without this agent, inbound email is a constant drag on RM productivity. Important client messages get buried, similar requests are handled in slightly different ways, and building a case from an email means copy-pasting text into CRM or ticketing tools. Over time, inbox overload leads to slower responses, missed follow-ups, and stressed RMs who spend a big chunk of their day on triage instead of value-adding work. The agent turns that mess of unstructured email into a manageable queue of clear tasks.

Business impact

The primary impact is higher RM throughput. Because triage and task extraction are largely automated, RMs and servicing staff can handle more client requests in the same time, respond faster, and spend more of their day on calls, meetings, and problem-solving rather than mailbox admin. That improves customer experience while also making better use of scarce RM capacity—especially in banks where one RM covers a large portfolio.

Integration and adjacent use cases

Integration complexity is low: the agent needs access to the relevant RM / servicing inboxes and a way to create or update cases in your CRM or servicing platform, then writes back short, structured task summaries and tags that humans can see and act on.

Common combinations in this Stand-alone Quick Win stack:

• Credit Score Improvement Assistant to automatically surface concrete “score improvement” suggestions when customers email about declines or limits;

• Alternative Risk Assessor to give RMs an augmented risk view on thin-file customers directly alongside the extracted email task;

• Customer Profiling & Product Recommender to suggest next-best products that fit the customer’s profile when emails open a sales conversation;

• Customer 360 Summary Generator to show an at-a-glance relationship snapshot next to the email so replies are better informed;

• Adverse Media Screening Agent to provide a quick adverse-news signal on higher-risk counterparties before responding or escalating; and

• Legal Contract Drafting Assistant to generate draft contracts, term sheets, or confirmation letters once an email-driven interaction turns into a concrete agreement.