AI Customer Retention · RM Copilot
Reach the right customer before they decide to leave.
RetentionCopilot AI reads the everyday signals in a customer's banking activity — a stopped
salary, a shrinking balance, money moving to another bank — and ranks the customers a
relationship manager should attend to first. For each one, it explains why the risk is rising
and recommends a specific retention action to review.
What the copilot does
Three questions a relationship manager has to answer every week.
01 · Identify
Who is most at risk
Every customer is scored on churn risk, relationship value, and urgency, then ranked by a
single retention-priority score. The book is ordered so the highest-value customers with the
clearest warning signs rise to the top of the list.
02 · Explain
Why the risk is rising
Each score is broken down into the signals behind it — the salary that stopped, the balance
that fell, the card that has gone quiet. The reasoning is visible, so a manager can judge it
rather than trust a number on faith.
03 · Recommend
What to do next
For each customer the copilot drafts a retention action matched to the cause — a call, an
offer, a service fix — with talking points the manager can adapt. Every draft is a starting
point for a person to review, not a decision to send.
The retention loop
Better signals, acted on earlier, compound into value.
Retention is not a single moment. It is a loop a bank runs continuously — and small gains at
each step carry forward to the next.
01
Better signal detection
Quiet changes in behaviour are caught while there is still time to act on them.
02
Earlier RM intervention
The right manager reaches the right customer with context, before the relationship cools.
03
Stronger retention
More at-risk customers stay, and stay engaged, because the outreach fits the cause.
04
Higher lifetime value
Retained relationships deepen over time, lifting the value of the book the loop feeds back into.
What this MVP demonstrates
Four capabilities, shown end to end on synthetic data.
Early warning
Risk surfaced while it can still be changed
A deterministic scoring engine reads 120 synthetic customer profiles and flags rising churn
risk from real behavioural signals — not after the customer has already gone.
Explainability
Every score traces back to its reasons
Scores are built from named drivers a manager can read and challenge. The model supports the
decision; it does not hide it.
Actionability
From insight to a next step
Each customer comes with a recommended retention action and talking points, so the work moves
from knowing to doing within the same screen.
RM productivity
Attention spent where it matters
A ranked, explained book lets a relationship manager start the week with the few conversations
most likely to protect the most value.
Start here
See the book ranked by retention priority.
The dashboard opens on Sara Al-Rashid's Priority Banking book, ordered so the most important
conversation sits at the top.