AI-Powered Cross-Sell Intelligence & Business Analytics on AWS

Turning Customer Transactions into Cross-Sell Opportunities and AI-Powered Insights

Designed an AWS-based cross-selling intelligence solution for a banking use case that combined transaction analytics, executive dashboards, vector search and generative AI to help identify customers with untapped product opportunities, money moving to competing institutions, idle funds and other revenue-leakage patterns.

The real business problem

The project was built around a real banking cross-selling problem.

The objective was not simply to build a chatbot or dashboard. The bank wanted to understand where existing customers were using competing financial products or where their transaction behaviour indicated an opportunity for another product.

Examples included identifying customers who:

regularly transferred significant funds to other banks

appeared to maintain important financial relationships outside the bank

had direct-debit mandates going to competing institutions

had children-related transactions but no corresponding kids account with the bank

kept substantial funds idle instead of using investment or wealth-management products

appeared to maintain relationships with external fund managers

showed transaction patterns that could indicate an appropriate additional banking product

The central question was:

How can existing customer transaction data be turned into specific, explainable cross-selling opportunities?

What I worked on

I worked on the design of a data and AI solution that brought together:

transaction processing + customer analytics + cross-sell rules + GenAI + semantic retrieval + executive visualization

The solution processed customer transaction data, identified behaviours relevant to predefined cross-selling scenarios and prepared those insights for both analytical dashboards and AI-assisted exploration.

Data used in the project

We used anonymised and PII redacted banking transaction data rather than exposing real customer financial information.

Cross-sell intelligence layer

The project wasn't simply asking an LLM to "find opportunities."

The underlying transaction data was first processed and analysed so that specific business signals could be identified.

For example:

External-bank leakage

A customer repeatedly transferring substantial amounts to another bank could indicate that the customer maintains an important banking relationship elsewhere.

Potential action:

relationship-manager follow-up or targeted account/product engagement

Idle funds

A customer maintaining a large balance without deploying those funds into relevant investment products could represent a wealth or investment opportunity.

Potential action:

investment, fixed-income or wealth-management offer

Children's banking opportunity

Transactions associated with school fees, children's services or similar behavioural indicators could be combined with existing customer-product information.

If the customer had no children's account with the bank, the system could flag a potential opportunity.

Potential action:

kids/children's account recommendation

External fund-management relationship

Patterns suggesting payments to external investment or fund-management providers could reveal investment assets being managed outside the bank.

Potential action:

wealth-management or investment-product engagement

Direct-debit leakage

Recurring mandates involving external financial providers could reveal products or services that the customer currently obtains elsewhere.

Potential action:

identify an equivalent or complementary product that the bank can offer

AWS architecture

The architecture was designed approximately as:

Transaction Sources

↓ Amazon API Gateway

↓ Amazon S3 — Raw Data

↓ AWS Glue

↓ AWS Lambda — Transformation / Business Logic

↓ Amazon S3 — Curated Data

From the curated layer, the solution branches.

Analytics path

Curated Data → Amazon Redshift Serverless → Amazon QuickSight

This supports dashboards showing customer behaviour, cross-sell opportunities, transaction patterns and executive-level insights.

AI / RAG path

Curated Data → Amazon Bedrock → Embeddings → Amazon OpenSearch Service

Relevant enterprise/customer context can then be retrieved through semantic search and supplied to the foundation model.

Conceptually:

Amazon Bedrock ⇄ Amazon OpenSearch Service

This enables grounded AI responses rather than relying solely on the foundation model's general knowledge.

What the GenAI component adds

The dashboard answers questions such as:

How many cross-selling opportunities have been identified?

Which opportunity categories are most common?

Which customer segments show the highest leakage?

Where are the largest potential opportunities?

The GenAI layer goes further by allowing the user to investigate the data conversationally.

For example:

“Which customers show evidence of significant funds leaving for competing banks?”

or

“Explain why this customer was identified as an investment opportunity.”

or

“What cross-selling opportunities should relationship managers prioritize based on these transaction patterns?”

The RAG component provides relevant business context to the model so that responses can be grounded in the underlying enterprise information.

Executive analytics component

Amazon QuickSight provides the structured analytical side of the solution.

The dashboard layer can surface metrics such as:

Total customers analysed

Cross-sell opportunities identified

Opportunity type

Estimated customer value

Transaction leakage patterns

Customer/product relationships

Trend over time

Opportunity distribution

Customers requiring relationship-manager attention

This means executives get a high-level picture while business users can investigate specific opportunities in greater depth.

My role

AI & Data Solutions Engineer

My work involved translating the cross-selling business problem into an implementable AWS data and AI architecture.

This included working across:

business requirements

cross-selling use cases validation

data gap analysis

transaction-data ETL & design

data transformation

analytics architecture

Generative AI

vector-search architecture

Amazon QuickSight

business insight presentation

Technologies

Amazon Bedrock • Amazon OpenSearch Service • Amazon S3 • AWS Glue • AWS Lambda • Amazon API Gateway • Amazon Redshift Serverless • Amazon QuickSight • Python • SQL • Generative AI • RAG • Vector Search • Data Engineering • Business Intelligence

Project outcome

The project demonstrated how a bank could move from simply storing customer transaction data to actively using that information to uncover cross-selling and revenue-retention opportunities.

Transactions → Behavioural Signals → Cross-Sell Opportunities → Analytics → AI-Assisted Exploration → Business Action