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Fraud Analytics

Unveiling the Cross-Selling Potential among High-Value Banking Customers


Institutions continually seek innovative ways to maximize their revenue streams in the fast-paced world of banking and financial services. One of the most effective strategies is identifying affluent customers within their existing customer base and targeting them for cross-selling and upselling opportunities. In this blog, we will explore the concept of income estimation models. This powerful tool enables banks and financial institutions to identify high-value customers and tailor their services accordingly.

The Quest for Affluent Customers

Our story begins with a leading consumer bank eager to enhance revenue from its existing customer base. This institution recognized the potential of targeting affluent customers for various financial products and services. The goal was clear: identify these high-value customers and offer them personalized solutions that align with their financial needs and goals.

The Approach: A 360° Customer View

To embark on this journey, the bank collaborated with TransOrg, a data analytics and machine learning company, to develop a comprehensive understanding of its customer base. The first step was to create a 360° customer view, a holistic approach that involved analyzing personal information, transactional data, account history, assets, liabilities, and even income data sourced from various channels such as credit bureaus, Equifax reports, credit card transactions, and bank records.

Machine Learning to the Rescue

With a wealth of customer data, TransOrg employed advanced machine learning techniques to build income estimation models. These models were designed to serve as a baseline for estimating customer wealth and, more importantly, to accurately predict their actual income with a remarkable 89% accuracy rate.

The Fruit of Labor: Affluent Customer Identification

The results of this collaboration were nothing short of impressive. The income estimation models enabled the bank to categorize customers into segments, focusing on identifying affluent individuals. Here are some key outcomes:

Prospective Affluent Customers: The income estimation models pinpointed a future base of 2,000 very affluent customers, each with a net worth exceeding $1 million.

High Net Worth Individuals (HNIs): The bank identified approximately 38,000 high net worth customers (HNIs) with a net worth exceeding $500,000.

Customer Tiering: The bank used the data to segment customers based on wallet share and wealth, allowing personalized offerings tailored to each customer’s financial situation.

Personalized Campaigns: Armed with this newfound knowledge, the bank launched customized marketing campaigns, ensuring the right product was presented to customers.

TransOrg’s Role in the Success Story

In the ever-competitive world of banking and financial services, the ability to pinpoint high-value customers and offer them tailor-made solutions is a game-changer. TransOrg played a pivotal role in this success story, enabling the bank to harness the power of data and machine learning to identify potential affluent customers.
Through TransOrg’s income estimation model, the bank uncovered 40,000 potential affluent customers, a remarkable achievement that opened the door to significant revenue growth. The predictive accuracy of 89% ensured that the bank’s resources were efficiently allocated to the right customers, maximizing the return on investment for marketing efforts.


The journey to identifying affluent customers through income estimation models is a testament to the power of data analytics and machine learning in the banking and financial services industry. By leveraging these tools, banks can uncover hidden opportunities, improve customer segmentation, and offer personalized solutions, ultimately boosting their revenue and staying ahead of the competition.
In the case of our leading consumer bank, partnering with TransOrg and implementing income estimation models led to the identification of 40,000 potential affluent customers. This achievement is a testament to the transformative potential of data-driven insights and their role in shaping the future of banking and financial services.

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