Historically, banks have struggled to create well-defined boundaries for “affluent” clients, leading to inconsistent acquisition and servicing strategies. Within the same institution, it’s not uncommon to encounter varying definitions of what constitutes an affluent client. This discussion defines three distinct sub-segments of affluent clients: emerging affluent ($150k–$250k in assets), mass affluent ($250k–$1m) and high net worth ($1m–$5m). Clients with over $5 million, usually serviced by a private bank or wealth management team, are outside the scope of this discussion.
Traditional segmentation often relies on static “on-us” asset thresholds that overlook consumers with wealth spread across multiple institutions. Identifying all members of an affluent household has also posed a thorny challenge. To capitalize, banks need to leverage predictive analytics and transactional data — such as credit card spending, mortgages and payroll activity — to identify existing affluent clients and detect emerging affluent clients earlier in their wealth journeys.
Banks also should leverage the vast amounts of proprietary consumer data in their possession and integrate disparate data sources to gain real-time insights. Transaction histories, cash flow patterns, product usage and digital behavior provide visibility into emerging wealth trajectories. Major life stage events — like buying a home, getting married, forming a business, rolling over a 401(k) or receiving an inheritance — are clear signals that a client needs guidance. But small behavioral cues, such as a gradual balance runoff, a one-time $25,000 transfer to an outside brokerage or a sudden change in bill pay rhythm, can also offer an opportunity to deepen a relationship.
Once banks identify potential affluent clients, artificial intelligence (AI) becomes essential to scale personalized engagement. Machine learning models that blend internal transactions with external data can spot patterns the moment they occur. Still, the insight matters only when it flows automatically from the marketing platform into a customer relationship management (CRM) platform. If a banker sees a prompt that a client visited the mortgage page three times in a week, the banker can call with relevant advice, turning raw data into a timely conversation that builds trust, expands share of wallet and reduces attrition.
Gen Z and younger millennials — many of whom are or soon will be beneficiaries of recent wealth transfers — represent the market’s fastest-growing segment. Banks should be mindful that this younger segment favors digital platforms for investment management and expects a seamless, integrated experience. To win their loyalty, banks must deliver intuitive, one-stop platforms that seamlessly combine banking and investing, powered by digital tools and human advice.
By moving beyond static, asset-based segmentation, banks can proactively engage future high-value clients — ultimately winning their loyalty and increasing share of wallet.