Data & AI: Unleashing Hyper-Personalization at Scale in Financial Institutions

In the realm of financial services, data-driven strategies, such as AI, machine learning, analytics, and more, are spearheading the era of hyper-personalization at an unprecedented scale. This transformation is arguably the linchpin propelling product development and marketing to unprecedented heights of engagement and satisfaction.

Om Deshmukh, Head of Data Science and Innovation at Envestnet Data & Analytics, observes, “What truly marks a revolution is how these technologies have unearthed even deeper layers of hyper-personalization. Financial services firms have always had an abundance of data at their disposal, but now they can process it at scale, converting it into structured, tagged, and enriched data. This, in turn, accelerates innovative product development and enables pinpoint targeting down to the individual customer.”

Facilitating Innovative Use Cases Data-driven machine learning opens the door to a myriad of applications, from delivering personalized “safe-to-spend” notifications to analyzing a consumer’s retirement aspirations and offering tailored recommendations. It can identify users ready for a home loan or receptive to credit card upgrades.

At a broader level, Envestnet Data & Analytics collaborates with clients on diverse use cases. For example, detecting and analyzing inflation, identifying users likely to be adversely affected, and deciding which users warrant a cautionary message and which require leniency.

Deshmukh elaborates, “Think of it as a three-month loan repayment break, where the financial institution can proactively tell long-standing customers, ‘You’ve been loyal to us for a decade; we understand you might be facing challenges. Would you like to stagger your monthly repayments?’ This is a once-in-a-lifetime opportunity to show appreciation for your customer’s loyalty.”

Another client sought to target customers seeking post-pandemic support, discerning what promotional offers would be suitable. They identified potential targets, such as those likely to take long-delayed vacations and offering them credit cards with airline points or customers shifting from ordering in to dining out.

Data-driven insights empower financial institutions to compare themselves with peer groups or local and national macroeconomic conditions, establishing benchmarks. This proves crucial in situations like recent bank crises, enabling FIs to swiftly gauge their status in real time and make necessary adjustments.

Unleashing Data-Driven Innovation This surge in innovation results from the convergence of three factors. First, a staggering amount of data, estimated at approximately 328.77 million terabytes daily, with 120 zettabytes generated annually, is available. Financial institutions have reaped the rewards of this data boom.

Secondly, machine learning models, especially large language models, are no longer the exclusive domain of organizations with abundant resources. Access to pre-trained models has been democratized, making them accessible to all.

The third factor is the accessibility of the computational power needed to run these models. Cloud computing has made it affordable and attainable for companies to utilize these models without concerns about computational barriers or costs.

Experienced data and AI partners become indispensable, aiding leaders in defining their use case objectives and navigating the intricate world of data types and availability. They offer mature end-to-end ML systems, sophisticated engineering setups, access to diverse and voluminous data required for personalized insights, and robust privacy and security measures to ensure customer confidence in acting on those insights.

Data Diversity and Bias Deshmukh underscores their commitment to leveraging machine learning to create data-driven products while rigorously addressing data diversity, bias, irrelevance, and anomalies. Ensuring that machine learning algorithms have access to a wide array of data sources is crucial. Relying on a limited set of sources that do not represent the user base or use cases can introduce substantial bias.

Stratified sampling, a method where data is sampled across various dimensions specific to each financial institution, enhances the models’ generalization capabilities by drawing from a diverse dataset.

Data enrichment plays a dual role, eliminating the “garbage-in, garbage-out” issue and adding essential customer context to transactions. Each financial transaction adds another piece to the customer’s financial portrait, enabling FIs to uncover new personalization opportunities, precise targeting, data-driven lending strategies, and more.

For further insights into valuable use cases and navigating the intricate landscape of data types and availability, explore the VB Spotlight.