FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications

TL;DR

FinTRec introduces a transformer-based framework for financial services that effectively handles complex, heterogeneous user interactions and multiple products, outperforming traditional tree-based models in real-time recommendations.

Contribution

This paper presents the first comprehensive study of a unified transformer-based recommendation model tailored for financial services, addressing technical and business challenges.

Findings

FinTRec outperforms tree-based baselines in simulations and live A/B tests.

The unified architecture enables cross-product signal sharing and reduces training costs.

Fine-tuning improves offline performance across multiple financial products.

Abstract

Transformer-based architectures are widely adopted in sequential recommendation systems, yet their application in Financial Services (FS) presents distinct practical and modeling challenges for real-time recommendation. These include:a) long-range user interactions (implicit and explicit) spanning both digital and physical channels generating temporally heterogeneous context, b) the presence of multiple interrelated products require coordinated models to support varied ad placements and personalized feeds, while balancing competing business goals. We propose FinTRec, a transformer-based framework that addresses these challenges and its operational objectives in FS. While tree-based models have traditionally been preferred in FS due to their explainability and alignment with regulatory requirements, our study demonstrate that FinTRec offers a viable and effective shift toward transformer-based architectures. Through historic simulation and live A/B test correlations, we show FinTRec consistently outperforms the production-grade tree-based baseline. The unified architecture, when fine-tuned for product adaptation, enables cross-product signal sharing, reduces training cost and technical debt, while improving offline performance across all products. To our knowledge, this is the first comprehensive study of unified sequential recommendation modeling in FS that addresses both technical and business considerations.