FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications
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.
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Dwipam Katariya
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2025-11-21T09:00:45.731Z
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Dwipam Katariya
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user
dwipamc1
691f45cb6d58f71c91487f3b
Snehita Varma
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Akshat Shreemali
691f45cb6d58f71c91487f3d
Benjamin Wu
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Kalanand Mishra
691f45cb6d58f71c91487f3f
Pranab Mohanty
FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications
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.
FinTRec, a transformer-based framework, addresses challenges in financial services recommendation systems by handling long-range interactions and multiple products, outperforming traditional tree-based models.
transformer-based architectures sequential recommendation systems long-range user interactions temporally heterogeneous context interrelated products coordinated models FinTRec tree-based models explainability regulatory requirements product adaptation cross-product signal sharing training cost technical debt offline performance