Banking on Attention: Personalizing Finance with Transformers

Author: Denis Avetisyan

A new framework leverages the power of sequential data modeling to deliver more relevant advertising and services in the financial sector.

$Sequential user representations, $Su^\hat{S\_{u}}$, are constructed by processing dynamic context ($FdF\_{d}$) and clickstream sequences ($SuS\_{u}$), and then temporally aligned with both static features ($FsF\_{s}$) and field-modulated embeddings ($FfmF\_{fm}$) to create a comprehensive user profile.$

Sequential user representations, $Su^\hat{S\_{u}}$, are constructed by processing dynamic context ($FdF\_{d}$) and clickstream sequences ($SuS\_{u}$), and then temporally aligned with both static features ($FsF\_{s}$) and field-modulated embeddings ($FfmF\_{fm}$) to create a comprehensive user profile.

FinTRec unifies contextual ad targeting and personalization using transformer models, multi-channel data, and cross-product learning for improved low-latency inference.

While tree-based models have long dominated financial service applications due to their interpretability, they often struggle to capture the complex temporal dynamics inherent in user behavior. This paper introduces FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications, a novel framework leveraging transformer architectures to model multi-channel user interactions for improved ad targeting and product recommendations. Our results demonstrate that FinTRec consistently outperforms production-grade tree-based baselines through unified sequential modeling and cross-product learning. Could this represent a paradigm shift towards more sophisticated, transformer-based solutions in the highly regulated financial sector?

The Disconnect Between Institutional Objectives and Genuine User Needs

Conventional recommendation systems in financial services frequently prioritize institutional objectives over authentic user requirements, creating a fundamental disconnect. These systems are often optimized for metrics like click-through rates or immediate product placement, rather than focusing on a client’s long-term financial wellbeing. This emphasis on short-term gains stems from the inherent pressures within financial institutions to demonstrate quarterly results and maximize revenue. Consequently, recommendations may lean towards products with higher commissions or greater institutional profit margins, even if those products aren’t the most suitable options for the individual user’s circumstances. The result is a diminished sense of trust and a failure to cultivate lasting customer relationships, as clients perceive a misalignment between the advice they receive and their personal financial goals.

Financial institutions frequently operate with fragmented data, treating aspects of a user’s financial life – such as savings, investments, and debt – as isolated entities. This siloed approach prevents a holistic understanding of individual circumstances and goals, resulting in recommendations that may be technically correct but ultimately misaligned with a user’s broader needs. For example, an investment suggestion might appear profitable in isolation, yet conflict with a pre-existing debt repayment plan or long-term savings objective. Consequently, users encounter disjointed experiences, receiving advice that feels impersonal or even counterproductive, hindering their ability to make informed decisions and build lasting financial well-being. The inability to synthesize a complete financial portrait diminishes the effectiveness of financial tools and fosters a sense of disconnect between institutions and their customers.

Current financial recommendation systems frequently emphasize immediate engagement, such as click-through rates, at the expense of fostering sustained customer relationships. This prioritization of short-term gains often manifests as promoting products generating quick revenue, even if they don’t align with a user’s long-term financial wellbeing. Consequently, customers may perceive these recommendations as self-serving rather than genuinely helpful, leading to diminished trust and reduced loyalty. This focus on immediate reward neglects the potential for building lasting value through guidance that supports financial growth over time, ultimately hindering the development of robust, customer-centric financial strategies and impacting long-term conversions.

This visualization demonstrates a user's multi-faceted journey across various channels, products, and webpages. — This visualization demonstrates a user’s multi-faceted journey across various channels, products, and webpages.

FinTRec: A Transformer-Based Framework for Holistic Financial Modeling

FinTRec utilizes the Transformer architecture, a deep learning model originally developed for natural language processing, to improve the modeling of sequential user interactions. Unlike recurrent neural networks (RNNs) traditionally used in recommendation systems, Transformers employ self-attention mechanisms that allow the model to weigh the importance of different interactions within a user’s history, regardless of their position in the sequence. This capability enables FinTRec to capture long-range dependencies and complex patterns in user behavior more effectively. The Transformer’s parallelization capabilities also significantly reduce training time compared to sequential models like RNNs, facilitating faster iteration and experimentation. By processing the entire interaction sequence simultaneously, FinTRec achieves a more holistic understanding of user preferences and intent, leading to improved recommendation accuracy.

The FinTRec framework utilizes a Proprietary Foundational Model (FM) to create comprehensive user profiles. This FM ingests both static contextual data, such as user demographics including age, location, and stated preferences, and dynamic contextual data derived from transaction history-encompassing purchase amounts, frequencies, categories, and timestamps. These data sources are combined to generate point-in-time representations, effectively capturing a user’s evolving state at any given moment. The resulting embeddings are designed to provide a more nuanced understanding of user behavior than traditional methods relying solely on sequential interactions or static attributes, allowing for improved recommendation accuracy and personalization.

FinTRec builds upon existing Sequential Recommendation Systems by integrating both static and dynamic user context into a unified modeling process. Traditional sequential models primarily focus on the temporal order of user interactions; however, FinTRec augments this with static features like user demographics and continually updated dynamic features derived from transaction history. This allows for a more holistic representation of user state at any given point in time, enabling the model to capture nuanced behavioral patterns beyond simple sequence analysis and ultimately improve recommendation accuracy by considering a broader range of influencing factors.

Multi-Objective Optimization and Attention Mechanisms for Enhanced Value

FinTRec utilizes Multi-Objective Optimization (MOO) as a core component of its recommendation strategy, addressing the inherent trade-offs between user engagement and business outcomes. Traditional recommendation systems often prioritize a single metric, such as click-through rate (CTR), potentially sacrificing conversion rates (CVR). MOO, however, allows the model to simultaneously optimize for both CTR and CVR, treating them as equally important objectives. This is achieved through a Pareto-optimal approach, identifying a set of solutions where improving one objective does not necessitate a reduction in the other. The framework defines a combined reward function that balances both metrics, enabling the model to learn a policy that maximizes overall business value, rather than focusing on isolated user interactions.

The FinTRec framework utilizes an Attention Mechanism to improve model performance by selectively weighting input features during processing. This mechanism assigns varying degrees of importance to different user signals and product characteristics, enabling the model to concentrate on the most predictive elements for each individual recommendation. Specifically, the attention weights are learned during training, allowing the model to automatically identify and prioritize features that contribute most significantly to accurate predictions. This focused approach contrasts with traditional methods that treat all features equally, and facilitates more nuanced and effective personalization by dynamically adjusting feature representation based on contextual relevance.

FinTRec’s enhanced cross-product awareness capabilities enable the identification of relevant financial products for users, leading to increased personalization. Offline simulations demonstrate this results in a statistically significant lift of up to 41.50% in predicted Present Value (PV). This improvement is achieved by the model’s ability to consider a user’s broader financial profile and identify product offerings beyond their immediately expressed needs, maximizing potential value for both the user and the financial institution.

Efficient Adaptation Through Low-Rank Decomposition

FinTRec employs Low-Rank Adaptation (LoRA) to mitigate the substantial computational demands associated with fine-tuning large language models. LoRA freezes the pre-trained model weights and introduces trainable low-rank decomposition matrices into each layer of the Transformer architecture. This approach drastically reduces the number of trainable parameters – typically by over 90% – compared to full fine-tuning, thereby lowering both computational cost and memory requirements. By focusing training on these smaller, low-rank matrices, FinTRec maintains performance while significantly accelerating the fine-tuning process and enabling its application with limited computational resources.

Low-Rank Adaptation (LoRA) mitigates the computational demands of fine-tuning large models by freezing the pre-trained model weights and introducing trainable low-rank decomposition matrices. This approach dramatically reduces the number of trainable parameters, typically by orders of magnitude compared to full fine-tuning. Consequently, LoRA enables significantly faster training times and reduces the required GPU memory and storage, making it feasible to adapt large models on resource-constrained hardware. The reduction in trainable parameters does not substantially impact performance, as demonstrated by FinTRec achieving a +24.21% increase in Recall@1 with LoRA, nearing the +26.85% achieved by full fine-tuning.

Evaluation of FinTRec demonstrates a Recall@1 improvement of +24.21% when utilizing Low-Rank Adaptation (LoRA). This performance is notably close to that achieved by full fine-tuning (F-FT), which yields a maximum Recall@1 increase of +26.85%. These results indicate that LoRA provides a computationally efficient alternative to full fine-tuning, maintaining a high level of performance in recommendation tasks with only a marginal decrease in Recall@1.

The FinTRec framework demonstrates a substantial improvement in model performance, achieving a 55.38% reduction in log loss when compared to baseline models. Log loss, a standard metric for evaluating the performance of classification models, directly reflects the accuracy of probability predictions; a lower log loss indicates better predictive capability. This reduction signifies a significant enhancement in the framework’s ability to accurately predict user preferences and item relevance, indicating a more efficient and effective recommendation system. The measured decrease in log loss provides quantitative evidence of the framework’s overall effectiveness and its capacity to outperform existing approaches.

Towards a Future of Empathetic and Proactive Financial Services

FinTRec signifies a notable advancement in financial service personalization, moving beyond simple demographic targeting to cultivate deeper, more resilient customer relationships. The framework achieves this by integrating a comprehensive array of data – encompassing transaction history, investment preferences, and even stated financial goals – to generate uniquely tailored recommendations and services. This holistic approach isn’t merely about suggesting products; it’s about understanding individual financial lives and proactively offering support, ultimately increasing customer loyalty and driving sustainable, long-term value for both the consumer and the financial institution. By prioritizing individual needs and fostering trust, FinTRec demonstrates a pathway towards a future where financial services are truly centered around the people they serve.

The FinTRec framework distinguishes itself by moving beyond fragmented customer profiles, instead consolidating data from traditionally disparate sources – including transaction history, investment portfolios, social media activity, and even real-time behavioral data. This unified view enables a fundamentally different approach to financial service design. Rather than optimizing for isolated metrics like individual product sales, the framework prioritizes holistic objectives, such as long-term financial wellness or achieving specific life goals. Consequently, opportunities for innovation surge; financial institutions can now develop hyper-personalized products and proactively deliver services tailored to an individual’s evolving needs and circumstances, fostering stronger customer relationships and ultimately, driving sustained value. This shift facilitates not just better financial products, but a more empathetic and effective service experience.

The principles underpinning FinTRec’s success are not limited to financial services; its adaptable framework promises advancements across numerous sectors. By prioritizing the unification of disparate data and optimization for comprehensive user goals, this approach facilitates the creation of highly personalized experiences in fields like healthcare, education, and even urban planning. Imagine customized learning paths tailored to individual student needs, proactive healthcare interventions based on holistic patient profiles, or smart cities dynamically adapting to citizen preferences – all powered by the same core methodology. This potential for broad application signifies a shift towards intelligent systems that anticipate and respond to individual requirements, ultimately fostering more effective and user-centric solutions across a diverse range of industries and fundamentally changing how services are delivered.

The pursuit of a unified framework, as demonstrated by FinTRec, echoes a fundamental principle of computational elegance. Ken Thompson once stated, “Sometimes it’s better to be clever than consistently correct.” While FinTRec prioritizes consistent accuracy through its transformer-based sequential modeling of multi-channel data, the very ambition to consolidate advertising and servicing into a single system represents a ‘clever’ simplification. The model’s focus on low-latency inference and cross-product learning isn’t merely about achieving better performance metrics; it’s about establishing a predictable, provable system for financial personalization-a testament to the inherent beauty of a well-defined algorithm.

Beyond the Horizon

The presented framework, while demonstrating efficacy in sequential modeling for financial applications, merely skirts the true challenges inherent in predictive systems. The performance gains achieved through cross-product learning, while statistically significant, are bounded by the limitations of the training data itself. A truly robust system necessitates a departure from purely observational learning; the incorporation of causal inference methods represents a logical, though mathematically arduous, next step. The current focus on low-latency inference, while practically necessary, should not eclipse the pursuit of algorithmic elegance – a reduction in computational complexity achieved through mathematical refinement, not simply clever engineering.

Furthermore, the reliance on multi-channel data introduces the perennial problem of feature selection and noise reduction. While transformers excel at pattern recognition, they are not immune to the curse of dimensionality. Future research must address the problem of disentangling spurious correlations from genuine predictive signals. A mathematically rigorous approach to feature importance, moving beyond empirical observation, is paramount.

Ultimately, the pursuit of personalized financial services through machine learning is not about achieving incrementally better predictions. It is about constructing a provably correct model of financial behavior – a daunting task, perhaps, but one that demands a commitment to mathematical purity above all else. The field must strive for solutions that are not merely effective, but right.

Original article: https://arxiv.org/pdf/2511.14865.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/