Integrating Artificial Intelligence with Product Innovation:
Enhancing User Engagement and Transactions at PicPay
Yan Werneck
yan.almeida@picpay.com
PicPaySão Paulo, Brazil
Federal University of Rio de Janeiro
Rio de Janeiro, Brazil

ABSTRACT
AI models have become essential within financial companies, especially fintech firms, by significantly enhancing user experience anddriving a wide range of transactions, including purchases, payments,investments, loans, and savings activities [1]. At PicPay, a Brazilian
fintech with over 35 million active users [2], the recommendationteam has successfully integrated machine learning with productdesign and user communication to generate contextually relevant
algorithms. This integration has enabled personalized recommendations throughout the user journey across various touchpoints in
the PicPay app, resulting in more than 92% of users engaging withrecommended content each month.
In this talk, we share our insights from leading the recommendation team at PicPay, highlighting the collaborative efforts with design, product, and business teams. By integrating machine learningalgorithms and A/B testing at scale, and through effective negotiation and cross-team collaboration, we have achieved significant
improvements in the recommendation system and overall user engagement. Over several months, these strategies have led to very
positive results, demonstrating the power of aligning technicalinnovation with strategic business objectives.

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POTENTIAL DISCUSSION POINTS• Cross-Team Collaboration for AI Integration: Strategies andbest practices for effective negotiation and collaboration
between design, product, and business teams to successfully
integrate AI models and achieve business objectives.
• Scaling Machine Learning and A/B Testing: Techniques for
deploying machine learning algorithms and conducting A/B
testing at scale to enhance personalized recommendations
and user engagement in a large fintech application.
• Enhancing User Experience with Contextually Relevant
Algorithms: Approaches for developing and implementing
machine learning algorithms that generate personalized
and contextually relevant recommendations across various
touchpoints within a fintech app, resulting in high userengagement.

2RELEVANCE TO THE WORKSHOPThis case study is highly relevant for it showcases the practical application of AI models within the financial sector, particularly in fintech firms, demonstrating how these technologies can significantly
enhance user experience and drive a wide range of transactions.
The example of PicPay illustrates how machine learning can beeffectively integrated with product design and user communication
to create contextually relevant algorithms.
Additionally, it highlights the importance of cross-disciplinary
collaboration. By working closely with design, product, and business teams, the recommendation team at PicPay successfully negotiated and implemented machine learning algorithms and A/B
testing at scale. This collaborative effort underscores the need for
effective teamwork and communication in deploying AI solutions.
Furthermore, this work provides concrete evidence of the positive impact of aligning technical innovation with strategic businessobjectives. The significant improvements in the recommendation
system and overall user engagement achieved at PicPay demonstrate the tangible benefits of such alignment, making this casestudy a valuable learning opportunity for the machine learning
community.

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ABOUT THE PRESENTER

Yan Werneck is a seasoned data scientist and the Head of Recommendation and Search Systems at PicPay, one of Brazil’s leading
fintech companies. He holds a Bachelor’s degree in Statistics fromthe Federal University of Rio de Janeiro, which laid the foundation
for his expertise in data analysis and machine learning.
In addition to his professional role at PicPay, Yan is also a passionate educator. He teaches Statistics and Machine Learning fordata science disciplines in an online postgraduate course at Descomplica. His teaching aims to bridge the gap between theoreticalknowledge and practical application, equipping students with theskills needed to excel in the data science field.
The company. Founded in 2012, PicPay is a brazilian fintech thatwas born to make life easier by reinventing the way people deal withmoney, shop and communicate. In 2015, the company was investedby the J&F group, one of the largest business conglomerates in Brazil.
PicPay is a two-sided ecosystem that offers products and services
to consumers and sellers to address all their needs, from day-today payments, financial services to investments and shopping, in asimple, easy and secure way.

REFERENCES[1] Saqib Aziz, Michael Dowling, Helmi Hammami, and Anke Piepenbrink. 2022.
Machine learning in finance: A topic modeling approach. European Financial Management 28, 3 (2022), 744–770. https://doi.org/10.1111/eufm.12326
arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/eufm.12326
Picpay tem primeiro lucro anual e chega a 35[2] Álvaro Campos. 2024.
milhões de clientes ativos. Valor Econômico (27 March 2024).
Available at: https://valor.globo.com/financas/noticia/2024/03/27/picpay-tem-primeirolucro-anual-e-chega-a-35-milhes-de-clientes-ativos.ghtml (Accessed: June 5th,2024).