Listen to this story
The finance sector has been grappling with significant data security issues, ethical concerns, and the complexities of complying with stringent regulations. Though AI offers solutions to these problems, the industry has been cautious about adopting it due to the sensitive nature of the data involved, potential ethical issues in AI-generated financial advice, and the overall challenge of navigating regulatory frameworks.
However, there is a gradual shift happening here.
When it comes to sifting through vast datasets, discerning current trends, and projecting potential shifts in market dynamics and customer behaviour, AI plays a crucial role. Fintech companies are discovering practical use cases for AI in these areas.
Subscribe to our Newsletter
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
“For a fintech entity like BharatPe, AI is instrumental in running credit-scoring models that predict credit eligibility, especially for people with limited or no credit history,” said Srivastava.
He also emphasised how these algorithms work into unconventional data, including expenditure on necessities versus luxuries, and may even analyse social media activity or online shopping habits to build a comprehensive profile of a borrower’s financial behaviour.
This comprehensive understanding of financial behaviour helps in making informed decisions about loan offers and determining the appropriate loan amount. Additionally, for BharatPe, AI facilitates the delivery of personalised offers and communication to customers, aligning with the contemporary demand for individualised services in the digital age.
This customer-centric approach represents a significant departure from the traditional product-centric approach of the past.
“We follow a multi-faceted approach, leveraging cutting-edge technologies and strategic methodologies to ensure the security and integrity of each transaction,” added Goel.
Inside BharatPe’s Data Science Team
BharatPe’s AI and analytics team, comprising 30 professionals, including 10 specialised in AI and ML, is pivotal in the organisation, contributing significantly to innovation and data-driven decision-making, involving close cooperation with data engineers, scientists, and domain experts, ensuring technical excellence aligns with business goals.
Goel explained the organisational structure of BharatPe’s tech team where the data science and AI/ML teams focus on developing and implementing advanced solutions, while the engineering and development teams handle tech infrastructure. Product Management ensures alignment with business objectives, and the Security Team ensures adherence to standards.
“We employ robust AI algorithms to analyse customer behaviour, identify patterns, and generate actionable insights for strategic decision-making, facilitating a deep understanding of customer purchasing patterns, identifying cross-selling and upselling opportunities and personalising our offerings,” said Srivastava.
BharatPe’s AI/ML-driven fraud and risk engine is adept at real-time prevention of anomalies and social engineering. Leveraging a diverse set of risk factors and social-behavioural parameters, the engine adapts autonomously to evolving threats.
Real-time feedback enhances the engine’s deep learning capabilities, enabling it to recognise and fight historical patterns in future transactions. He further added that agile development practices, along with collaboration with product and engineering teams, ensure scalability and robustness vital for real-world conditions serving millions of users.
“To stay ahead in this evolving landscape, we’ve established a dedicated research team focused on AI, investing in continuous upskilling of our tech teams,” he added.
“Our AI/ML success relies on robust collaboration, emphasising open communication and cross-functional teamwork. This approach harnesses collective strengths, leading to impactful AI/ML solutions with the potential to shape the future of fintech innovation,” said Goel.
BharatPe employs a dynamic tech stack for its payment processing, showcasing innovation in payment systems. The programming arsenal includes Java, Python, Node.js, and GoLang. Front-end development utilises React.js and Next.js for dynamic and responsive interfaces. Database management employs MySQL, PostgreSQL, MongoDB, AeroSpike, and Cassandra for structured and unstructured data.
“Scalability is addressed through a microservices architecture, Docker containerisation, and Kubernetes orchestration, hosted on AWS or Google Cloud. Apache Kafka facilitates secure microservices communication while the security is maintained through industry-standard coding practices and encryption,” added Goel.
Monitoring tools like Prometheus, Grafana, New Relic, Coralogix, Sentry, Pagerduty, and Loki ensure real-time insights. Caching is optimised with Redis, and horizontal scaling and load balancing achieve scalability. Jenkins and DevTron automate the continuous integration/deployment pipeline.
In high transaction volumes, efficient database management, query optimisation, caching, and distributed systems are key. BharatPe’s tech excellence ensures a secure, scalable, and efficient payment processing ecosystem.
Programming languages include Java for scalability, Python for agility, Node.js for dynamic backend, and GoLang for performance. Web frameworks like Spring Boot, Flask/Django, and Next.js provide robust foundations. Databases include MySQL, PostgreSQL, MongoDB, Redis for caching, and GCP BQ for efficient data warehousing.
Decoding BharatPe’s Generative AI Progress
In terms of how generative AI has transformed operations at BharatPe, Srivastava said that it improved their capabilities by identifying undiscovered trends and influencing their business landscape.
“Its adoption is expected to bring notable transformations to various departments in our company, giving rise to specialised units focused on generative AI,” said Srivastava. These units will play a vital role in restructuring procedures for customer service products and addressing data gaps through the collection of unstructured data from diverse sources, with the added benefit of minimising biases in data analysis.
“Leveraging cloud-based infrastructure facilitates the quick scalability of systems to accommodate business growth so the integration of ML outcomes directly informs real-time decision-making processes,” he added.
The team is currently experimenting with different generative AI tools for enhanced market analysis and a deeper understanding of customer behaviour. These tools play a crucial role in market forecasting, enabling proactive anticipation of trends and informed planning for new products or technologies.
[Update: 14th November 2023 10:30 |This article was updated to include Pankaj Goel’s quotes. The story has now been updated to reflect the changes.]