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Machine Learning on Banking: Data Scientist Facilitates New Era of Banking Innovations The US banking technology has seen significant developments, with a strong focus on digital transformation to address evolving economic challenges and consumer demands.

By Rohan Goyal

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A shift towards automation, personalisation, and security in the digital banking era is redefining the industry.

The US banking technology has seen significant developments, with a strong focus on digital transformation to address evolving economic challenges and consumer demands. A key industry trend is the rapid adoption of machine learning (ML), particularly generative artificial intelligence, moving from traditional workflows to increase productivity and modernise banking services.

Central to this transformation is the essential role of data scientists. They are pioneering and simplifying ML integration into banking, facilitating a new era of financial innovations. Among these innovators is Senior Data Specialist Ranjeeta Bhattacharya.

Bhattacharya's expertise is evident from her academic background, including a Computer Science and Engineering undergraduate degree and a Master's in Business and Science (Data Analytics/Information Technology) from Rutgers University-New Brunswick. Her 15 years in analytics and technology roles have equipped her with a thorough understanding of banking systems and the effective use of machine learning.

Banking in Today's Modern World

A shift towards automation, personalisation, and security in the digital banking era is redefining the industry. Advanced algorithms enhance banking security by detecting fraud through real-time transaction patterns and historical data analysis. Bhattacharya states this is crucial for maintaining customer trust.

Furthermore, ML excels in refining credit risk assessments, enabling more precise lending decisions. Such streamlines the credit process and broadens credit access, especially for traditionally underserved communities.

However, Bhattacharya emphasises that personalisation and automation are central to this digital transition. ML's ability to analyse vast customer data allows for tailored financial advice and product recommendations, elevating customer service to new levels of customisation. At the same time, automating tasks like document verification and customer inquiries reduces errors and enhances profitability.

Bhattacharya Leading Banking Innovation

With more than a decade of experience spanning roles from software development to project management, Bhattacharya brings a comprehensive skill set to the banking sector. Her work at the world's largest custodian bank primarily focuses on its end-to-end solution development within its AI hub wing.

She safeguards that the latest technological developments, from inception to launch, are well executed and align with the bank's objectives. This involves developing, testing, refining, and integrating algorithms into existing systems.

The data scientist explains how her role is fundamentally data-driven. She analyses vast data sets to identify patterns, anomalies, and insights that inform decision-making and enhance customer experience. She also contributes to improving security measures. Her approach to complex issues like fraud detection and customer service optimisation involves applying her cognitive skills to develop effective AI/ML solutions, ensuring the technologies are advanced and relevant to the bank's needs.

"Everything starts with bridging the gap between technology and business needs. This ensures every ML solution delivers real value," Bhattacharya explains.

A Continuous Pursuit of Banking Excellence

Despite the progress in ML integration, Bhattacharya acknowledges the remaining considerable work. The banking industry is continuously changing, driven by technological advancements, regulatory changes, and shifting customer expectations. Being an ML innovator requires her to stay abreast of these changes, keep pace with the industry's evolution, and anticipate future trends.

Moreover, Bhattacharya's task extends beyond developing cutting-edge ML solutions. It includes simplifying these technologies for practical business applications. She turns ML techniques into actionable insights and strategies banks can implement to enhance their operations, customer service, and product offerings.

By bridging the gap between advanced ML research and real-world banking applications, Bhattacharya enables US banks to remain relevant and competitive in the global financial landscape, marked by high-level operational efficiencies and top-tier customer services.


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