Every day, new developments in artificial intelligence and machine learning generate an increasing amount of data. For finance and investment professionals, navigating this data-rich environment is challenging, and the costs of mistakes are high. That’s why the industry of data analytics and visualization is expanding at an extraordinary pace.

A few years ago, my approach to data analysis was completely transformed by Atmajitsinh Gohil’s R Data Visualization Cookbook, which offers over 80 “recipes” for data visualization with R programming language.

Published in 2015, this book has been used by students of data analytics, journalists, designers, and policymakers alike. However, the landscape is evolving rapidly.

I spoke with Atmajitsinh Gohil to discuss how technology is revolutionizing data management, particularly in finance.

Data’s rapid growth

With AI and ML creating new datasets that previously didn't exist, the challenge lies in continuously developing skills to visualize this data effectively.

According to Gohil, the tools available often lag behind the advancements in technology. That’s why finance professionals should be innovative in their approach to data representation and analysis.

AI's impact on financial applications

“In the banking sector, the application of AI is still in its nascent stages,” Gohil said. “Banks are cautious about adopting new technologies due to concerns over data security and client privacy.”

However, the finance industry is starting to explore AI’s potential in various areas. Gohil mentions internal AI projects aimed at text summarization, automating report generation, and predictive analysis of stock performance.

Many banks are also developing in-house AI models versus relying on OpenAI or other external AI products.

“The effectiveness of these models heavily depends on the quality of the data provided,” Gohil notes. “If a bank's data is biased or poorly maintained, the resulting models will likely underperform.”

Use in fraud detection

Data visualization plays a pivotal role in fraud detection. Gohil explains that machine learning models identify fraud by analyzing vast datasets, even though the proportion of fraudulent data is typically small.

Visualization techniques help create dashboards that display login patterns, demographic information, and fraud occurrences, enabling banks to tweak and improve their fraud detection models.

"Fraudsters adapt very quickly," Gohil warns, emphasizing the importance of being prepared for new threats and implementing robust security measures.

Developing proprietary AI

The need for concise and actionable data presentation has never been greater, said Gohil. "The rate at which we are producing data has increased," he explains. Visualization tools help make sense of this data, allowing for informed decision-making and efficient analysis.

As the finance sector navigates the complexities of AI and ML, the continuous development of innovative visualization techniques is crucial in strategy and decision making.

However, the biggest challenge and opportunity for banks lie in developing proprietary AI, where they face tough competition.

OpenAI models benefit from training on publicly available data, from YouTube to news articles, which banks cannot use due to privacy concerns.

The future of data visualization

Gohil sees the data visualization industry expanding in tandem with AI and ML, development of new visualization techniques, and tools specifically designed for large-scale datasets used by AI models.

"It would be interesting to see data visualization tools that explain how AI models operate at different stages," Gohil notes.

Such tools would make it easier for banks to understand and adopt new technologies, potentially leading to broader acceptance and application.

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