Ardhendu Sekhar Nanda is a renowned Fintech expert in Treasury data services, Treasury management and Market data, boasting two decades of extensive experience in the financial services and technology sectors. He has played a pivotal role in global corporations in successfully implementing Fintech solutions across various banking verticals. Ardhendu has proficiently navigated and addressed complex compliance and risk challenges using tech-enabled solutions. His work has been instrumental in bridging the gap between technological advancements and business imperatives, earning him recognition as a thought leader and innovator.
Ardhendu holds a Bachelor degree of Engineering with specialization in Electrical and instrumentations stream and is currently undergoing master’s degree in business and data analytics, complemented by specialized IT certifications. His thought leadership is further evidenced by his numerous publications on Fintech and Banking in reputed journals. He has also served on program committees, editorial boards and conducted peer reviews for reputed journals. He plays active role as jury member for reputable award organizations in Technology and Business which include Brandon Hall Group, Finnovate, Globee Awards and others.
Ardhendu Nanda is an eminent member of the Forbes Technology Council, and a distinguished Senior Member of IEEE. He has been awarded withPrestigious 2024 Global Recognition Award as well as prominent publication houses such as Digital Journal, International Business Times, and TechBullion have featured Ardhendu’s insights on various topics for their readership.
Currently Ardhendu Sekhar Nanda holds a Vice President Position with First Citizens bank where he is responsible to bring technology transformation in Treasury data operations. In this interview with our Global Banking & Finance Review team, he discusses what is Gen AI and the impact of Real-Time Data Processing on Generative AI Trading Models.
1.What is Generative AI and its role in Online trading?
Generative AI is one of the branches of artificial intelligence, and it implies creating new, similar data based on its training set. The models based on this technology include Generative Adversarial Networks (GANs),and Variational Autoencoders (VAEs).Bothuse real and simulated data to create data that looks more and more realistic.
Generative AI is used in online trading to analyze large historical market data, identifying patterns and trends. It enables the generation of synthetic data points, making the trading models more profound and precise. Such a feature allows better market predictions and strategy creation. In addition, generative AI can continuously learn and adjust to new data, which allows better predicting of market shifts. By doing so, traders can identify currently hidden patterns, elaborate more sophisticated trading strategies, and react more quickly to market shifts.
2.What is Real-Time Data Processing and how it is essential in Online Trading?
Real-time data processing analyzes data as it’s generated, crucial in online trading for instant decision-making, enhanced accuracy, and effective risk management. It provides traders with a competitive edge by allowing quick responses to market changes and dynamic strategy adjustments based on the most current market conditions.
3. What is the impact of Real-Time Data Processing on Generative AI Trading Models?
The integration of real-time data significantly boosts the accuracy and effectiveness of generative AI trading models.
Generative AI models equipped with real-time data processing facilitate dynamic decision-making by continuously analyzing and interpreting live market data. This provides traders with immediate insights into market volatility, customer sentiment, and emerging trends.
Incorporating real-time data processing into generative AI trading models enhances risk assessment and management.
Real-time data processing empowers generative AI trading models to uncover unique patterns and maximize profitability. By identifying subtle correlations between market factors, these models enable traders to optimize their strategies.
4. What are some of the benefits of Real-Time Data Processing ?
Traders benefit from real-time data in that they can make decisions based on the most current information. Such information is also very accurate since it captures the current market trends and market conditions.
Traders benefit from instantaneous updates, allowing them to react swiftly to market changes. This enables the execution of trades at the most opportune moments, maximizing profitability and minimizing risks.
Traders can manage their market activities properly with the help of Real-time data processing by continuously monitoring market conditions and adjusting trading strategies accordingly.
Traders can refine their market strategies by Leveraging real-time data which provides them with dynamic market insights.
Access to real-time market information provides traders with a significant advantage in fast-paced and volatile markets. Informed decisions based on current data allow traders to outperform competitors and achieve better overall trading results.
5.Challenges of Real-Time Data Processing ?
Handling vast amounts of data from market feeds, news updates, and social media in real-time requires robust infrastructure and advanced processing technologies.
Ensuring the accuracy of real-time data is challenging. Traders must use sophisticated techniques to filter out noise and verify data before making decisions.
Real-time data processing needs a strong infrastructure with powerful hardware, scalable software, and reliable connectivity, which can be costly and complex to maintain.
Rapid market changes can lead to impulsive decisions. Traders must balance quick responses with thoughtful analysis to manage risks in volatile markets.
6.What are some of the Market Trends and Future Projections of Generative AI in Trading ?
Generative AI improves predictive accuracy and insights, allowing traders to optimize performance, identify opportunities, and minimize risks based on real-time data and transaction cost analysis.
Driven by market advantages, the market for generative AI in trading is expected to grow significantly, potentially reaching USD 13.5 billion by 2028
Innovations like advanced algorithms (e.g., K-nearest neighbors) and AI-powered research tools in cloud-based environments enhance generative AI’s effectiveness in trading.
7.What are the steps in Implementing Real-Time Data Processing in Generative AI Trading Models ?
Implementing real-time data processing in generative AI trading models requires careful planning and best practices to enhance model accuracy, efficiency, and effectiveness. Here are key steps:
8.How can we overcome Challenges and Maximize the Potential of Real-Time Data Processing in Trading ?
Below are some of the issues and mitigation to implement Real time Data processing
