TurinTech AI Joins the NayaOne Tech Marketplace

TurinTech AI has joined the NayaOne Tech Marketplace. It provides financial institutions with advanced AI-driven code optimisation solutions that enhance the performance and efficiency of their data and machine learning applications, reducing costs and improving sustainability. “Partnering with NayaOne allows us to bring our advanced AI-driven code optimisation solutions to more financial institutions, helping them achieve their goals of efficiency and sustainability. We are excited to see the transformative impact our technology will have on the industry.” Dr. Leslie Kanthan CEO, TurinTech AI “We’re excited to welcome TurinTech AI to the NayaOne Tech Marketplace. As technology continuously evolves, optimising code is crucial for banks. It enhances transaction processing speed, reduces operational costs, lowers energy consumption, and improves overall efficiency, leading to better customer experiences, faster decision-making, and increased competitiveness in the financial sector.” Varun Resh Marketplace Manager, NayaOne Artificial intelligence, and technology in general, keeps evolving at a rapid pace. Because of that, financial institutions struggle to get the best performance from their data and AI applications. This hurts their efficiency and sustainability. Poorly written code leads to slow performance, high energy use, and wasted resources, resulting in poor user experiences and growing technical debt. TurinTech AI’s code optimisation products, Artemis AI and evoML address these issues. They leverage Generative AI technologies to improve the performance of machine learning models and data-intensive applications. Benefits Financial Institutions Enjoy with TurinTech AI Both TurinTech AI products offer a range of features and benefits tailored to meet the unique needs of financial institutions. Artemis AI is a GenAI-powered code optimisation platform. It helps organisations transform their legacy codebase, improving performance, and reducing technical debt. With it, financial institutions can enjoy:

• Generative AI-Powered Optimisation: Leverage the power of Generative AI to efficiently improve the quality and performance of code.
• Code Refactoring: Many financial institutions rely on outdated code that hampers efficiency and scalability. Artemis AI refactors this legacy code into modern, optimised versions, ensuring that banks can operate with agility and resilience.
• Reduced Maintenance Costs: Refactored code requires less maintenance, freeing up resources for innovation.
• Improved Application Speed: By optimising the underlying code, financial institutions can process transactions and analyse data more quickly, leading to faster decision-making and improved customer experience.
• Improved Efficiency: Optimised code requires fewer computational resources. This lowers operational costs and energy consumption, which aligns with the growing emphasis on sustainability in the financial sector.

Meanwhile, evoML empowers financial institutions to accelerate the development and deployment of robust machine learning models. This enables them to make more informed decisions and improve risk management, and customer experience. It helps businesses achieve:

• Rapid Model Development: Quickly build and deploy machine learning models that meet production standards.
• Higher Explainability: evoML comes with increased metrics and visualisations to understand the inner workings of a model. This helps make better decisions and explain them to regulatory bodies.
• Increased Accuracy: evoML builds several models, evaluates their efficiency based on user-defined metrics, and suggests the best model. Compared to conventional models, these models have proven to be more accurate.
• Customisation: Download and customise the model code for your deployment criteria to better fit your business needs. A full report explaining the end-to-end ML model-building process is provided.
• Scalability: Seamlessly scale models to accommodate growing data and user demands.

Accelerating Financial Software Runtime by 32.72% with Artemis AI: From a £10 Optimisation to £400K Savings QuantLib is an open-source library for quantitative finance, extensively used by banks and hedge funds for modelling, trading, and risk management. Slow and inefficient code in critical libraries like QuantLib can significantly impact the speed and efficiency of financial applications, reducing profitability and competitiveness for businesses. Using Artemis AI, TurinTech AI was able to optimise the performance of QuantLib and achieve a 12.2% acceleration in runtime with a single pull request. The corresponding unit test for the code improvement runs approximately 30% faster on a single processor. Other benefits include

• Value for Money: With Artemis AI, entire codebases can be optimised within minutes for a cost of as little as £10 per codebase. Compared with the costs of running inefficient code and spending expensive developer time to spot and fix code inefficiencies, Artemis AI gives excellent value for money. For example, a bank spending £100,000 per month on cloud computing resources for QuantLib-based financial applications, could save £32,720 monthly, or £392,640 annually, with just a £10 investment in achieving a 32.72% runtime acceleration.
• Developer Productivity: Developers are freed from weeks of manual optimisation, allowing them to focus on more strategic tasks.
• Business Impact: Faster analysis and responsiveness to financial market changes, substantial cloud cost savings, and reduced carbon emissions.
• By integrating evoML and Artemis AI into the NayaOne Tech Marketplace, banks and financial institutions can easily discover, access, and deploy these powerful tools within their existing ecosystems.

This also fosters a more dynamic and innovative environment, enabling financial institutions to experiment, develop, and implement cutting-edge AI solutions faster than ever before. Finally, NayaOne’s Digital Sandbox environment offers a controlled space for testing and experimentation. This helps financial firms mitigate the risks associated with deploying new AI models or implementing code changes in production systems.