ING Group has been nominated for the 2026 World AI Awards in the Algorithmic Trading category, recognising the Dutch banking group’s work in machine-learning-powered trading, electronic execution and AI-assisted pricing across financial markets.
Algorithmic trading at ING is not a new experiment dressed up with a generative AI label. The bank has spent years applying predictive analytics and machine learning to fixed income and foreign exchange, where milliseconds matter, prices shift constantly and traders have to process more market information than any individual can realistically absorb.
The World AI Awards recognises organisations, individuals, products and technologies contributing to the development and real-world application of artificial intelligence across industries.
For ING, the practical question is straightforward: can machine learning improve pricing and execution while keeping human traders and established risk controls in the loop? Its work in bond trading and, more recently, foreign exchange provides some concrete answers.
Machine learning moves into currency pricing
ING has pushed machine learning directly into its electronic foreign-exchange operation.
The bank developed an AI model using reinforcement learning, a machine-learning approach in which a system improves its decisions through feedback from previous outcomes. In ING’s case, the model was built to make currency-pricing decisions and react to changing market conditions — work that traditionally demanded significant attention from human traders.
Bloomberg reported in 2024 that ING had begun using the model for FX pricing. By 2026, the bank was pointing to tangible commercial results from the technology.
Simon Bevan, ING’s global head of electronic trading, said the in-house AI currency-pricing model had produced an initial 50% increase in large-ticket trades. That is a company-reported result and should not be interpreted as a guarantee of equivalent performance across every market or trading strategy.
The technology has also moved geographically. ING Wholesale Banking said in August 2026 that it had brought its algorithmic trading business live in Singapore during the previous year. The capability allows clients to interact digitally with ING when hedging currency exposure, with machine learning supporting faster execution.
Katana showed the strategy years earlier
ING’s current algorithmic trading push has roots stretching back almost a decade.
In 2017, the bank introduced Katana, a predictive analytics tool developed by its Financial Markets Global Credit Trading and Wholesale Banking Advanced Analytics teams. Rather than automatically replacing the bond trader, Katana was designed to analyse historical and real-time information and suggest pricing decisions.
The system learned from hundreds of thousands of historical trades.
During initial testing with ING’s emerging-markets desk in London, the bank reported faster pricing decisions for 90% of trades, a 25% reduction in trading costs, and a fourfold increase in the frequency with which traders could offer clients the best price. These figures came from ING’s testing rather than an independent benchmark of the wider algorithmic-trading industry.
ING later expanded the concept with Katana Lens, developed with Dutch pension fund PGGM. The web-based application applied predictive analytics to hundreds of thousands of historical trades to help bond investors identify and compare potentially relevant trading opportunities more quickly.
That distinction remains important. ING’s approach was not simply to automate every trading decision. The tools were built to narrow large pools of market information into something a professional investor or trader could act on.
Generative AI is now helping build the trading systems themselves
There is another layer emerging inside ING’s electronic trading operation.
In 2026, the bank began using generative AI-assisted development — sometimes called “vibe coding” — to build financial-market technology. Instead of engineers manually writing every part of an application, developers can describe what they need and use AI models to generate portions of the underlying software.
ING has used the approach to create analytics dashboards showing real-time pricing, incoming trades and performance metrics. Bevan also said the bank used it extensively to build an entire credit electronic-trading system, compressing development work that could previously have taken teams weeks into hours in some cases. ING uses external AI models for this work, with Bevan reporting particularly strong results from Anthropic technology.
The bank is not presenting this as permission to remove engineering oversight. Trading systems operate in an environment where model behaviour, market risk, compliance and software reliability all matter. ING’s broader strategy calls for increased structural automation in Financial Markets alongside enhanced pricing tools and data-driven insights.
Algorithmic trading becomes part of a wider AI strategy
The technology now sits inside a much broader transformation of ING’s financial-markets operation.
ING describes e-trading as covering products ranging from foreign exchange, commodities, futures and cash equities to corporate and government bonds. Its current quantitative trading recruitment also points directly to the development of a new generation of pricing and hedging algorithms, with data at the core of the operation.
The significance of ING’s work is therefore less about one algorithm beating a human trader.
It is about rebuilding parts of the trading workflow around machine learning: algorithms can calculate prices, analyse previous transactions and execute electronically; AI-assisted development can accelerate the construction of new trading infrastructure; traders remain responsible for operating within the bank’s market and risk framework.
That combination makes ING particularly relevant to the Algorithmic Trading category.
Graham Cooke, President of the World AI Awards, said:
“ING Group’s nomination highlights how artificial intelligence and machine learning are becoming increasingly practical tools within global financial markets.
“From AI-supported currency pricing and electronic execution to predictive analytics in fixed-income trading, ING’s work demonstrates the opportunity for intelligent systems to help market professionals process information and respond more efficiently to changing conditions. We congratulate ING Group on its 2026 World AI Awards nomination in Algorithmic Trading and look forward to following the continued development of these technologies.”
ING Group joins organisations, researchers, entrepreneurs and technology developers being recognised through the 2026 World AI Awards.
The programme recognises organisations, individuals and technologies contributing to the development and application of artificial intelligence across industries. In financial services, that increasingly means moving beyond experimental AI demonstrations and finding controlled applications for machine learning inside real banking and market infrastructure.
ING’s algorithmic trading work provides a useful example. Katana established an early model for combining predictive analytics with human trading expertise. Machine-learning currency pricing has since taken algorithms closer to real-time execution, while AI-assisted software development is beginning to change how the bank builds the systems surrounding those trades.
The technology does not eliminate the complexity of financial markets. It changes where some of that complexity gets handled.
Learn more about ING Group and its financial-markets technology at ing.com.
Discover the World AI Awards 2026, explore the nominees and learn more about the awards at worldawards.ai.
