Innovation is at the heart of what CompatibL does: introducing new methods of addressing complex problems for its capital markets clients. This win follows last year's success in the best new technology introduced over the last 12 months (trading and risk) category in these awards, thanks to the release of its machine learning-based market generators underpinning the firm's credit risk model. Given CompatibL's propensity for pushing the AI-based risk model envelope, who would bet against it picking up another category in these awards next year?

Financial collaboration platform Symphony Communications Services is planning to leverage artificial intelligence (AI) and data-sharing technology from Google, after confirming it will switch to the tech giant as its main cloud provider. Symphony initially decided to switch cloud providers after assessing its existing deal with Amazon Web Services (AWS) to be too expensive, says Dietmar Fauser, chief information officer at Symphony.

Anthony looks at an interesting project using causal inference by IBM and Refinitiv, and what this latest evolution of machine learning could mean for innovation in the capital markets in the future.

In 2019, SmartStream rolled out Air, a cloud-native reconciliations platform. At the time, it was described as "the birth of all our AI and machine learning products." While Air is a standalone product that a user can license on the cloud, the AI that underpins the system can be used across SmartStream's suite of products. To that end, SmartStream has already added that AI functionality for matching to its TLM Aurora and TLM Reconciliations Premium platforms. The next step is to add Air's data

The Federal Reserve Bank of New York is turning to machine learning to cut down on the back and forth between the regulator and banks and predict potential misreporting. Sri Malladi, senior director at the New York Fed's data and statistics group, said during the Waters USA conference in Manhattan that the regulator's long-term goal with machine learning is to be able to predict potential issues with banks' reporting. "We want to be at the point where we know that distribution expert reports

Yesterday, Nasdaq announced the launch of its new AI-powered market surveillance tools for finding patterns of abusive behavior.

The company plans to test out machine-learning models in the coming months to automatically fix IT system fragmentation.

As firms are coming under increasing pressure to adopt artificial intelligence (AI) and machine learning programs, working out how to integrate them into existing systems in the least disruptive and most efficient way is a pivotal consideration. Peter Simon, lead data scientist, financial markets at DataRobot, explores how firms can satisfy regulators' calls for interpretability, the sustainable advantages of setting up AI programs and why the often-overlooked route of an open-source code base could be key to maximising efficiency.

Many in financial services are trialing artificial intelligence (AI) applications, with projects increasingly sophisticated in methodology and ambition. WatersTechnology, in partnership with SmartStream, recently convened a Chatham House-style discussion with industry technologists to discuss their hopes for AI as well as the practical and ethical challenges to greater adoption. While the world of AI grows ever wider, one constant has shown through as firms inch closer to implementation--gambits with a strong data orientation will thrive, and those without will not. Banks and asset managers experimenting with AI have come through the early stages with enthusiasm, but, as with the peloton in the Tour de France, they are beginning to feel some strain in their legs. For many, arduous new terrain still awaits before reaching the finish line. As one contributor to the WatersTechnology/SmartStream discussion described it: "The struggle at the moment remains being able to articulate a business case for AI; hence the reason people are doing more proof of concepts to trial it--to help define the business case, to then move forward with its applicability within the industry."

Few technologies this century have captured the imagination of so many technologists within the capital markets as artificial intelligence (AI). Thanks to a fertile combination of data, storage, and computing power, AI has emerged from its long winter and spread into nearly all areas of the capital markets, across the front, middle and back offices. While many vendors can claim AI capability--some of them dubiously--few have the bona fides of firms like Digital Reasoning. The Franklin, Tenn.-headquartered firm had its start in 2000, quickly winning defense contracts with the US Army, and in 2010, funding from the Central Intelligence Agency. It wasn't until around 2012, however, that Digital Reasoning entered into finance with a bang, signing up names such as Point72 and UBS.