Tag: data science
AI supervision: Cutting false positives the right way
AI-driven supervision tools are now central to modern RegTech strategies, particularly in communications surveillance and misconduct detection. Yet as firms invest in AI to...
Numerai lands $30m to scale AI-powered hedge fund
Numerai, an investment firm that builds its portfolio using machine learning models and crowdsourced data science, has secured fresh capital of $30m.
The investment was...
Why PRAUC is the true test of AML model performance
Determining how effective an anti-money laundering (AML) model truly is has become a major challenge for financial institutions.
Research from PwC shows that 90–95% of...
Napier AI joins FCA Supercharged Sandbox
London-based RegTech Napier AI has been selected to join the Financial Conduct Authority’s (FCA) new Supercharged Sandbox, created in partnership with NVIDIA and NayaOne...
US FinTech funding dropped by 13% QoQ in Q1 2025 as...
Key US FinTech investment stats in Q1 2025: US FinTech investments dropped by 13% QoQ in Q1 2025
Average deal value dropped to $27.3m...
Turbine unveils $121m funding to transform liquidity access for private equity...
Turbine Finance, commonly referred to as Turbine, has recently announced a significant financial milestone.
Sift revolutionizes fraud detection with innovative ThreatClusters technology
In its most recent product update, Sift has introduced ThreatClusters, a pioneering data science innovation designed to enhance fraud detection.
The evolution of AI in data science: How LLMs are changing...
The rapid advancements in artificial intelligence, particularly in language model technology, are reshaping the data science landscape.
CreditLogic secures €3.5m to boost European expansion
CreditLogic, the Dublin-headquartered FinTech company specialising in lending-as-a-service, has successfully raised €3.5m to propel its expansion across Europe.
Navigating Pitfalls in AI Finance
The buzz surrounding Artificial Intelligence (AI) often heralds it as a panacea for all modern problems. Yet, the reality is more nuanced, as the effectiveness of AI heavily relies on the quality and readiness of the data it processes.










