Dr. Janet Bastiman

‘By aligning technical innovation with regulatory expectations from the outset, Janet transformed a perceived barrier into an enabler’

Part of the AI Leadership Index – a curated list of the most inspirational figures shaping the AI revolution.

Dr. Janet Bastiman is Chair of the Royal Statistical Society’s Data Science and AI Section, Vice Chair of the Royal Statistical Society’s AI Task Force and a member of Financial Conduct Authority’s (FCA) Synthetic Data Expert Group, Janet started coding in 1984 and discovered a passion for technology.

She holds multiple degrees and a PhD in Computational Neuroscience. Janet has helped both start-ups and established businesses implement and improve their AI offering prior to applying her expertise as Chief Data Scientist at Napier AI.

Please share a case study of how you have you innovated with AI over the last year?

Over the past twelve to eighteen months, Dr Bastiman has played a leading role in advancing responsible artificial intelligence in financial crime compliance. In 2025, she led the development of an agentic testing framework that uses multiple autonomous agents to simulate realistic money-laundering behaviours. These artificial laundering bots adapt their strategies to evade detection, allowing financial institutions to stress-test rules and AI models against evolving threats in a controlled, auditable environment. This work is helping shift the industry from hype-led AI adoption toward evidence-based, compliance-first innovation.

Janet also led Napier AI’s work on synthetic data in collaboration with the FCA and the Alan Turing Institute, demonstrating how privacy-preserving datasets can validate financial crime controls without exposing sensitive customer data. She has championed a compliance-first approach to AI, working closely with the FCA on initiatives including its Supercharged Sandbox FCA programme (in collaboration with NVIDIA), which gives firms access to better data, technical expertise and regulatory support to accelerate innovation.

In addition, Janet secured participation in the FCA’s AI Live Testing FCA AI Lab programme, an environment designed to support the safe and responsible deployment of AI while delivering positive outcomes for UK consumers and markets. Napier AI is the only vendor invited to both the Supercharged Sandbox and AI Live Testing programmes, highlighting Janet’s leading expertise in AI for financial services.

What is the biggest challenge you have faced with AI innovation and how you have overcome it?

The biggest challenge Dr Bastiman has faced in AI innovation is bridging the gap between what AI can theoretically do and what regulated organisations can safely deploy. In financial services, AI failures have direct regulatory and societal consequences, which creates understandable resistance to models that cannot be clearly explained, particularly in areas such as transaction monitoring where decisions can affect customers.

Janet addressed this by rethinking how AI should be designed for high-stakes environments. Rather than prioritising model sophistication, she started with regulatory reality. Through Napier AI’s participation in the FCA’s Supercharged Sandbox FCA programme, she worked with regulators and technology partners to explore how advanced AI techniques could be applied without introducing opacity or unacceptable risk.

A key breakthrough was shifting from isolated transaction analysis to a network-based approach that models financial crime as a flowing system. By analysing factors such as direction, value, currency and behavioural change over time, her team demonstrated how deeper insights could be produced using fewer compute cycles, making the approach both operationally viable and environmentally efficient.

Crucially, Janet helped define a model where typologies, behavioural indicators and upstream risk are exposed as explicit, explainable insights. This ensures outputs can be understood, challenged and defended by human investigators.

By aligning technical innovation with regulatory expectations from the outset, Janet transformed a perceived barrier into an enabler, delivering AI that institutions can confidently deploy in production with stronger insight, lower cost and greater regulatory confidence.

How do you balance innovation with ethical safeguards?

Dr Bastiman is a strong advocate for the idea that ethical safeguards are not a constraint on innovation, but a prerequisite for it. In her work, ethics are embedded at the design stage rather than added as controls after deployment. As a result, Napier AI’s products never surface an AI inference without traceable supporting data.

Every AI capability Janet leads is designed to be explainable, auditable and subject to human oversight. Models operate within defined evidence boundaries, and outputs are presented in human-readable form so users can understand, agree with or challenge decisions. This avoids the risks associated with ungrounded generative outputs or autonomous decision-making in high-stakes contexts.

Janet also works closely with regulators, academic institutions and industry bodies to ensure innovation aligns with societal expectations as well as technical best practice. Her involvement with the FCA’s Synthetic Data Expert Group and AI sandbox initiatives reflects her belief that ethical AI must be developed collaboratively, not in isolation.

By treating transparency, accountability and fairness as core engineering requirements, Janet ensures innovation delivers long-term value rather than short-term novelty.

How do you believe the artificial intelligence space will change over the next 12-24 months?

Over the next 12–24 months, Dr Bastiman expects the AI landscape to shift from experimentation toward accountability. Organisations will move beyond asking what AI can do and focus on what it can be safely trusted to do in production, particularly in regulated sectors.

This shift will bring greater scrutiny of explainability, governance and evidence, with regulators and boards demanding clearer understanding of how AI systems reach conclusions and manage risk. As a result, the limitations of opaque models will become more visible, driving adoption of approaches that combine advanced analytics with transparency.

Janet also expects growing emphasis on efficient, targeted AI. Rising infrastructure costs and environmental considerations will encourage organisations to favour smarter, lower-compute approaches over brute-force modelling. Techniques such as network-based analysis, synthetic data and agentic testing will become increasingly important.

Finally, human–AI collaboration will mature. The most effective systems will augment expert judgement rather than replace it, embedding AI into workflows that strengthen accountability and decision quality. In Janet’s view, leadership in AI will increasingly be defined not by novelty, but by responsibility and impact.