Drawn to intelligent systems, financial infrastructure, research, and building things that matter.
Thinking, lately, about graph intelligence. graph intelligence, financial crime, machine learning in finance, building useful things, research that becomes software, and the future of payments.
Product, engineering, research, and an enduring curiosity about how things work.
Currently
Financial Technology · AI Research
I'm Iyanu, a product and engineering professional working across artificial intelligence, financial systems, and technology.
My experience spans payments, financial technology, product development, and engineering programme management.
Much of my current research explores how machine learning and graph intelligence can improve financial crime detection.
Outside work, I'm interested in entrepreneurship, books, and understanding how the world works.
Building products and systems at the intersection of technology, finance, and human behaviour.
A selection of organisations and teams I've worked with.
Cross-functional engineering programme management supporting retail technology and digital commerce.
Digital payments, financial products, and product development across fintech markets.
Product operations and marketplace technology across Bolt and Jumia.
Exploring intelligent systems for financial crime prevention.
My research explores payment behaviour, graph intelligence, explainable AI and real-time risk decisioning. Manuscripts, publications and technical material will be linked as they become available.
Investigating how machine learning can identify anomalous payment behaviour before authorisation, while balancing fraud recall, false alerts and inference latency.
Exploring how graph neural networks, language models and agent orchestration can connect suspicious transaction relationships with interpretable investigative evidence.
A proposed study of payment fraud detection with sandbox datasets, focused on realistic payment patterns, generalisation and practical deployment constraints.
Investigating suspicious flows across bank transfers, electronic money institutions, virtual IBANs and crypto or stablecoin payment channels.
Ideas become more interesting when you build them.
Research-driven experiments and software exploring how intelligent systems can strengthen financial infrastructure. Explore live applications, research implementations and source code below.
An interactive exploration of reported payment fraud across EU member states, payment instruments and time. Built with European Central Bank statistics to examine geographic patterns, fraud rates and historical trends.
Exploring how connections between accounts and transactions reveal suspicious networks that isolated transaction checks can miss.
An interactive environment for exploring payment behaviour, model decisions and fraud-detection trade-offs through visual analysis.
Classifying payment fraud risk with machine learning. A research implementation exploring payment statistics, feature engineering, classification models and fraud-risk analysis.
An experimental implementation exploring graph neural networks, language models and agent orchestration for explainable fraud detection.
A research-driven approach to assessing suspicious payment behaviour before authorisation, with an emphasis on useful decisions and low-latency inference.
Places, people, and moments worth remembering.
A small visual collection of architecture, nature, textures and everyday life.
Six exposures · Monochrome collection
