Ben Archer
Lead Consultant
With almost two decades of data science and fraud prevention experience within the financial services and retail sectors, Archer’s expertise is instrumental in enhancing Jaywing's fraud prevention offering to meet the growing demands of our clients.
Prior to joining Jaywing, Archer spent two years at Provenir, where he played a pivotal role in the development of fraud products. His responsibilities included collaborating with lending organisations and governments to identify fraud rings across different countries, as well as working with the gaming industry on graph database analytics and linkage scoring to combat issues of multi-accounting and bonus abuse.
Archer also spent twelve years at FICO, a prominent analytics company, where his leadership in fraud detection and prevention operations covered the EU, Middle East, and Africa.
How a graph database caught 23% more fraud from a synthetic identity ring
How a real-time graph database caught an extra 23% of fraudulent applications, and what it means for fraud teams and developers in Thailand.
From credit risk to fraud risk: what changes and how to prepare
Ben Archer explains how credit risk professionals can prepare for fraud risk, from thinking like a detective to judging models on capture rate over GINI.
Fraudscape 2026 update: Six fraud trends worth your team's attention
Fraudscape 2026 H1 update: identity fraud up 9%, SIM swap up 402%, money mules up 69%. Six trends financial services firms need to act on.
Are fraud controls becoming unsustainable?
Fraud volumes are rising. Investigative capacity isn't. Here's why rules engines drift toward unsustainability, and what the data says about where to focus.
Fraud contamination in credit risk models: why portfolios stop behaving as expected
Fraud losses hiding inside credit risk models corrupt training data for years. Here's what a retrospective audit catches that backtesting can't.
Identifying hidden fraud networks: Why fraud detection needs a network-based approach
Fraud is now networked. Learn how graph databases help detect fraud rings, reduce losses and improve real-time decision making.
Smarter fraud and AML convergence: Escaping the silos
Why fraud and AML separation weakens detection and what unified, graph-based architecture requires by 2026.
Tackling telecom-enabled fraud through smarter data collaboration
UK fraud losses topped £629m in H1 2025. Real-time telecom intelligence now lets banks intervene during scams. What CROs need to know.
Credit fraud trends shaping 2026 lending strategies
Synthetic ID, ATO attacks and smarter risk are reshaping 2026 lending. How integrated models and targeted controls improve detection and decisioning.