Alex Krohn
Head of editing pool
Alex Krohn is an editor at Risk.net, leading a pool of non-specialist editors. He joined Risk in 2001. Before that he worked as a journalist at the BBC and as a freelance writer for publishing house EMAP.
He holds a first-class degree in Latin from the University of Manchester.
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Articles by Alex Krohn
EU officials tamp down hopes for bank capital relief
Capital cuts are not a done deal in EC’s review of competitiveness, despite US deregulation
Why Trump’s latest Truth should make TradFi twitchy
Wall Street is becoming the villain in US president’s crypto movie
Passive investing and Big Tech: an ill-fated match
Tracker funds are choking out active managers, leading to hyped valuations for a dangerously small number of equities
Top 10 investment risks for 2026
AI, strained governments, inflated private assets: risky bets have become hard to avoid
North American banks outpace Europeans in ERM
New research shows US, Canadian banks have more developed enterprise risk management functions
Generative AI brings testing times for modellers
Flagstar’s lead model validator offers some tips for safely integrating LLMs into risk models
Risk appetite breaches test development banks
MDBs also more likely to change services or strategy to reduce risk exposure, survey shows
Libor appeals leave future misconduct cases Hayes-y
UK authorities must develop a more effective framework for punishing bad bankers
People: Natixis adds global markets trio, Six switches CRO, and more
Latest job changes across the industry
Policy-makers must keep the heat on climate transition
As the financial industry shows signs of climate fatigue, regulators need to pick up the slack
Using correlation to model op risk losses may be unsafe – study
Techniques for linking economic factors and bank losses produce varying – and sometimes contradictory – results
Single climate risk metric ‘not realistic’, says Bank of England
Senior official argues banks and investors must weigh up multiple factors when assessing climate risk
Neural networks show fewer false positives on bad loans – study
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers