Cross-inspection consistency
I lead consistency reviews across international supervisory inspections, connecting individual findings with shared standards and a coherent supervisory view.
Professional profile
Banking supervision, teaching and applied research
Team Lead at the European Central Bank in Frankfurt am Main. I work on the consistency of international supervisory inspections, with a particular focus on credit risk and IFRS 9 expected credit loss models.
Banking supervision is the centre of my professional practice. My teaching and independent research share a focus on how financial risks are measured, how models are evaluated, and how evidence supports professional judgement.
Profile
My career brings together supervisory judgement, quantitative analysis and first-hand banking experience. Across roles at the European Central Bank and Banco de España within the Single Supervisory Mechanism, I have worked on inspections, credit-risk assessment, supervisory methodology and the use of granular data to make complex portfolios more intelligible.
My independent research develops this analytical approach through reproducible empirical studies: making measurement choices explicit, comparing models with meaningful alternatives, and distinguishing description, prediction and causal interpretation. Teaching brings the same reasoning into the classroom.
Before joining public banking supervision, I spent fourteen years in private-sector banking and finance. That period covered branch management, mortgage lending, non-performing loan recovery and restructuring, and complex negotiations—experience that continues to inform how I read institutions, incentives and balance sheets.
In 2018, I placed seventh in the competitive selection process for thirty Banco de España Inspector positions.
CEF interview · Preparing for the examination alongside work and family responsibilities01 · Primary practice
My core work is the consistent and evidence-based assessment of banking risks across the European supervisory framework.
I lead consistency reviews across international supervisory inspections, connecting individual findings with shared standards and a coherent supervisory view.
My main technical focus is credit risk, including expected credit loss models, portfolio evidence and the assumptions that shape provisioning outcomes.
I serve as Head of Mission on selected assignments, bringing together technical depth, clear communication and proportionate supervisory judgement.
My work also includes supervisory methodology, granular portfolio analysis and the practical use of natural-language processing and other SupTech approaches.
Selected SupTech contribution
At Banco de España, I developed a text-mining approach to support risk-based credit-file sampling. The resulting tool analysed unstructured information in credit files to identify exposures potentially misclassified as performing and bring them forward for supervisory review.
The initiative was subsequently referenced by the BIS Financial Stability Institute as “the Bank of Spain’s text mining for wiser sampling” in FSI Insights No 37, Suptech Tools for Prudential Supervision and Their Use During the Pandemic (2021).
External reference · BIS FSI Insights No 37Training and knowledge transfer have been a recurring part of my work at both Banco de España and the European Central Bank. I have designed, organised and delivered training for inspectors and banking supervisors from across the Single Supervisory Mechanism and ASBA member authorities in the Americas. Topics have included machine learning applied to banking supervision, data analysis with R and Python, and AI-assisted approaches to supervisory analysis.
02 · Teaching
My teaching connects economic reasoning with practical data analysis: defining the question, examining assumptions, testing alternatives and explaining what the evidence supports. It is built around structured intuition and clear explanation. It also reflects a personal continuity: I later taught at Universidad Carlos III de Madrid, where I earned my degree, and I now teach at Centro de Estudios Financieros (CEF), where I prepared for the Banco de España competitive examination. The same commitment to knowledge transfer informs the training I deliver in banking supervision.
I teach Financial Mathematics, Statistics, Econometrics and Applied Quantitative Methods in programmes preparing candidates for Banco de España Inspector examinations.
I taught Econometrics, International Trade and Derivatives, including bilingual groups. My teaching received formal commendations following student evaluations in 2019 and 2020.
UC3M alumni interview · Mentoring and returning experience to the university community03 · Independent research
What can we infer from a financial indicator, and what evidence do we need to use it? My research examines how debt, payment burdens and resource proxies are represented in financial indicators, and tests what those representations add.
Read the trilogy overviewEvidence base
Three linked studies use quarterly macrofinancial data for 18 economies over 1999–2023 to examine resource normalisation, credit asymmetry and common-denominator effects. They distinguish descriptive interpretation from predictive performance through comparisons with the indicators’ components and simple benchmarks. Papers and replication materials are linked below.
What survives scrutiny
Across the trilogy, model criticism is treated as a substantive result: the studies identify which constructions survive stronger scrutiny, which apparent gains are absorbed by simpler explanations, and where the evidence requires narrower claims.
Distinguishes debt-ratio stabilisation from debt-service coverage. Examines how resource proxies shape the diagnosis and tests whether normalised payment pressure adds useful information beyond its components.
Tests resource and credit margins against their underlying components, credit asymmetry and simple forecasts. Shows why an apparent gain over a linear model must be examined against a matched nonlinear benchmark.
Decomposes changes in a composite debt indicator into movements in claims, payment burdens and their common denominator. Examines historical associations, predictive performance and the limits of public–private aggregation.
Selected education
Universidad Nebrija · 66 ECTS · Highest distinction, 10/10
CEMFI and Banco de España
Universidad Carlos III de Madrid
Contact
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