Machine Learning for Credit Risk Evaluation

What are the most promising machine learning techniques for credit risk evaluation? Fintegral will assess the options at the Artificial Intelligence in Industry and Finance conference in Winterthur, Switzerland on Friday September 7.

Stress Testing under IFRS 9

Banks are busy preparing methodologies and models for the introduction of IFRS 9 in January 2018. So far there is no industry standard for stress testing under the new regime and that presents challenges but also opportunities, as a Fintegral Briefing explains.  

Solutions for an Independent Validation Function

Both banks and regulators want to manage the risk created by the use of models, but they’re frequently at odds over how to do it. In the new yearbook of the Frankfurt Institute for Risk Management and Regulation (FIRM), Fintegral Partner Dr Andreas Peter and Helaba Bank Head of Credit Risk Management Stephan Kloock present […]

The Future of Risk Management

How do we manage risk in future? As traditional banking is turned on its head by FinTech and the acceleration of digitalization, how are we innovating to meet the expectations of both the market and supervisors? These were two of the questions discussed recently at Fintegral’s 31st Expert Forum in Frankfurt. Speakers from Commerzbank, Bergmann & […]

EBA Timeline for 2018 Stress Tests: How we can help you

The European Banking Authority (EBA) has outlined a timetable for the 2018 EU-wide stress test. The exercise is expected to be launched at the beginning of 2018 and the results published in the middle of the year. The EBA is now in the process of preparing methodology and templates before consulting with the industry this […]

VaR Survey: Fears over industry’s readiness for FRTB

A Fintegral survey of Value at Risk methodologies and frameworks at tier 1 banks suggests a lack of preparedness for the Fundamental Review of the Trading Book (FRTB). Among the key findings: Most respondents view data quality as a major concern for them Institutions appear ill-prepared to meet the new requirements There is resistance to […]

Model Risk and Model Governance

Machine-Learning: Decision-Making in Complex Environments

From robot surgery to self-driving cars, the algorithms used to control socio-technical systems are becoming more powerful. And in the era of machine-learning, our cyber decision-makers both learn and adapt. But what do we know about the risks involved? If machine-learning is not to be a technological black box, then there needs to be transparency and […]

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