The imperative of expanding the traditional MRM function

Financial institutions and non-bank financial technology companies (FinTechs) alike make extensive use of various machine learning models (MLOps) in core and non-core areas of their business.

Banks, for example, rely on such models for a range of risk assessments, including predictive underwriting, credit risk management, suspicious and/or fraudulent activity management, fair lending compliance, derivative and financial instrument pricing and valuation, securitisation risks associated with trading and financial reporting. Developed in Python, R, MatLab or Excel, these powerful models are broadly leveraged by business users to support complex business needs.

Managing an increasingly complex model environment creates challenges for the modelling, risk and compliance teams, for senior management, as well as for auditors.

Download the full article below.

Mazars Forvis logo

Emmanuel Dooseman

Partner, Global Head of Banking and Capital Markets - New York