Prometeia ERMAS LAB

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27 February 2020

Next Generation QuantTech For Balance Sheet Performance & Risk Optimization


Within the domain of financial asset-liability (ALM) and balance sheet risk management, next generation technology and emerging digital approaches are already making headway to enhance current industry practices and functions, not least towards achieving more dynamic projections, better foundations for modeling, and greater scalability to handle complex scenarios and expanded balance sheet metrics.

Regulators, internal stakeholders, customers, and investors are demanding more transparency in understanding the front office, risk, and capital models — from trading algos, capital models to newly emergent retail credit risk models that incorporate statistical learning approaches. Transparency demands are required not only at an analytical level, but also in development workflows and lifecycle activities associated with quantitative, strategy development/testing, risk models, and data. With these developments, one imperative that we believe to be significant in the coming years is the emergence of what we call “next-generation Data Science” and “QuantTech” platforms.

As part of Celent’s ongoing research into balance sheet management solutions, Prometeia briefed Celent about their ERMAS Lab solution through multiple sessions and extensive demos over the course of Q1 2020 . This briefing note highlights one example of a cohort of growing instances where QuantTech is expected to make a difference for financial institutions.