Conning
Conning is a leading global investment management firm with a long history of serving the insurance industry. Conning supports institutional investors, including pension plans, with investment solutions and asset management offerings, risk modelling software and industry research. Founded in 1912, Conning has investment centres in Asia, Europe and North America.
Conning’s risk management software platform includes GEMS® Economic Scenario Generator, FIRM® Portfolio Analyzer and ADVISE® Enterprise Risk Modeler, offering greater accuracy in financial risk modelling and providing deeper insights for decision-making, regulatory and rating agency compliance, and capital allocation. Conning Allocation Optimizer™ is available as a software-as-a-service solution to perform strategic asset allocation analyses and identify optimal investment strategies.
GEMS®, FIRM® and ADVISE® are registered trademarks in the US of Conning, Inc.
Articles about Prometeia
Nature-related risk: the next challenge for banking risk management
Banks are turning nature-related risks into decision-useful risk metrics in preparation for the next phase of supervisory scrutiny
Boosting regulatory assessment with GenAI: Prometeia’s use case for credit risk models
Prometeia’s GenAI tool provides institutions with the technology to remain competitive and compliant in a complex regulatory environment
Real estate loan origination and monitoring with a macroeconomic-coherent Monte Carlo approach
The paper aims to show how Monte Carlo simulations based on economic scenarios can improve a sector such as real estate, which can be profitable for banks and other financial institutions, but also very risky.
Prometeia’s LLM/GenAI validation framework
Prometeia’s LLM/GenAI validation framework: a method to validate generative AI models for financial applications
Increasing trust to artificial intelligence in finance: AI model validation framework
This paper highlights the risks of using AI in financial applications and provides significant motivations for having an AI validation framework to control and eliminate those risks
Machine learning and AI in model risk management: a quant perspective
Statistical risk models face issues of validity as unprecedented events and social phenomena occur. However, artificial intelligence (AI) and machine learning can assist models in maximising accuracy. By Tiziano Bellini, head of risk integration…
Counterparty credit risk: special report 2022
This Risk.net special report contains a collection of articles that consider the impact of SA-CCR and how banks are adapting to the new regime, as well as the challenges of managing counterparty credit risk and reducing the cost of trading over-the…
Project finance risk methodologies
Federico Tacchetto, senior manager at Prometeia, describes how to calculate risk parameters for project finance exposures. Based on a simulation approach of the cashflows, it is assessed whether the generated net revenue will be sufficient to repay the…
Hints on quantification approaches
Tiziano Bellini, head of risk integration competence line, international markets at Prometeia, examines the key components of successful model risk management, focusing on the importance of integration, processes, governance and IT solutions to…
As Covid snaps credit models, lenders turn to stress-testing
Banks enlist scenario analysis to bolster creaking default models
Lenders reveal struggles over IFRS 9 roll-out
Size of task caught some banks unawares, leading to botched home-grown systems or data problems
Leading the way in the risk management (r)evolution
Sponsored feature: Prometeia
Risk software survey 2015: speed, compliance and valuation
Regulatory change drives innovation, with valuation, centralisation and speed taking centre stage