Migrations.ml utilizes machine learning to provide unbiased credit risk analytics based on macroeconomic factors and company fundamentals for over 9,000 corporate bonds. This enables portfolio managers to effectively manage risk, identify investment opportunities, and enhance existing financial models.
Funding
$120K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Credit risk assessment for corporate bonds often relies on biased or incomplete data, hindering portfolio managers' ability to effectively manage risk and identify investment opportunities. Traditional credit models may lack transparency and fail to incorporate macroeconomic factors and company fundamentals comprehensively.
Solution
Migrations.ml provided unbiased credit risk analytics for over 9,000 corporate bonds, leveraging machine learning and focusing on macroeconomic factors and company fundamentals. The platform delivered transparent, data-driven insights to enhance existing financial models and improve risk management. By offering a clear view into the driving factors behind credit risk, Migrations.ml enabled portfolio managers to identify new trading opportunities and enrich their default models. The analytics aided in developing a robust counterparty risk management framework, limiting portfolio risk exposure while maximizing returns.
Target Audience
The primary users were forward-thinking portfolio managers seeking to manage risk, identify investment opportunities, and enhance existing financial models.
Features
- Unbiased credit risk insights based on macroeconomic factors and company fundamentals
- Coverage of over 9,000 corporate bonds issued by over 800 companies
- Identification of discrepancies between Option-Adjusted Spread (OAS) and Migrations spread to identify trading opportunities
- Model enrichment capabilities for existing default models, forecasted losses, and trading strategies
- Portfolio risk management tools to understand existing portfolio risk in accordance with current market conditions
- Counterparty risk monitoring to develop a robust risk management framework