A comprehensive suite of cross-asset models built on modern technology to generate timely, accurate and consistent front-to-back analytics.

Insurance Analytics

Market Leader in Analytics

The insurance industry has undergone dramatic change. The combination of MiFID ll and Solvency ll have resulted in deep ongoing structural changes to the markets. The ability to anticipate and respond to market and portfolio changes are key motivators for investment managers to maintain a strong risk function. Sophisticated and robust analytics are an important component of this capability. Quantifi’s advanced cross-asset analytics combined with new technology significantly enhance the ability for firms to monitor and control risk.

Delivered in XL, Matlab with APIs supporting C#, C++, Python, Java, R

Accurate analytics for even the most complex products

New technology delivering groundbreaking performance


This is How We Do it

Cross-asset Models

Advanced models (fixed income, rates, FX, credit, equities & commodities) trusted by start-ups and the largest investment managers.

Full Transparency

Full transparency and control for all model inputs, methodologies, data and results. Detailed documentation provided for all models.

Accurate Analytics

Stable, accurate results in a fraction of the time previously possible for even the largest and most complex portfolios.

Open & Extendable

Open platform for clients to integrate their own models and data using standard technologies, rather than scripting languages or rigid APIs.

Scenario & Sensitivity Analysis

A set of finite-difference and ultra-fast semi-analytic sensitivities and scenario functions.

Highly Scalable

Highly optimised analytics engine can be scaled both horizontally (grid) and vertically (multi-core).

Quantifi ‘Category Leader’ for Buy-Side Analytics

Quantifi’s open, service-based technology architecture makes the solution easier to integrate, more flexible and easier to support. This report covers the competitive landscape as well as the capabilities and market position of Quantifi for buy-side risk analytics.


Adoption of Data Science
in Finance

Firms are now recognising that big data technologies, like data science, are the way forward. Using data science can help them focus their resources efficiently, make smarter decisions, and improve performance. This survey was conducted during a webinar Quantifi hosted, featuring Celent, on ‘Next Generation Risk technology Powered by Data Science’. Over 180 individuals from the financial services industry registered for the webinar and were invited to take part in the survey.


Next Generation Portfolio Management System

Delivering cross-asset trading, front-to-back operations, position management, accurate analytics, risk management, margining and regulatory reporting, Quantifi’s integrated solution provides consistency between front, middle and back office functions.


Navigate major trends & developments shaping the industry


Next Generation Risk Technology Powered by Data Science

Regulators, internal stakeholders, customers, and investors are demanding more transparency with understanding of front office, risk, and capital models - from trading algos, capital models to counterparty risk models that incorporate statistical learning approaches.


Buy-Side System Requirements

In the last few years, the financial markets have undergone a dramatic change. While some of this is down to natural evolution, much of the change can be directly attributed to new rules introduced in the wake of the 2007 crisis. Regulators, legislators and central bank governors have been determined to avert another bubble bursting or an unexpected event that could threaten markets.


Data Quality and Integration the Biggest Challenge Faced by Firms for Managing Liquidity Risk

Quantifi, OTC Partners, a boutique consultancy firm and BlackRock, a global investment management firm hosted a webinar on ‘Identifying liquidity Risk for Financial Stability’. The 108 delegates took part in a survey on their risk management practices and the IT/operational challenges associated with managing liquidity risk.

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