Case Study: Risk-as-a-Service

The Client Portfolio and Risk Management Software Vendor Get a DemoTalk Scope The Problem Our client is a Portfolio and Risk Management software vendor and leading provider of on-demand derivative trading analytics, portfolio and risk management solutions for the global financial industry. Its flagship product provides thousands of users worldwide with advanced real-time portfolio and risk management...

riskspan case study

Case Study: RS Edge – Analytics and Risk

The Client Large Life Insurance Company - Investment Group Get a DemoTalk Scope The Problem The Client was shopping around for an analytics and risk platform to be used by both the trading desk and risk managers. RiskSpan Edge Platform enabled highly scalable analytics and risk modeling providing visibility and control to address investment analysis, risk surveillance,...

riskspan case study

Risk-as-a-Service – Transforming Portfolio Market Risk Analytics

Watch RiskSpan Co-Founder and Chief Technology Officer, Suhrud Dagli, discuss RiskSpan's Risk-as-a-Service offerings. RiskSpan's market risk management team has transformed portfolio risk analytics through distributed cloud computing. Our optimized infrastructure powers risk and scenario analytics at speeds and costs never before possible in the industry. Still want more? Take a look at our portfolio market...

CTO Suhrud Dagli | Risk-as-a-Service

Managing Risk Data: Financial Instrument Terms and Conditions

An instrument’s terms and conditions lie at the heart of cash flow generation and valuation. Not surprisingly, errors in terms and conditions can drive errors in valuation. Fortunately, fixing these errors is often straightforward, provided the terms and conditions data is readily available, which is not always the case for private placement instruments.

Data Management for a Robust Risk Framework

In an article published last year, the Harvard Business Review quotes IBM research that estimates that bad data costs US business $3 Trillion per year. Although it is difficult to identify the specific cost associated with bad data in market-risk management, it is obvious that managing data has never been more important. The success of a market-risk management implementation is largely dependent on a validated, scalable, and well-governed data management process.

Calculating VaR: A Review of Methods

Many firms now use Value-at-Risk (“VaR”) for risk reporting. Banks need VaR to report regulatory capital usage under the Market Risk Rule, as outlined in the Fed and OCC regulations [1] and [2]. Additionally, hedge funds now use VaR to report a unified risk measure across multiple asset classes. There are multiple approaches to VaR, so which method should we choose? In this brief paper, we outline a case for full revaluation VaR in contrast to a simulated VaR using a “delta-gamma” approach to value assets.

Portfolio Analytics Risk Service: Vendor Considerations

There is movement in the vendor market for Risk Analytics. Barclays is divesting its POINT risk analytics system and Capital IQ has exited the risk business. These changes have prompted the market to consider vendor alternatives and the timing for a fresh look at solutions couldn’t be better. The regulatory environment persists. There is a constant stream of demand from investors and regulators for independent risk analysis and reporting. Further, the emergence of new data management tools and the declining cost of cloud-managed hardware presents the market with an opportunity to scale data processing and reduce costs.