Financial Risk Analytics

Get in Touch

Addressing evolving risk and regulatory capital requirements with scalable and modular cloud solutions

Financial Risk Analytics provides products and solutions to financial institutions to measure and manage their counterparty credit risk, market risk, regulatory risk capital and derivative valuation adjustments. Using the latest analytics and technology such as a fully vectorized pricing library, Machine Learning and a Big Data stack for scalability, our financial risk analytics products and solutions are used by the largest tier-one banks to smaller niche firms. Our risk analytics solutions are available deployed, in the cloud or can be run as a service so we free up your internal resources to focus on your business priorities.

Learn more about Financial Risk Analytics

Put risk at the forefront of your investment decisions Explore the Buy Side Risk Solution

LEARN MORE

Financial Risk Analytics For sell-side traded markets and buy-side

DOWNLOAD BROCHURE

Financial Risk Analytics

{ "speed": 600, "fade": true, "dots": false, "arrows": false, "autoplay":false, "autoplaySpeed":4000 }

Advantages of our Financial Risk Analytics Solutions

Trusted Data, Analytics and Expertise

Gain unique insights with our curated data and analytics combined with your internal or third-party data

Cloud compatible across major providers

Pay only for what you use with technology built to scale up or scale down depending on your needs

Reduce Total Cost of Ownership (TCO)

Leverage our platform’s turnkey capabilities and project acceleration track-record without sacrificing flexibility

View our financial risk analytics product set

{"slidesToScroll": 1,"dots": true,"arrows": true,"autoplay":false}

Traded Market Risk

Supports regulatory and market risk requirements

LEARN MORE

XVA

A flexible, cloud-based solution delivering deal-time insights to the XVA desk

LEARN MORE

Counterparty Credit Risk

A flexible, cloud-based solution that supports counterparty credit risk and regulatory capital requirements

LEARN MORE

Buy Side Risk

Real-time valuation and aggregation for market risk across extensive asset classes. A fast, cloud-based risk simulation engine for the buy-side.

LEARN MORE

Videos

Sell Side Technology Awards 2023 Interview

Mark Findlay at Risk Minds 2022

Chartis RiskTech Buyside 50

RiskTech100 2022: Winner's Interview

FRTB challenges for banks

Thought Leadership/Case Studies

Financial Risk Analytics FAQ

What is XVA?

X-Value Adjustment (XVA) is a collective term for “valuation adjustments” made to derivative trades to reflect various costs related to the trade. The first valuation adjustment was Credit Value Adjustment (CVA); which reflect the credit risk for a given counterparty of a trade. Since the creation of CVA, additional valuation adjustments have been created to capture the cost of funding (FVA), cost of your party defaulting (DVA), cost of collateralization (KVA) and the cost of initial margin (MVA).

What is SA-CCR?

The Standardized Approach for counterparty credit risk (SA-CCR) is the capital requirement framework under Basel III addressing counterparty risk. The SA-CCR exposure-at-default calculation drives a bank's regulatory capital calculations and is a measure of counterparty credit risk as it calculates the exposure at default (EAD) of derivative trades with counterparties. SA-CCR is calculated as if a counterparty were to default today as the current value of the trade plus an add-on which measures potential future exposure (PFE). The framework replaced both non-internal model approaches (non-IMA): the current exposure method (CEM) and the standardized method (SM).

What is FRTB?

The Fundamental Review of the Trading Book (FRTB) refers to a comprehensive restructuring of market risk regulatory capital requirements published by the Basel Committee on Banking Supervision (BCBS) between 2016 and 2019 in response to the financial crises. After a few iterations since 2016, BCBS published the final version of the framework in January 2019, and local regulators are starting to release their final translation of FRTB into local law.

With FRTB, banks will have to comply with new rules by 1 January 2023, but a lot of retro planning, implementation and model validation work as well as regulatory approval is required in order to publish official capital numbers by this date. Banks operating trading books will have to confirm to their respective supervisory authorities whether they wish to pursue a Standardized Approach (SA) calculation or obtain approval for an Internal Model Approach (IMA) for each desk in scope.

What is the difference between FRTB and previous regulations?

FRTB introduces a number of changes for market risk capital requirements including stricter boundaries between the bank’s Trading and Banking Book allocations (i.e. for active trading vs. held to maturity) and fewer possibilities to move trades between them. Instead of a top-of-the-house model approval, banks will have to seek approval at the level of each Regulatory Trading Desk as well as calculating consolidated capital requirements either under the Standardized Approach (SA) or the Internal Model Approach (IMA). Risk Factors to be included in IMA calculations will need to be evidenced as derived from sufficiently observable or liquid instruments (thereby becoming “modellable”), else banks will have to calculate a Stressed Expected Shortfall (SES) add-on for them instead.

The IMA framework also switches from the usual VaR metric to an Expected Shortfall-based risk measure based on the risk type and associated liquidity horizon. In addition, eligible desks will need to demonstrate they pass not only back testing requirements but also a P&L Attribution test designed to reduce any gaps between front-office and risk models which will often increase risk factor granularity compared to existing VaR models. Failing these criteria, desks will have to fall back to the new Standardized Approach which although still based on close-ended sensitivity-based calculations, still increases in complexity and requires implementation changes.

What is ISDA SIMM?

The International Swaps and Derivatives Association (ISDA) standard initial margin mode (SIMM) is a risk sensitivity-based approach to calculate initial margin (IM) for uncleared derivatives ISDA providing model parameters to calibrate for each product class on historical data considering a period of relevant financial stress. Over-the-counter (OTC) derivatives are categorized into one and only one of the following four Product Classes: RatesFX, Credit, Equity, and Commodity. Under ISDA SIMM, initial margin is calculated using sensitives as inputs. This includes Delta and Vega such that the initial margin would cover 99% confidence given a 10-day margin period of risk. For each product class covered ISDA specifies which sensitivities are required. For Equity, Commodities & FX spot prices are bumped 1% to calculate deltas, and volatility is shifted 1% to calculate vegas. For Interest Rates, curves are bumped at 12 points to calculate deltas.

What is Scenario Stress Testing?

A bank stress test is an analysis conducted under hypothetical unfavorable economic scenarios, such as a deep recession or financial market crisis, designed to determine whether a bank has enough capital to withstand the impact of adverse economic developments. The importance of robust and rigorous stress testing has moved to the top of the agenda of risk management priorities with many banks having to comply with multiple regulatory regimes i.e. US CCAR & DFA, UK BoE/PRA and ECB/EBA requirements.

In contexts where firms have discretion over the design of scenarios, they must make a large number of subjective decisions around scenario definition. Scenario Stress Testing (SST) allows users to specify hypothetical, historical or regulatory shocks and revalue their portfolio to understand market-market (MTM) loss and changes to counterparty credit exposure. Using Scenario Stress Testing, users can specify shocks to a subset of risk factors which are expanded to all the risk factors in their portfolio through a combination of rules and statistical methods. Banks construct scenarios thus allowing stress testing to be a pro-active process they can use for internal risk and business planning.

Leadership Team

Vinay Nayak

Chief Technology Officer, Financial Risk Analytics, S&P Global Market Intelligence

Stuart Nield

Global Head of Product, Financial Risk Analytics, S&P Global Market Intelligence

Allan Cowan, Ph.D.

Global Head, Data Analytics, Financial Risk Analytics, S&P Global

Vikas Valand

Global Head of Professional Services and Customer Operations, Financial Risk Analytics, S&P Global Market Intelligence

{"items" : [ {"name":"facts","url":"https://cdn.ihsmarkit.com/www/pdf/1122/620886153-1022-CU-FS-FinancialRisk-Analytics-Brochure-SPGI-U1-Final-HiRes.pdf","enabled":true,"desc":"Download product information","alt":"Download product information","mobdesc":"PDF","mobmsg":"Download product information"},{"name":"login","url":"","enabled":false,"desc":"Product Login for existing customers","alt":"Login","large":true,"mobdesc":"Login","mobmsg":"Product Login for existing customers"},{"name":"sales","override":"","number":"[num]","enabled":true,"desc":"Call Sales [num]","proddesc":"[num]","alt":"Call Sales</br>[num]","mobdesc":"Sales","mobmsg":"Call Sales: [num]"}, {"name":"share","enabled":true,"desc":"<strong>Share</strong>","mobdesc":"Share","options":[ {"name":"facebook","url":"https://www.facebook.com/sharer.php?u=http%3a%2f%2fstage.www.spglobal.com%2fmarketintelligence%2fen%2fmi%2fproducts%2ffinancial-risk-analytics.html","enabled":true},{"name":"twitter","url":"https://twitter.com/intent/tweet?url=http%3a%2f%2fstage.www.spglobal.com%2fmarketintelligence%2fen%2fmi%2fproducts%2ffinancial-risk-analytics.html&text=Financial+Risk+Analytics+for+Market+Risk+%26+Credit+Risk+%7c+S%26P+Global","enabled":true},{"name":"linkedin","url":"https://www.linkedin.com/sharing/share-offsite/?url=http%3a%2f%2fstage.www.spglobal.com%2fmarketintelligence%2fen%2fmi%2fproducts%2ffinancial-risk-analytics.html","enabled":true},{"name":"email","url":"?subject=Financial Risk Analytics for Market Risk & Credit Risk | S&P Global&body=http%3a%2f%2fstage.www.spglobal.com%2fmarketintelligence%2fen%2fmi%2fproducts%2ffinancial-risk-analytics.html","enabled":true},{"name":"whatsapp","url":"https://api.whatsapp.com/send?text=Financial+Risk+Analytics+for+Market+Risk+%26+Credit+Risk+%7c+S%26P+Global http%3a%2f%2fstage.www.spglobal.com%2fmarketintelligence%2fen%2fmi%2fproducts%2ffinancial-risk-analytics.html","enabled":true}]}, {"name":"rtt","enabled":true,"mobdesc":"Top"} ]}

Financial Risk Analytics

Addressing evolving risk and regulatory capital requirements with scalable and modular cloud solutions

Financial Risk Analytics

Advantages of our Financial Risk Analytics Solutions

Trusted Data, Analytics and Expertise

Cloud compatible across major providers

Reduce Total Cost of Ownership (TCO)

View our financial risk analytics product set

Traded Market Risk

XVA

Counterparty Credit Risk

Buy Side Risk

Videos

Thought Leadership/Case Studies

S&P Global Market Intelligence Wins Best FRTB Solution in Regulation Asia Awards

S&P Global Market Intelligence retains top-10 position in the Chartis RiskTech100 2024

Basel III Endgame - Or the prologue to a New game?

Basel III endgame arrives: Preparing for FRTB in the US

Buy Side Risk Solution wins multiple awards in the Chartis RiskTech Buyside 50 2023

Navigating the Challenges and Nuances of Hedging in SA-CVA

S&P Global Market Intelligence wins Waters Sell-Side Technology Awards 2023

A Framework for Buy-side Risk Managers: Constructing Inflationary Stress Tests

The future of XVA desks in volatile markets

How can banks reduce regulatory risk capital in volatile markets?

Chartis RiskTech BuySide 50: Financial Risk Analytics Wins Investment/Market Risk Category

Financial Risk Analytics wins Market Risk (Buy Side) category in the RiskTech100 awards

Improving XVA Accuracy with Empirical Martingale Simulation

LIBOR Transition and FRTB Modellability

Can Risk Engines Approximate their RFR Payoffs?

SEC 18F-4: What it is and what fund managers need to consider

xVA Neural Net Engine - Machine Learning Accelerated xVA Simulations

FRTB Roundtable: what you may have missed in our virtual roadshows

Fixing of LIBOR Fallback Spreads

Option prices and downside odds

Convergence of LIBOR fallback spreads

What will drive capital markets firms to migrate to cloud in 2020 and beyond?

Flattening the (Funding) Curve

Financial Risk Analytics FAQ

Leadership Team

Vinay Nayak

Stuart Nield

Allan Cowan, Ph.D.

Vikas Valand