Sumit Sourabh

CEO

Amsterdam, North Holland, Netherlands14 yrs 3 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Over a decade of experience in financial markets.
Expert in quantitative models for pricing and risk management.
Strong leadership and collaboration skills in cross-functional teams.

Stackforce AI infers this person is a Fintech expert with strong quantitative finance and risk management capabilities.

Contact

sumit.sourabh@ing.com LinkedIn

Skills

Core Skills

Risk AnalyticsQuantitative FinancePredictive AnalyticsMachine LearningAlgorithm DevelopmentEpistemic ReasoningStatistical TechniquesAlgorithm Implementation

Other Skills

AlgorithmsAnalyticsArtificial IntelligenceBig DataCC++CommunicationComputer ScienceData AnalysisData MiningDerivativesEconometricsFinanceFormal LanguagesFormal Logic

About

With over a decade of experience in front office financial markets, I lead quantitative initiatives that enhance pricing and risk management across multi-asset derivative portfolios. My core strength lies in designing and deploying advanced quantitative models that drive efficiency in pricing, risk mitigation, and xVA strategies. I’m passionate about transforming complex data into actionable insights that support front-office decision-making and strategic growth. In addition to my technical expertise, I have demonstrated strong leadership and collaboration skills, working closely with Trading & Sales desks, Risk model developers, Pricing model validators, and IT teams. I am also passionate about mentoring junior talent and fostering a culture of innovation.

Experience

14 yrs 3 mos

Total Experience

3 yrs 7 mos

Average Tenure

9 yrs 3 mos

Current Experience

Ing

4 roles

Director

Promoted

Apr 2024 – Present · 2 yrs 1 mo

Developed enhancements for FRTB CVA and co-developed CIC pricing for FRTB SA-CVA capital
which significantly reduces capital reserves and aligns pricing with the regulatory capital
On-boarded cross-section CDS proxy tool including model monitoring in RaceING and integration of
market data from DMD which improves trade level pricing in RaceING
Supervision of innovation projects such as ongoing CIC optimization project to suggest trades which
lower CIC across client portfolios, RaceING GUI sensi runs optimization and effective CDS hedging

Risk AnalyticsFinanceDerivativesQuantitative FinanceData Analysis

Vice President

Promoted

Apr 2020 – Mar 2024 · 3 yrs 11 mos

Coordinated on-boarding and validation of additional inflation indices and OOS which significantly reduced BVA reserves
Collaborated with FO quants, risk model development, and CEM desk to come up with a consistent
funding framework for xVA after a fundamental review of the xVA model
Led an extensive assessment of our current wrong-way risk model to come up with its shortcomings
and co-developed a challenger model to monitor the portfolio level WWR impact on a periodic basis

Risk AnalyticsFinanceDerivativesQuantitative Finance

Associate

May 2017 – Mar 2020 · 2 yrs 10 mos

Implemented transition of xVA models from LIBOR to the new RFR interest rate framework which
allows FM to trade these products on behalf of clients
Implemented the ISDA SIMM model for calculating initial margin which is a pre-trading condition
with streetnames for bilaterally cleared OTC derivatives
Extensively involved in development of a client income charge based pricing framework which allows
ING to effectively quantify FM income per client

Risk AnalyticsFinanceQuantitative Finance

Technology Lead | ING Wholesale Banking Innovation

Nov 2016 – Apr 2017 · 5 mos

As the technology lead within the ING Wholesale Banking Collateral Optimisation accelerator
project, I developed a model for collateral optimisation using predictive analytics

Predictive AnalyticsFinance

University of amsterdam and quant analytics, ing bank

2 roles

Postdoctoral Researcher in Quantitative Finance and Machine Learning

Oct 2015 – Oct 2016 · 1 yr

Mathematical and data-based modelling to support trading and credit risk management within the quantitative analytics team at ING Bank in association with the H2020 EU BigData Finance project: “Machine Learning for Trading and Risk Management”.

MathematicsMachine Learning

PhD Candidate, Mathematics

Sep 2011 – Sep 2015 · 4 yrs

PhD research on developing algorithmic-algebraic methods for reasoning in formal systems funded by an European Union Erasmus Mundus scholarship.
Developed algorithms to provide an automated correspondence between extensions of modal logic and first-order logic for tractable query answering in databases and proving completeness of formal systems.
Published 8 research articles in top-tier journals, taught graduate level courses, presented talks in international conferences and supervised master theses. For a complete list of publications and talks, see http://staff.fnwi.uva.nl/s.sourabh.

Algorithm DevelopmentFormal Logic

Google

Google Summer of Code (GSoC)

May 2015 – Aug 2015 · 3 mos · Amsterdam Area, Netherlands

As a part of Google Summer of Code project, I developed an epistemic reasoning model for Probabilistic Logic Network (PLN) framework within OpenCog, which is an open source cognitive platform.
Implemented the model in Scheme and Python.

Epistemic ReasoningProbabilistic Logic

Bravo lucy as

Internship

Apr 2011 – May 2011 · 1 mo · Hyderabad Area, India

As a part of supply chain analytics team, I developed models for predicting optimal orders for new product introduction using statistical and machine learning techniques, followed by their implementation in R.

Statistical TechniquesMachine Learning

Max planck institute for informatics

Research Assistant

May 2010 – Jul 2010 · 2 mos · Saarbrücken Area, Germany

• Developed a novel formula renaming algorithm in C++ for SPASS, an automated theorem prover, which considerably reduces the number of clauses, thereby lowering computational complexity.

Algorithm DevelopmentC++

Indian statistical instiute, kolkata

Research Assistant

Jun 2007 – Jul 2007 · 1 mo · Kolkata Area, India

This research project was a comparative study and implementation of the various algorithms for (k, n) Visual Threshold Scheme and general access structures using MATLAB. We proposed a novel strategy to reduce the pixel expansion and fine tune the image obtained after obtaining the result.

Algorithm ImplementationMATLAB