Lokesh Pathairia

Associate Partner

Cracow, Małopolskie, Poland9 yrs 7 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in Treasury risk management and stress testing.
Proficient in validating liquidity and funding models.
Strong background in risk modeling for financial products.

Stackforce AI infers this person is a Treasury Risk Management expert in the Fintech industry.

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Skills

Core Skills

Liquidity Risk ManagementTreasury ManagementMarket Risk

Other Skills

IRRBBfund tranfer pricingInterest Rate Risk ManagementDCF ValuationInvestment BankingMachine LearningFinancial AnalysisSwaptionsOption Pricing ModelsForeign Exchange (FX) OptionsFinancial ModelingQuantitative FinanceExotic DerivativesStatistical ModelingForecasting

About

Lokesh Pathairia is a Treasury model Risk management professional in Interest rate risk in Banking Book, Treasury risk management for BAU and Stress testing scenarios (CCAR and PRA). Previous to this he was associated with Liquidity and Funding Model Validation profile at UBS, Mumbai, and also with JP Morgan Chase Model Risk team. He is a seasoned analytics professional with expertise in validating Investment Bank's Liquidity and Funding assessment and IRRBB under stress scenarios. He is also well-versed in Global finance business risk management (Credit risk, Market risk, Liquidity risk). Primarily, he understands global regulatory requirements and expectations for the firm’s Treasury Risk, including both Interest rate risk and Liquidity and Funding risk metrics. While working in Global Investment Banks like HSBC, JP Morgan and UBS he got expertise in risk modeling of financial products like Loans, Secured funding transactions, Prime Brokerage, and Deposits, and got technical knowledge of pricing of complex derivatives like Swaptions, stochastic interest rate modeling, financial mathematics. Lokesh holds a Master's degree MBA from IIM Shillong. He did his bachelor's B-Tech in Electrical Engineering from IIT Kanpur. His technical expertise includes proficiency in Quantitative analysis, Validation of Liquidity Risk management models, IRRBB (NII and EVE), Statistical modelling (ARIMA, GARCH).

Experience

9 yrs 7 mos

Total Experience

1 yr 11 mos

Average Tenure

2 yrs 2 mos

Current Experience

Hsbc

2 roles

Senior AVP, Model Risk Management

Promoted

Jul 2025 – Present · 10 mos · Cracow, Małopolskie, Poland

Liquidity Risk ManagementIRRBBfund tranfer pricingTreasury Management

AVP, Model Risk Management

Nov 2021 – Mar 2023 · 1 yr 4 mos · Cracow, Małopolskie, Poland

Asset, Liability and Cash management
IRRBB
Asset management

Liquidity Risk ManagementMarket RiskTreasury ManagementInterest Rate Risk Management

Ubs

AO

Dec 2018 – Oct 2021 · 2 yrs 10 mos · Mumbai Area, India

Model Validation || Treasury- (Liquidity and Funding Risk), performs risk modelling of banking products like Prime Brokerage, Securities financing transactions (SFTs), Derivatives, Deposits, Financial Market Utilities and other banking products.

Liquidity Risk ManagementMarket RiskTreasury ManagementInterest Rate Risk Management

Jpmorgan chase & co.

Sr. Analyst

Apr 2017 – Nov 2018 · 1 yr 7 mos · Bengaluru Area, India

Pricing of Bermudan swaptions
Structured products
Fx derivatives
Model Validation
Option pricing

Ibm

Project Trainee

Apr 2016 – Jul 2016 · 3 mos · Bengaluru Area, India

Strategy: Digital Sales

Accenture

Analytics Analyst

Jul 2014 – Jun 2015 · 11 mos · Gurgaon, India

Mentored a team of 2, formulated econometric modelling solutions for clients in telecom, retail, toy manufacturing sector
 Analysed impact of marketing and promotional channels on sales and traffic for a major US retail client using SAS/R
 Recommended and optimised the allocation of campaign spends for various marketing channels over linked divisions

Meritus analytics, a wpp company

Senior Analyst

May 2012 – Jun 2014 · 2 yrs 1 mo · Bangalore Area, India

Client: Multinational Consumer goods company having operations in over 100 countries
Optimising the ROI solutions by building Analytical models using various analytic studies like Market Mix Modelling, Bayesian, Category/Demand Forecasting , Vector Auto regression, multivariate regression etc.
(A)Market Mix Modelling
> Involves an Exploratory Data Analysis to check for the outliers & probable drivers of marketing activities(Price, Distribution, TV spends, Promotions etc) and determine the effectiveness of each driver on a brand's sales using analytical tools like SAS , R
>Recommending on key business issues based on the findings of Regression/Bayesian models
(B)Price and Promotion framework
>Developed a stable modelling structure using a multiplicative model to tackle the non-linear relationship between sales and price/promotion for a store-level data
>Determine promotional sales lift and ROI for each in-store event like Display, feature advertising
>Quantify sales to base price and promoted prices including macroeconomic environment, trend seasonality
(C)Demand/Category forecasting
>Involves Predictive analytics (Demand Forecasting) dedicated to understanding consumer demand for goods or services in the future based on Price assumption, Economic growth of the countries (measured by macroeconomic variables such as GDP, Money Supply, Earning Average etc.) and some extraneous factors such as trends and seasonality using techniques such as UCM, ARIMA.
(D)Integrated Marketing ROI (IMR)
>To tackle the multiple study syndrome formulated a stable IMR framework, acts as a bridge between MMM & Awareness modelling
>Focused on the soft metrics(Consumer perception of the brand) along with the hard metrics(Impressions,Pay per click) transparently understanding consumer habits
>Capture the long term effect of tangible marketing drivers(Price,Media,Promotion) using Impulse response function
>Channelling the media spends on a specific touch point