“Always protect your downside, upside will take care of itself”
Protecting your downside is what I like to do as a profession, it’s the essence of working as a risk manager, it’s something that continues to excite and challenge me!
Growing up as a child, I was always fascinated my numbers and mathematics, somehow I was grasping mathematical concepts very easily . This fascination led me to pursue my undergrad in Mathematics and Statistics at Indian Institute of Technology Kanpur (IITK). Post that, I worked in actuarial science within the insurance/reinsurance industry applying simulation models to understand our insurance portfolio losses. In 2015, after deciding to pursue a career in finance instead, I came to study Master in Financial Engineering (MFE) at Anderson School of Management, UCLA. Looking back, this was one of the best decisions I’ve made given the impact the program has had on my professional growth. The ecosystem that was made accessible via the MFE program - world class professors, networking opportunities, supportive alumni, career services, lots of assignment (painful but helpful), diverse mix of student groups to work with etc. is truly remarkable and gave me a strong foundation for my journey in Finance and FinTech.
Post MFE graduation, I worked as a Portfolio Risk Quant at Western Asset Management, where the risk team was responsible building risk systems for managing more than $440Bn of client assets. The team was full of smart people with great academic backgrounds and lots of experience in finance, which really helped me to speed up my learning curve quickly. My job was to help senior researchers and risk officers with improving our existing factor risk models, optimizing the parameters used for forecasting, experimenting with various portfolio simulation techniques and developing risk attribution reports for risk intelligence reporting to the risk committee on a monthly cadence. Fixed Income markets were something I was never exposed to in my life, so it was very interesting to learn about interest rate models and credit spread impacts on our portfolios.
Crypto Quant:
During the 2020 pandemic, I began to read about blockchain technology and was very intrigued by the prospects of using blockchain tech in finance. This curiosity eventually made me take the big leap, I decided to quit my secure sob at Western Asset Management and pursue a Quantitative Risk Manager role at BlockFi, a crypto native fintech that provides lending services to retail and institutions. BlockFi was a high-flying crypto firm (back in 2021) valued close to $3bn, announced a Series D round within a few months of my joining. My role as a quant manager was to look after the risk profile for the institutional and retail loan book that were collateralized by crypto tokens like BTC, ETH, LINK etc. This involved building the Value-At-Risk models and balance sheet shortfall analysis for firm executives. Additionally, I was working closely with the trading team to help build the systematic crypto yield strategies using options and futures. I enjoyed the flexibility of working without any hierarchy and defining how things should be built from scratch.
Managing risk of an asset that can move 10-15% overnight wasn’t easy but I was enjoying the challenge more than stressing about it, comforted with the fact that I worked on volatility forecasting models for currency risk factors at my previous role, I was comfortable dealing with a volatile asset class. May 2021 turned out be a very bad month for crypto as the drawdowns in price was more than 40% and we had a chance to test our models in real-time scenario. During my tenure, I worked on building a risk framework for limiting our losses for trading and lending books. It was quite a learning journey within the span of few months given the levels of volatility we were witnessing and the business was growing rapidly.
Over time, I was getting concerned by the lack of risk culture at the firm, although I enjoyed building risk models and providing risk insights to the team, I never found a healthy appreciation for much of my risk work as the firm was more focused on growing the clients loan book by underwriting more loans and not adequately focused on what are risks arise with approving various low market cap tokens for collateral. I wasn’t prepared for working in such culture given my experience at Western Asset highlighted the importance of having a strong risk culture at the firm. Eventually, after trying to voice my concerns internally and not finding enough success, I decided to pursue outside opportunities where I can apply my quant skills to a better use and place where a more established risk team was in place already. I understood that being a first risk person in the team requires lot more than just building models, a supportive structure is a must to excel!
Treasury Risk Management:
In late 2021, I joined Stripe, the payments fintech in Silicon Valley. It was one of the best organizations I have worked at given the internal work culture they incubated over the years. My role was a mix of quant and data scientist work applied for currency risk management. Currency risk is one of the key risks along with fraud risk for a payments firm. As a firm, Stripe helps merchants grow their online business by providing the tools and services to accept payments online both domestically and cross border. Stripe pays the merchant upfront and works with card networks on the back-end to ensure transactions are settled correctly. The risk arises if the currency rates that we paid to the merchant are different to the currency rate we received at settlement. This can adversely impact our P&L (profit and loss) as Stripes processes millions of volume per currency every day. One of the ways we can mitigate this is by applying a risk premium to the rate we charge the merchant:
Final Rate = Base Currency Rate + Bank Spread + Card Network Cost + Risk Premium
Risk Premium is the component of the rate that accounts for volatility of the currency and helps us protect our profit margins if currency rates move against our favor during the time it takes to settle the transaction (1 or 2 days). Applying the traditional currency volatility forecasting models to predict the future range of possible currency rates (using simulation techniques) is one way to estimate the risk premium needed to protect against drastic moves in the markets. We used the simple time series forecasting models to predict the currency rates at 99% confidence levels as a reasonable proxy for setting the limits.
Key Learnings:
As I worked in large organization like Stripe, I realized the importance of cross-collaboration with partner teams. It’s one challenge to build a model but it’s an equal challenge to get other teams to align with vision and prioritize your project needs over others. This is where proper communication with the product managers and engineering managers matter. And to help you with this, it’s important to maintain proper notes/documentation outlining:
Trends to Watch:
Having worked in both Crypto and FinTech start-ups, I have been fortunate enough to notice some key trends that I believe will shape the future of finance going forward, this also presents a great opportunity for talented professionals to apply their Quant and Data Science skills to solve interesting problems for meeting customer financial needs. Here are my top 5 trends that according to me:
Protecting your downside is what I like to do as a profession, it’s the essence of working as a risk manager, it’s something that continues to excite and challenge me!
Growing up as a child, I was always fascinated my numbers and mathematics, somehow I was grasping mathematical concepts very easily . This fascination led me to pursue my undergrad in Mathematics and Statistics at Indian Institute of Technology Kanpur (IITK). Post that, I worked in actuarial science within the insurance/reinsurance industry applying simulation models to understand our insurance portfolio losses. In 2015, after deciding to pursue a career in finance instead, I came to study Master in Financial Engineering (MFE) at Anderson School of Management, UCLA. Looking back, this was one of the best decisions I’ve made given the impact the program has had on my professional growth. The ecosystem that was made accessible via the MFE program - world class professors, networking opportunities, supportive alumni, career services, lots of assignment (painful but helpful), diverse mix of student groups to work with etc. is truly remarkable and gave me a strong foundation for my journey in Finance and FinTech.
Post MFE graduation, I worked as a Portfolio Risk Quant at Western Asset Management, where the risk team was responsible building risk systems for managing more than $440Bn of client assets. The team was full of smart people with great academic backgrounds and lots of experience in finance, which really helped me to speed up my learning curve quickly. My job was to help senior researchers and risk officers with improving our existing factor risk models, optimizing the parameters used for forecasting, experimenting with various portfolio simulation techniques and developing risk attribution reports for risk intelligence reporting to the risk committee on a monthly cadence. Fixed Income markets were something I was never exposed to in my life, so it was very interesting to learn about interest rate models and credit spread impacts on our portfolios.
Crypto Quant:
During the 2020 pandemic, I began to read about blockchain technology and was very intrigued by the prospects of using blockchain tech in finance. This curiosity eventually made me take the big leap, I decided to quit my secure sob at Western Asset Management and pursue a Quantitative Risk Manager role at BlockFi, a crypto native fintech that provides lending services to retail and institutions. BlockFi was a high-flying crypto firm (back in 2021) valued close to $3bn, announced a Series D round within a few months of my joining. My role as a quant manager was to look after the risk profile for the institutional and retail loan book that were collateralized by crypto tokens like BTC, ETH, LINK etc. This involved building the Value-At-Risk models and balance sheet shortfall analysis for firm executives. Additionally, I was working closely with the trading team to help build the systematic crypto yield strategies using options and futures. I enjoyed the flexibility of working without any hierarchy and defining how things should be built from scratch.
Managing risk of an asset that can move 10-15% overnight wasn’t easy but I was enjoying the challenge more than stressing about it, comforted with the fact that I worked on volatility forecasting models for currency risk factors at my previous role, I was comfortable dealing with a volatile asset class. May 2021 turned out be a very bad month for crypto as the drawdowns in price was more than 40% and we had a chance to test our models in real-time scenario. During my tenure, I worked on building a risk framework for limiting our losses for trading and lending books. It was quite a learning journey within the span of few months given the levels of volatility we were witnessing and the business was growing rapidly.
Over time, I was getting concerned by the lack of risk culture at the firm, although I enjoyed building risk models and providing risk insights to the team, I never found a healthy appreciation for much of my risk work as the firm was more focused on growing the clients loan book by underwriting more loans and not adequately focused on what are risks arise with approving various low market cap tokens for collateral. I wasn’t prepared for working in such culture given my experience at Western Asset highlighted the importance of having a strong risk culture at the firm. Eventually, after trying to voice my concerns internally and not finding enough success, I decided to pursue outside opportunities where I can apply my quant skills to a better use and place where a more established risk team was in place already. I understood that being a first risk person in the team requires lot more than just building models, a supportive structure is a must to excel!
Treasury Risk Management:
In late 2021, I joined Stripe, the payments fintech in Silicon Valley. It was one of the best organizations I have worked at given the internal work culture they incubated over the years. My role was a mix of quant and data scientist work applied for currency risk management. Currency risk is one of the key risks along with fraud risk for a payments firm. As a firm, Stripe helps merchants grow their online business by providing the tools and services to accept payments online both domestically and cross border. Stripe pays the merchant upfront and works with card networks on the back-end to ensure transactions are settled correctly. The risk arises if the currency rates that we paid to the merchant are different to the currency rate we received at settlement. This can adversely impact our P&L (profit and loss) as Stripes processes millions of volume per currency every day. One of the ways we can mitigate this is by applying a risk premium to the rate we charge the merchant:
Final Rate = Base Currency Rate + Bank Spread + Card Network Cost + Risk Premium
Risk Premium is the component of the rate that accounts for volatility of the currency and helps us protect our profit margins if currency rates move against our favor during the time it takes to settle the transaction (1 or 2 days). Applying the traditional currency volatility forecasting models to predict the future range of possible currency rates (using simulation techniques) is one way to estimate the risk premium needed to protect against drastic moves in the markets. We used the simple time series forecasting models to predict the currency rates at 99% confidence levels as a reasonable proxy for setting the limits.
Key Learnings:
As I worked in large organization like Stripe, I realized the importance of cross-collaboration with partner teams. It’s one challenge to build a model but it’s an equal challenge to get other teams to align with vision and prioritize your project needs over others. This is where proper communication with the product managers and engineering managers matter. And to help you with this, it’s important to maintain proper notes/documentation outlining:
- The problem you are trying to tackle.
- Explaining the solution in detail.
- Estimating the impact on the business (ROI)
- Defining the success metric for the initiative.
- Execution Timeline - Break down the project in phases.
Trends to Watch:
Having worked in both Crypto and FinTech start-ups, I have been fortunate enough to notice some key trends that I believe will shape the future of finance going forward, this also presents a great opportunity for talented professionals to apply their Quant and Data Science skills to solve interesting problems for meeting customer financial needs. Here are my top 5 trends that according to me:
- Embedded Finance: Integration of financial services like lending, payment processing or insurance into nonfinancial businesses without the need to redirect to traditional financial institutions.
- Robo-Advising: Providing financial planning services through automated algorithms with no human intervention using AI techniques.
- Buy Now Pay Later: Services that provide flexible installment payment options for consumers to better manage their spending over time, thereby helping merchants with higher conversions.
- Tokenization of Assets: The use of blockchain technology to create digital tokens that are backed by ownership of real assets, thereby unlocking more liquidity and creating efficient markets for real asset exchange.
- Decentralized Finance: Emerging technology that provides financial services like lending, investing, insurance, yield enhancement without any central governing entity.
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