Portfolio Valuation for a Retail Bank using Monte Carlo Simulation and Forecasting for Risk Measurement

Abstract: 

Banks today need to have a very good assessment of their portfolio value at any point in time. This is both a regulatory requirement and an operational metrics which helps banks to assess the risk of their portfolio and also calculate the Capital Adequacy that they need to maintain at portfolio levels, product levels, and all of these aggregated at the Bank level.
This presentation will walk you through a case study that will discuss in detail how we went about calculating the Portfolio value for a Home loan on sample data. The bank wanted a scientific /statistical approach to this as they could take this to regulators for approval and thus convince them about the capital that they have for a particular portfolio.

The other interesting dimension was that in case the bank wants to sell a particular loan book to another bank /third party financial institutions they would be able to quote a price within the confidence interval of the calculated price. The same model/tool could be also shared with the buyer to convince them on quoted price and will make the negotiation and selling smooth.
We have used Monte Carlo Simulation on historical data of the portfolio to measure the Portfolio Value for the next 5 years of a Home loan Portfolio. It is a two-step modeling process with Machine Learning Models to predict default and then further using simulation to calculate Portfolio value year on year for next 5 yrs taking into account diminishing returns too.

The presentation will take you through the approach and modeling process and how Monte Carlo Simulation helped us deliver the same to Customer with high accuracy and confidence level. This is a real case study and will focus on why Risk Measurement is important and why Basel, CCAR implementation across banks worldwide helps the Central Banks to manage risks in case of a financial downturn or Black Swan events.

Bio: 

Kavita is an Analytics leader with 12 + years of core hands-on experience having an excellent track record on Presales, Partner Management, Analytics Delivery, and Team management across domains in World-Class Organizations. Currently, she is heading the Data Science function at Infinite Sum Modeling. She is a Chemical Engineer by education followed by a Masters (Eco) from IGIDR. She is a seasoned analytics professional with work experiences across companies like Fair Isaac, Experian, Accenture, Infosys, and Vodafone. Her vast experience in domains like Banking, Insurance, Telecom, Fraud, and Risk Management gives her the right kind of diversification. She has published papers in areas of Financial Econometrics and Social Media Analytics. She has been an esteemed speaker at various national seminars on Analytics. Her passion for Analytics and learning drives her to explore newer technologies and innovations in the Analytics space. She is currently leading the capabilities of the firm in the areas of data science and analytics training as well as new business development in several areas that include data science, business analytics, Artificial Intelligence, Data Mining, Machine learning, blockchain, robotic process automation, econometric modeling, CGE modeling, policy/business strategy analysis, among other areas of interest to the company.