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

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 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 Bank level.

This presentation will walk you through a case study which will discuss in detail how we went about calculating Portfolio value for a Home loan on a 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 in 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.


Outline/Structure of the Demonstration

1. Importance of Risk Assessment by Banks/ Financial Institutions - 2-3 mins

2. How Central Banks use the Regulatory Frame work to measure overall Capital Adequacy across Banks/FIs? - 2-3 mis

3. Discussion of a real case study of a Bank where the need was of Portfolio Valuation and pricing using Forecasting Techniques like Monte Carlo Simulation 5 mins

4. Solution Presentation with details on Modeling Approach 5 mins

5. How Bank benifitted from this Valuation Modeling? 2-3 mins

Learning Outcome

1. Clear understanding of Risk Assessment in Banks and why it is important

2. What is Capital Adequacy ratio and how does Central Banks/RBI ensure the same across all major Banks/Financial Institutions across India?

3. Understanding of Risk Models and Statistics behind them

4. Monte Carlo Simulation for Forecasting

Target Audience

Bankers , Data Scientists , MBA Finance , Financial Consultants , Risk Officers , Regulatory Compliance Officers ,Banking professionals who work in Regulatory Reporting / Model Validation, Data Modellers

Prerequisites for Attendees

Participants need to have an understanding of the Banking Process , Regulatory Guidelines and how Banks calculate Risk of portfolios using statistical models


schedule Submitted 3 years ago

  • Badri Narayanan Gopalakrishnan

    Badri Narayanan Gopalakrishnan - Global Supply Chain Analytics

    20 Mins

    Today's world is full of enormously inter-linked countries and businesses. Therefore, business planning decisions should take into account the linkages between different business segments as well as countries. Government policies also affect businesses not only in the home country but also elsewhere. However, typically business analysts who work on forecasts and planning do not account for changes in terms of policies and supply chains in their countries and elsewhere. In this session, we propose a framework that marries data-intensive computational methods employed in economic modeling with business decision making. The framework we propose is well-known in computational economics, called Computable General Equilibrium (CGE) model. It captures equilibrium in all markets in the economy, and tracks the movement from one equilibrium to another, in the presence of a perturbation from outside the system - like a policy shock, technological change or disaster. This framework employs publicly available rich information on linkages between countries and business segments to evaluate the market changes that can arise from major global and local policies as well as technological changes and disasters such as pandemics. This has immense potential to facilitate a scientific approach to handle future uncertainties involved in analytics for business decisions. This can be used for manufacturing sector or any other sector, to adjust their business strategies to external environments. Technically, we will discuss about data concepts in supply chain economics - called Input Output matrix-based models, which may further be built with behavioral equations that may be parametrized using AI/ML methods or conventional econometric techniques. In real time, we will show a classic example of this kind of a model - an open source global supply chain CGE model, named Global Trade Analysis Project (GTAP: www.gtap.org). We will conduct simulations to show how US-China trade war can affect different sectors in India, and how Covid-19 pandemic is impacting several sectors in India, based on our recent research with several international public organizations (ADB, UN, US Chamber of Commerce etc.) We shall provide study materials and links to download the software needed, ahead of the session. We will need atleast 45 minutes or 90 minutes (or even half a day session), to make sure that the audience can learn something actionable from this. These tools have been used by top consulting firms like McKinsey and almost all international organizations and governments, for example, for key decision making. There is also a lot of scope to use standard AI/ML tools herein. The purpose of this session is to both introduce these tools to the audience and also encourage them to collaborate on developing AI/ML solutions for some challenges in these tools, such as lack of accuracy in capturing the behavior of different components of the economy and forecasting the future.