This document discusses credit risk model building in four main steps:
1) Studying historical customer data to identify factors that impact the likelihood of default or charge-off.
2) Identifying the 20 most impacting factors out of hundreds of potential variables.
3) Using logistic regression to quantify the exact impact of each factor and develop a predictive model.
4) Applying the model to new customers to predict their probability of default based on their characteristics and factor weights. Other examples discussed include marketing response modeling and fraud detection modeling.
Credit risks are calculated based on the borrowers’ overall ability to repay. Our objective was to use optimization in order to create a tool that approves or rejects loans to borrowers. We also used optimization to establish how much interest rate/credit will be extended to borrowers who were approved for a loan.
Predicting Credit Card Defaults using Machine Learning AlgorithmsSagar Tupkar
This is a project that I worked on as a Capstone for my Masters in Business Analytics program at the University of Cincinnati. In this project, I have performed an end-to-end data mining exercise including data cleaning, distribution analysis, exploratory data analysis, model building etc. to identify and predict Credit Card defaults using Customer's data on past payments and general profile. In the process for building Machine Learning models, I have fit and compared the performance of multiple models and algorithms like Logistic Regreesion, PCA, Classification tree, AdaBoost Classifier, ANN and LDA.
Default credit cards are an important issue that bring negative consequences to both sides, i.e, banks and customer. If a customer does not pay his obligations, banks loose money, the customer will lose credibility in future payments, collection calls start to be made and in last resort, the case may go into the court. In order to avoid all of that trouble, effective methods that are able to predict the default of credit cards are needed. Therefore, default credit card prediction is an important, challenging and useful task that should be addressed.
This presentation documents how the problem can be addressed, following the pipeline of a typical Patter Recognition application. The main task is to classify a set of samples representing the history of payments and bill statements of a given client plus some background information about the client according to its ability to pay or not (Default) the next monthly payment of its credit card.
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Certain Cases of Customers default on Payments in Taiwan.
From a Risk Management Perspective a Bank/Credit Card Company is more interested in minimizing their losses towards a particular customer.
The information that is more valuable to them is estimating the probability of default rather than classifying a customer as credible/not credible.
Goal: To compute the predictive accuracy of probability of default for a Taiwanese Credit Card Client.
Problem Analysis – Classify Probability of default for next month: 1 as “Default” and 0 as “Not Default”.
Machine Learning Project - Default credit card clients Vatsal N Shah
- The model we built here will use all possible factors to predict data on customers to find who are defaulters and non‐defaulters next month.
- The goal is to find the whether the clients are able to pay their next month credit amount.
- Identify some potential customers for the bank who can settle their credit balance.
- To determine if their customers could make the credit card payments on‐time.
- Default is the failure to pay interest or principal on a loan or credit card payment.
Credit risks are calculated based on the borrowers’ overall ability to repay. Our objective was to use optimization in order to create a tool that approves or rejects loans to borrowers. We also used optimization to establish how much interest rate/credit will be extended to borrowers who were approved for a loan.
Predicting Credit Card Defaults using Machine Learning AlgorithmsSagar Tupkar
This is a project that I worked on as a Capstone for my Masters in Business Analytics program at the University of Cincinnati. In this project, I have performed an end-to-end data mining exercise including data cleaning, distribution analysis, exploratory data analysis, model building etc. to identify and predict Credit Card defaults using Customer's data on past payments and general profile. In the process for building Machine Learning models, I have fit and compared the performance of multiple models and algorithms like Logistic Regreesion, PCA, Classification tree, AdaBoost Classifier, ANN and LDA.
Default credit cards are an important issue that bring negative consequences to both sides, i.e, banks and customer. If a customer does not pay his obligations, banks loose money, the customer will lose credibility in future payments, collection calls start to be made and in last resort, the case may go into the court. In order to avoid all of that trouble, effective methods that are able to predict the default of credit cards are needed. Therefore, default credit card prediction is an important, challenging and useful task that should be addressed.
This presentation documents how the problem can be addressed, following the pipeline of a typical Patter Recognition application. The main task is to classify a set of samples representing the history of payments and bill statements of a given client plus some background information about the client according to its ability to pay or not (Default) the next monthly payment of its credit card.
Loan default prediction with machine language Aayush Kumar
Deafult-Loan-Prediction-Project-Using-Random-Forest-and-Decision-Tree
Deafult Loan Prediction Project Using Random Forest and Decision Tree, In This Project we use loan data from Leanding Club Random Forest Project - Deafult Loan Prediction For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.
Certain Cases of Customers default on Payments in Taiwan.
From a Risk Management Perspective a Bank/Credit Card Company is more interested in minimizing their losses towards a particular customer.
The information that is more valuable to them is estimating the probability of default rather than classifying a customer as credible/not credible.
Goal: To compute the predictive accuracy of probability of default for a Taiwanese Credit Card Client.
Problem Analysis – Classify Probability of default for next month: 1 as “Default” and 0 as “Not Default”.
Machine Learning Project - Default credit card clients Vatsal N Shah
- The model we built here will use all possible factors to predict data on customers to find who are defaulters and non‐defaulters next month.
- The goal is to find the whether the clients are able to pay their next month credit amount.
- Identify some potential customers for the bank who can settle their credit balance.
- To determine if their customers could make the credit card payments on‐time.
- Default is the failure to pay interest or principal on a loan or credit card payment.
Loan Default Prediction with Machine LearningAlibaba Cloud
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/detail.htm?webinarId=50
This webinar is designed to help users understand the end-to-end data science processes of using a propensity model on Alibaba Cloud’s Machine Learning Platform for AI; from defining the business problem, exploratory data analysis, data processing, model training to testing and deployment. You get an end-to-end case study (including a live demo) on how to use Alibaba Cloud products to predict the propensity of loan defaults.
Learn more about Machine Learning Platform for AI:
https://www.alibabacloud.com/product/machine-learning
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
What is Predictive Analytics?
Predictive Analytics is the stream of the advanced analytics which utilizes diverse techniques like data mining, predictive modelling, statistics, machine learning and artificial intelligence to analyse current data and predict future.
To Know more: https://goo.gl/zAcnCR
LOAN DEFAULT PREDICTION – A CASE STUDY
Content Covered in this video:
Business Problem & Benefits
The Risk - LOAN DEFAULT PREDICTION
Data Analysis Process
Data Processing
Predictive Analysis Process
Tools & Technology
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
Measuring and Managing Credit Risk With Machine Learning and Artificial Intel...accenture
In recent years, technological developments have undergone in-depth analysis among banks, but we are still far from attaining mature levels both at the methodological and at the credit granting, monitoring and control process levels. Banks should equip themselves with new and more structured Model Risk frameworks to manage new Machine Learning model validation paradigms. Learn more from Accenture Finance & Risk: https://accntu.re/2qGUUMx
Credit scoring has been used to categorize customers based on various characteristics to evaluate their credit worthiness. Increasingly, machine learning techniques are being deployed for customer segmentation, classification and scoring. In this talk, we will discuss various machine learning techniques that can be used for credit risk applications. Through a case study built in R, we will illustrate the nuances of working with practical data sets which includes categorical and numerical data, different techniques that can be used to evaluate and explore customer profiles, visualizing high dimensional data sets and machine learning techniques for customer segmentation.
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
Overview of Data Analytics in Lending BusinessSanjay Kar
AI/ML use cases
BFSI industry overview
Lending Products
Underwriting Strategy
Customer Lifecycle Management
How to prepare for becoming a banking analyst
Materials to study for statistics
What is fintech?
What is a Credit Bureau?
Books for statistics
Tools for data science
Techniques for data science
This project aims at predicting Defaulters of Credit Card Payment. R programming is used for Exploratory Data Analysis and for Model building R programming and Azure ML is used.
Loan Default Prediction with Machine LearningAlibaba Cloud
See webinar recording of this presentation at: https://resource.alibabacloud.com/webinar/detail.htm?webinarId=50
This webinar is designed to help users understand the end-to-end data science processes of using a propensity model on Alibaba Cloud’s Machine Learning Platform for AI; from defining the business problem, exploratory data analysis, data processing, model training to testing and deployment. You get an end-to-end case study (including a live demo) on how to use Alibaba Cloud products to predict the propensity of loan defaults.
Learn more about Machine Learning Platform for AI:
https://www.alibabacloud.com/product/machine-learning
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
What is Predictive Analytics?
Predictive Analytics is the stream of the advanced analytics which utilizes diverse techniques like data mining, predictive modelling, statistics, machine learning and artificial intelligence to analyse current data and predict future.
To Know more: https://goo.gl/zAcnCR
LOAN DEFAULT PREDICTION – A CASE STUDY
Content Covered in this video:
Business Problem & Benefits
The Risk - LOAN DEFAULT PREDICTION
Data Analysis Process
Data Processing
Predictive Analysis Process
Tools & Technology
Loan Prediction system is a system which provides you a interface for loan approval to the applicants application of loan. Applicants provides the system about their personal information and according to their information system gives his status of availability of loan.
Measuring and Managing Credit Risk With Machine Learning and Artificial Intel...accenture
In recent years, technological developments have undergone in-depth analysis among banks, but we are still far from attaining mature levels both at the methodological and at the credit granting, monitoring and control process levels. Banks should equip themselves with new and more structured Model Risk frameworks to manage new Machine Learning model validation paradigms. Learn more from Accenture Finance & Risk: https://accntu.re/2qGUUMx
Credit scoring has been used to categorize customers based on various characteristics to evaluate their credit worthiness. Increasingly, machine learning techniques are being deployed for customer segmentation, classification and scoring. In this talk, we will discuss various machine learning techniques that can be used for credit risk applications. Through a case study built in R, we will illustrate the nuances of working with practical data sets which includes categorical and numerical data, different techniques that can be used to evaluate and explore customer profiles, visualizing high dimensional data sets and machine learning techniques for customer segmentation.
According to the Nilson report, the global Credit card and debit card fraud resulted in losses amounting to $24.71 billion in 2016 and 72% were bored by the Card issuers. Therefore, the card issue companies are eager to predict the fraud in real time and in advance to reduce their loss and protect their revenue. The goal of the project is to provide fraud analytics for credit card issue companies to predict fraud in real-time and in advance. By building a supervised fraud prediction model, we are aiming to capture the maximum number of real frauds while limiting the occurrence of mis-flagged frauds, in order to achieve a win-win situation both maximize our ROI and achieve customer satisfaction.
Deep Credit Risk Ranking with LSTM with Kyle GroveDatabricks
Find out how Teradata and some of world’s largest financial institutions are innovating credit risk ranking with deep learning techniques and AnalyticOps. With the AnalyticOps framework, these organization have built models with increased accuracy to drive more profitable lending decisions, while being explainable to regulators.
Join us for a live session and learn about:
A machine learning ensemble including LSTM that achieves 90%+ accuracy at predicting delinquency/default, exceeding conventional credit risk methods by more than 20%.
A model management accelerator that is used to build and deploy the models in an integrated cloud platform, based on TensorFlow and Spark, and supports Keras, DeepLearning4J and SparkML models.
An innovative technique for model interpretability that obviates LIME’s need to generate synthetic examples.
Overview of Data Analytics in Lending BusinessSanjay Kar
AI/ML use cases
BFSI industry overview
Lending Products
Underwriting Strategy
Customer Lifecycle Management
How to prepare for becoming a banking analyst
Materials to study for statistics
What is fintech?
What is a Credit Bureau?
Books for statistics
Tools for data science
Techniques for data science
This project aims at predicting Defaulters of Credit Card Payment. R programming is used for Exploratory Data Analysis and for Model building R programming and Azure ML is used.
There are 100,000 applicants for loans. Who is likely to default? How to effectively offer a loan
There are 100,000 consumers who is likely to buy my product? How to effectively market my product?
There are more than 1,000,000,000 transactions in a day. How to identify the fraud transaction?
There are 1,000,000 claims every year. How to identify the fake claims
Presented at Bethesda Data Science Meetup October 2019
Chris Conlan shares his perspective on when and how data science methods ought to be applied in financial services organizations.
Worked on End-to-End Implementation of Machine Learning Project.
Project Name: Loan Status Prediction
• Handling Null Values, Outliers, Unbalanced Dataset
• Data Pre-processing, Restructuring for Balanced Data
• Applying various Machine Learning Classification models
• Analyzing Various Accuracy Parameters
• Tuning and Pickling Models
• Deploying Model on Streamlit
The term “alternative data” is tossed about in the industry, but what types of alternative data can truly be used when lenders want to make a credit decision? How can it be leveraged to help you grow your credit portfolio wisely? What insights can you glean to expand your consumer universe?
Uncover some of the latest trends attached to the non-prime universe and learn the latest around alternative credit data. This deck additionally explores how some of the newest attributes can benefit lenders of all sizes.
To identify the segment of customers, who have a higher tendency to default, if they are offered a Personal Loan
To leverage the existing Two-Wheeler Loan (TW) customer base to cross-sell the Personal Loan product
Learn about how to do a qualitative and quantitative analysis to determine the gap in your market for micro and small business financing. Friedman Associates has developed a unique methodology in this area.
My talk on AI for Human Resource Management at the Faculty Development Programme conducted by Department of Management Studies MVGR College of Engineering
Ever wondered about the full form of Chat GPT?🤔 It stands for Chat Generative Pre-Trained Transformer. For those diving into the world of Transformers, I've been using this PPT during my lectures📚. Thought it might be handy for some of you too! Check it out and let me know what you think!🌟
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
What is boosting
Boosting algorithm
Building models using GBM
Algorithm main Parameters
Finetuning models
Hyper parameters in GBM
Validating GBM models
Neural network Intuition
Neural network and vocabulary
Neural network algorithm
Math behind neural network algorithm
Building the neural networks
Validating the neural network model
Neural network applications
Image recognition using neural networks
Introduction to Analytics
Introduction to SAS
Introduction to Satistics
Introduction to Predictive Modeling
Introduction to Forecasting
Introduction to Bigdata
Step-1 Tableau Introduction
Step-2 Connecting to Data
Step-3 Building basic views
Step-4 Data manipulations and Calculated fields
Step-5 Tableau Dashboards
Step-6 Advanced Data Options
Step-7 Advanced graph Options
List of data sets and data set sources
Sample data sets for machine learning
Data sets for predictive modeling and visualizations
Economic and Social Data sets
Business and Financial datasets
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2. Note
• This presentation is just the lecture notes for the talk on Credit Risk Model building
conducted for MBA students
• The best way to treat this is as a high-level summary; the actual session went more
in depth and contained other information.
• Most of this material was written as informal notes, not intended for publication
• Please send your questions/comments/corrections to
venkat@trenwiseanalytics.com or 21.venkat@gmail.com
• Please check my website for latest version of this document
-Venkat Reddy
3. Contents
• Applications of Statistics in Business
• The Model Building Problem
• Credit Risk Model Building
• Other Applications of Model Building
4. Applications of Statistics in Business
Increasingly, business rely on intelligent tools and techniques to analyze data systematically to
improve decision-making.
Retail sales analytics
Financial services analytics
Telecommunications
Supply Chain analytics
Transportation analytics
Risk & Credit analytics
Talent analytics
Marketing analytics
Behavioral analytics
Collections analytics
Fraud analytics
Pricing analytics
5. The Problem
Who will run away with my money?
• Citi Bank : Present in more than 90 countries.
• More than 100,000 customers apply for credit
cards/loans every month
• All of them have different characteristics
• Out of 10o,000 customers, who all have the higher
probability of default/ Charge off?
• Basically, who will run away with my money?
• We need to predict the probability of “Running away”
• Who all have ‘Gupta Bank’ credit card applications in
this room?
6. Bank builds a model that gives a score to each
customer
Applicants
“Developing set of equations or mathematical formulation to forecast future
behaviors based on current or historical data.”
7. Predictive Modeling
Lets try to understand predictive modeling
Predictive Modeling – Fitting a model to the data to predict the future.
• Predicting the future –and it is so easy some times
• Who is going to score more runs in IPL-2013?
• That’s it you predicted the future..
• BTW how did you predict?
• Predicting the future based on historical data is
nothing but Predictive modeling
8. Predictive Modeling
Lets try to understand predictive modeling
Predictive Modeling – Fitting a model to the data to predict the future.
• Who is going to score more runs in IPL 2013?
• Predicting the future …well it is not that easy …
10. The Historical Data
Win vs. Loss record in past 2 years
• Long legs: 75% (Horses with long legs won 75% of the times)
• Breed A: 55%, Breed B: 15 % Others : 30%
• T/L (Tummy to length) ratio <1/2 :75 %
• Gender: Male -68%
• Head size: Small 10%, Medium 15% Large 75%
• Country: Africa -65%
11. Given the historical data
Which one of these two horses would you bet on?
Kalyan Chethak
Length of legs 150 cm 110 cm
Breed A F
T/L ratio 0.3 0.6
Gender Male Female
Head size Large Small
Country India India
12. Given the historical data
Which one would you bet on….now?
Kalyan Chethak
Length of legs 110 cm 150 cm
Breed C A
T/L ratio 0.45 0.60
Gender Male Female
Head size Small Large
Country Africa India
13. Given the historical data
What about best one in this lot?
Horse-1 Horse-2 Horse-3 Horse-4 Horse-5 Horse-6 Horse-7 Horse-8 Horse-9 Horse-10
Length of
109 114 134 130 149 120 104 117 115 135
legs
Breed C A B A F K L B C A
T/L ratio 0.1 0.8 0.5 1.0 0.3 0.3 0.3 0.6 0.7 0.9
Gender Male Female Male Female Male Female Male Female Male Female
Head size L S M M L L S M L M
Country Africa India Aus NZ Africa Africa India India Aus Africa
14. Citi has a similar problem?
Who is going to run away with my money?
• Given Historical of the customers we want to predict the probability of bad
• We have the data of each customer on
• Customer previous loans, customer previous payments, length of account credit
history, other credit cards and loans, job type, income band etc.,
• We want to predict the probability of default
15. Credit Risk Model Building
Four main steps
1. Study historical
data
3. Find the exact
• What are the 2. Identify the most 4. Use these
causes(Customer impact of each
impacting factors coefficients for future
Characteristics) factor(Quantify it)
• What are the
effects(Charge off)
16. The Historical Data of Customers
Attribute Value
• Contains all the information about customers
SSN 111259005
Age 27
• Contains information across more than 500
variables Number of dependents 2
• Portion of data is present in the application form Number of current loans 1
• Portion of the data is available with bank
Number of credit cards 1
Number Installments 30days late in 4
• Lot of data is maintained in bureau last 2 years
• Social Security number –in US Average utilization % in last 2 years 30%
• PAN Number in India Time since accounts opened 60 months
Number of previous applications for 2
credit card
Bankrupt NO
18. Model Building
logistic regression model to predict the probability of default
• Probability of bad = w1(Var1)+w2(Var2) +w3(Var1)……+w20(var20)
• Logistic regression gives us those weights
• Predicting the probability
• Probability of bad=0.13(number of cards)+ 0.21 (utilization)+…….+0.06(number of loan
applications)
• That’s it ….we are done
19. Credit Risk Model Building
Real-time Example
Attributes used on the model
1. MonthlyIncome
2. Number of loans
3. Number of times 30days late in last 2 years
4. Utilization in last 2 years
5. Age
6. DebtRatio – Monthly Debt / Monthly Income
20. ‘Gupta Bank’ Credit Cards Approval
• Lets use above model for gupta bank
• Who are the applicants here?
• Lets get the bureau information for the applicants
21. Actual model Building Steps
Objective & Customer score Observation
Portfolio vs. account Exclusions point and/or
identification score window
Performance Validation plan
Bad definition Segmentation
window and samples
Variable Scoring and Score
Model Building
Selection Validation Implementation
22. Marketing Example
Predicting the response probability to a marketing Campaign
• Selling Mobile phones – Marketing campaign
• Who should we target?
• Consider historical data of mobile phone buyers
• See their characteristics
• Find top impacting characteristics
• Find weight of each characteristic
• Score new population
• Decide on the cut off
• Try to sell people who score more than cut off
23. Other Applications of Model Building
• Fraud transactions scorecard – Fraud identification based on attributes like
transaction amount, place, time, frequency of transactions etc.,
• Attrition modeling – Predicting employee attrition based on their characteristics