Business Analytics and Credit Scoring

Size: px
Start display at page:

Download "Business Analytics and Credit Scoring"

Transcription

1 Study Unit 5 Business Analytics and Credit Scoring ANL 309 Business Analytics Applications

2 Introduction Process of credit scoring The role of business analytics in credit scoring Methods of logistic regression and decision trees

3 Constructing a Credit Scoring Model The construction of a credit or behavioural scoring model may be broadly broken down into the following phases: Defining Risky Customers Data Gathering and Analysis Scorecard Generation Implementation and Credit Risk Strategy

4 Defining Risky Customers First, define clearly the customers who the institution would classify as risky customers. Depends on the overall risk that the institution is willing to expose itself to, coupled with its profitability expectations. This is the basis on which the entire scoring framework is built upon.

5 Data Gathering and Analysis Next, identify all the dimensions which have an effect on the customer s propensity to default. Gather all the available information related to past credit behaviour of the customers. Perform the necessary data mining analysis to determine the significant relationships between demographic, behavioural dimensions, and the customer s propensity to default.

6 Scorecard Generation Scorecards can be generated using various techniques in data mining, such as logistic regression, which is a parametric statistical technique, or neutral network, which is a nonti technique. parametric A scorecard is a linear combination of the various attributes with appropriate weights assigned to each of them.

7 Implementation and Credit Risk Strategy Once every customer in the system is assigned a credit score, banks or lending institutions will re-formulate credit policies and operational strategies based on the portfolio. For example, customers with higher credit scores will enjoy better rates, tenures and faster approvals compared to the others. The institution may also decide to deny credit to customers who have very low credit scores.

8 Credit Scoring and Business Analytics There are a number of business analytical methods that can be applied in credit scoring. Two popular methods are: logistic regression decision trees

9 Model Variability Validity monitoring i is conducted d to ensure that t the model differentiates or slopes behaviour that is consistent with the business needs and expectations. When the performance or slope has degraded significantly, it indicates that the business needs are not being served and corrective measures must be taken. Validity monitoring should be viewed as the final defense mechanism because it identifies model failures after they have occurred.

10 Model Stability New models are assessed for stability that begins three months after their first use in production. For existing models, assessment occurs on a quarterly basis. Population stability will be assessed via the Population Stability Index (PSI) and a score distribution report. The statistic will be calculated by comparing a benchmark score distribution with the most recent score distribution.

11 Population Stability The Population Stability Index (PSI) calculations are performed monthly on the Small Business Card population used to the score the SL02 and NA01 models. The PSI value indicates if the population is stable or if there are significant shifts in the population. As such, model breakdown or data inconsistencies can be easily detected.

12 Logistic Regression Logistic regression is similar to linear regression, except that the dependent variable is not continuous. The dependent variable is discrete/ categorical, e.g. 1=respond to an offer, 0=did not respond to an offer; or 1=default on loan, 0=did not default on loan.

13 Logistic Regression

14 Logistic Regression: Assumptions The true conditional probabilities are a logistic function of the independent variables. No omission of important variables. No extraneous variables are included. No measurement error for the independent variables. Independence of observations. o s The independent variables are not linear combinations of each other.

15 Decision Trees Very popular in business analytics applications mainly because it produces visual model and generate rules that can be easily interpreted. Examine all possible questions which can distinguish the data into segments which are nearly homogeneous in characteristics.

16 Types of Decision Trees There are many types of decision tree approaches: C&RT ID3 C4.5/C5 CHAID. Their main difference is how they partition the data.

17 Decision Trees: Stopping Rule A decision tree algorithm will stop growing the tree when one of the following criteria is satisfied: Segment contains only one record. All records in the segment have identical characteristics. Improvement is not substantial to warrant growing the tree further.

18 Over-fitting and Cross-validation Once the tree has grown to a certain size, depending on the stopping rule, it is also important to check the tree for over-fitting of the data. Cross-validation and test set validation may be applied.

Credit Risk Models. August 24 26, 2010

Credit Risk Models. August 24 26, 2010 Credit Risk Models August 24 26, 2010 AGENDA 1 st Case Study : Credit Rating Model Borrowers and Factoring (Accounts Receivable Financing) pages 3 10 2 nd Case Study : Credit Scoring Model Automobile Leasing

More information

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d. EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

More information

Customer and Business Analytic

Customer and Business Analytic Customer and Business Analytic Applied Data Mining for Business Decision Making Using R Daniel S. Putler Robert E. Krider CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is an imprint

More information

The Predictive Data Mining Revolution in Scorecards:

The Predictive Data Mining Revolution in Scorecards: January 13, 2013 StatSoft White Paper The Predictive Data Mining Revolution in Scorecards: Accurate Risk Scoring via Ensemble Models Summary Predictive modeling methods, based on machine learning algorithms

More information

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4. Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics

More information

STATISTICA. Financial Institutions. Case Study: Credit Scoring. and

STATISTICA. Financial Institutions. Case Study: Credit Scoring. and Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHAT

More information

Revenue s Business Context

Revenue s Business Context Analytics and Risk Examples from Research & Analytics Branch Duncan Cleary dcleary@revenue.ie http://www.linkedin.com/in/duncancleary Research & Analytics Branch DATA - INFORMATION - KNOWLEDGE 1 Revenue

More information

A Basic Guide to Modeling Techniques for All Direct Marketing Challenges

A Basic Guide to Modeling Techniques for All Direct Marketing Challenges A Basic Guide to Modeling Techniques for All Direct Marketing Challenges Allison Cornia Database Marketing Manager Microsoft Corporation C. Olivia Rud Executive Vice President Data Square, LLC Overview

More information

Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America

Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America Application of SAS! Enterprise Miner in Credit Risk Analytics Presented by Minakshi Srivastava, VP, Bank of America 1 Table of Contents Credit Risk Analytics Overview Journey from DATA to DECISIONS Exploratory

More information

Some Statistical Applications In The Financial Services Industry

Some Statistical Applications In The Financial Services Industry Some Statistical Applications In The Financial Services Industry Wenqing Lu May 30, 2008 1 Introduction Examples of consumer financial services credit card services mortgage loan services auto finance

More information

WHITEPAPER. How to Credit Score with Predictive Analytics

WHITEPAPER. How to Credit Score with Predictive Analytics WHITEPAPER How to Credit Score with Predictive Analytics Managing Credit Risk Credit scoring and automated rule-based decisioning are the most important tools used by financial services and credit lending

More information

Statistics in Retail Finance. Chapter 6: Behavioural models

Statistics in Retail Finance. Chapter 6: Behavioural models Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural

More information

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.

More information

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification

More information

TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science

TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science TNS EX A MINE BehaviourForecast Predictive Analytics for CRM 1 TNS BehaviourForecast Why is BehaviourForecast relevant for you? The concept of analytical Relationship Management (acrm) becomes more and

More information

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION ISSN 9 X INFORMATION TECHNOLOGY AND CONTROL, 00, Vol., No.A ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION Danuta Zakrzewska Institute of Computer Science, Technical

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

Data Mining Methods: Applications for Institutional Research

Data Mining Methods: Applications for Institutional Research Data Mining Methods: Applications for Institutional Research Nora Galambos, PhD Office of Institutional Research, Planning & Effectiveness Stony Brook University NEAIR Annual Conference Philadelphia 2014

More information

Machine Learning Logistic Regression

Machine Learning Logistic Regression Machine Learning Logistic Regression Jeff Howbert Introduction to Machine Learning Winter 2012 1 Logistic regression Name is somewhat misleading. Really a technique for classification, not regression.

More information

The future of credit card underwriting. Understanding the new normal

The future of credit card underwriting. Understanding the new normal The future of credit card underwriting Understanding the new normal The card lending community is facing a new normal a world of increasingly tighter regulation, restrictive lending criteria and continued

More information

Addressing Analytics Challenges in the Insurance Industry. Noe Tuason California State Automobile Association

Addressing Analytics Challenges in the Insurance Industry. Noe Tuason California State Automobile Association Addressing Analytics Challenges in the Insurance Industry Noe Tuason California State Automobile Association Overview Two Challenges: 1. Identifying High/Medium Profit who are High/Low Risk of Flight Prospects

More information

MERGING BUSINESS KPIs WITH PREDICTIVE MODEL KPIs FOR BINARY CLASSIFICATION MODEL SELECTION

MERGING BUSINESS KPIs WITH PREDICTIVE MODEL KPIs FOR BINARY CLASSIFICATION MODEL SELECTION MERGING BUSINESS KPIs WITH PREDICTIVE MODEL KPIs FOR BINARY CLASSIFICATION MODEL SELECTION Matthew A. Lanham & Ralph D. Badinelli Virginia Polytechnic Institute and State University Department of Business

More information

Prediction of Stock Performance Using Analytical Techniques

Prediction of Stock Performance Using Analytical Techniques 136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Statistics in Retail Finance. Chapter 2: Statistical models of default

Statistics in Retail Finance. Chapter 2: Statistical models of default Statistics in Retail Finance 1 Overview > We consider how to build statistical models of default, or delinquency, and how such models are traditionally used for credit application scoring and decision

More information

Customer Life Time Value

Customer Life Time Value Customer Life Time Value Tomer Kalimi, Jacob Zahavi and Ronen Meiri Contents Introduction... 2 So what is the LTV?... 2 LTV in the Gaming Industry... 3 The Modeling Process... 4 Data Modeling... 5 The

More information

Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90

Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90 FREE echapter C H A P T E R1 Big Data and Analytics Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90 percent of the data in the

More information

Predictive modelling around the world 28.11.13

Predictive modelling around the world 28.11.13 Predictive modelling around the world 28.11.13 Agenda Why this presentation is really interesting Introduction to predictive modelling Case studies Conclusions Why this presentation is really interesting

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

An Introduction to Advanced Analytics and Data Mining

An Introduction to Advanced Analytics and Data Mining An Introduction to Advanced Analytics and Data Mining Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda What are Advanced Analytics and Data Mining? The toolkit

More information

Analytical CRM at Swisscom Fixnet

Analytical CRM at Swisscom Fixnet The data warehouse concept, model development with Enterprise Miner and implementation Dr. Miltiadis Sarakinos Swisscom Fixnet AG, Switzerland Analytical CRM: analysing customers and understanding their

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century

An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century An Overview of Data Mining: Predictive Modeling for IR in the 21 st Century Nora Galambos, PhD Senior Data Scientist Office of Institutional Research, Planning & Effectiveness Stony Brook University AIRPO

More information

Best Segmentation Practices and Targeting Procedures that Provide the most Client-Actionable Strategy

Best Segmentation Practices and Targeting Procedures that Provide the most Client-Actionable Strategy Best Segmentation Practices and Targeting Procedures that Provide the most Client-Actionable Strategy Frank Wyman, Ph.D. Director of Advanced Analytics M/A/R/C Research Segmentation Defined Segmentation:

More information

Cross-Tab Weighting for Retail and Small-Business Scorecards in Developing Markets

Cross-Tab Weighting for Retail and Small-Business Scorecards in Developing Markets Cross-Tab Weighting for Retail and Small-Business Scorecards in Developing Markets Dean Caire (DAI Europe) and Mark Schreiner (Microfinance Risk Management L.L.C.) August 24, 2011 Abstract This paper presents

More information

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within

More information

Classification and Regression Trees

Classification and Regression Trees Classification and Regression Trees Bob Stine Dept of Statistics, School University of Pennsylvania Trees Familiar metaphor Biology Decision tree Medical diagnosis Org chart Properties Recursive, partitioning

More information

Training program on Big Data Analytics

Training program on Big Data Analytics Training program on Big Data Analytics Finesse / StatLabs, Bangalore; are leading organization providing Big Data Analytics Training and Services that helps organizations anticipate job/ business opportunities.

More information

Use of Data Mining in Banking

Use of Data Mining in Banking Use of Data Mining in Banking Kazi Imran Moin*, Dr. Qazi Baseer Ahmed** *(Department of Computer Science, College of Computer Science & Information Technology, Latur, (M.S), India ** (Department of Commerce

More information

Master of Science in Marketing Analytics (MSMA)

Master of Science in Marketing Analytics (MSMA) Master of Science in Marketing Analytics (MSMA) COURSE DESCRIPTION The Master of Science in Marketing Analytics program teaches students how to become more engaged with consumers, how to design and deliver

More information

not possible or was possible at a high cost for collecting the data.

not possible or was possible at a high cost for collecting the data. Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day

More information

CoolaData Predictive Analytics

CoolaData Predictive Analytics CoolaData Predictive Analytics 9 3 6 About CoolaData CoolaData empowers online companies to become proactive and predictive without having to develop, store, manage or monitor data themselves. It is an

More information

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved. Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA

More information

Data mining and statistical models in marketing campaigns of BT Retail

Data mining and statistical models in marketing campaigns of BT Retail Data mining and statistical models in marketing campaigns of BT Retail Francesco Vivarelli and Martyn Johnson Database Exploitation, Segmentation and Targeting group BT Retail Pp501 Holborn centre 120

More information

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry

Potential Value of Data Mining for Customer Relationship Marketing in the Banking Industry Advances in Natural and Applied Sciences, 3(1): 73-78, 2009 ISSN 1995-0772 2009, American Eurasian Network for Scientific Information This is a refereed journal and all articles are professionally screened

More information

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA

Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of

More information

Comparison of Data Mining Techniques used for Financial Data Analysis

Comparison of Data Mining Techniques used for Financial Data Analysis Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract

More information

Data Mining in CRM & Direct Marketing. Jun Du The University of Western Ontario jdu43@uwo.ca

Data Mining in CRM & Direct Marketing. Jun Du The University of Western Ontario jdu43@uwo.ca Data Mining in CRM & Direct Marketing Jun Du The University of Western Ontario jdu43@uwo.ca Outline Why CRM & Marketing Goals in CRM & Marketing Models and Methodologies Case Study: Response Model Case

More information

COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments

COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for

More information

Credit Scorecards for SME Finance The Process of Improving Risk Measurement and Management

Credit Scorecards for SME Finance The Process of Improving Risk Measurement and Management Credit Scorecards for SME Finance The Process of Improving Risk Measurement and Management April 2009 By Dean Caire, CFA Most of the literature on credit scoring discusses the various modelling techniques

More information

Scoring Systems. Chapter 18

Scoring Systems. Chapter 18 Scoring Systems Chapter 18 Example: Credit Card Application 2 Example: Credit Card Application 3 Example: Credit Card Application 4 Introduction Description Mathematical methods (scoring systems) Purposes

More information

Managing Consumer Credit Risk *

Managing Consumer Credit Risk * Managing Consumer Credit Risk * Peter Burns Anne Stanley September 2001 Summary: On July 31, 2001, the Payment Cards Center of the Federal Reserve Bank of Philadelphia hosted a workshop that examined current

More information

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis ElegantJ BI White Paper The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis Integrated Business Intelligence and Reporting for Performance Management, Operational

More information

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

More information

Behavior Model to Capture Bank Charge-off Risk for Next Periods Working Paper

Behavior Model to Capture Bank Charge-off Risk for Next Periods Working Paper 1 Behavior Model to Capture Bank Charge-off Risk for Next Periods Working Paper Spring 2007 Juan R. Castro * School of Business LeTourneau University 2100 Mobberly Ave. Longview, Texas 75607 Keywords:

More information

Segmentation for Credit Based Delinquency Models White Paper

Segmentation for Credit Based Delinquency Models White Paper Segmentation for Credit Based Delinquency Models White Paper May 2006 Overview The objective of segmentation is to define a set of sub-populations that, when modeled individually and then combined, rank

More information

Classification and Regression Trees as a Part of Data Mining in Six Sigma Methodology

Classification and Regression Trees as a Part of Data Mining in Six Sigma Methodology , October 20-22, 2010, San Francisco, USA Classification and Regression Trees as a Part of Data Mining in Six Sigma Methodology Andrej Trnka, Member, IAENG Abstract The paper deals with implementation

More information

D&B integrate the data into our database through our patented Entity Matching, which produces a single accurate picture of each business.

D&B integrate the data into our database through our patented Entity Matching, which produces a single accurate picture of each business. D&B Rating Guide D&B Risk Assessment Explained - Decide with confidence Understanding and minimising risk is fundamental to your organisation. Staying informed of any changes is the only way to grow your

More information

Banking Analytics Training Program

Banking Analytics Training Program Training (BAT) is a set of courses and workshops developed by Cognitro Analytics team designed to assist banks in making smarter lending, marketing and credit decisions. Analyze Data, Discover Information,

More information

Validating a Credit Score Model in Conjunction with Additional Underwriting Criteria September 2012

Validating a Credit Score Model in Conjunction with Additional Underwriting Criteria September 2012 Validating a Credit Score Model in Conjunction with Additional Underwriting Criteria September 2012 INTRODUCTION Model validation is a critical activity to verify that credit scorecards are working as

More information

UNDERSTANDING THE EFFECTIVENESS OF BANK DIRECT MARKETING Tarun Gupta, Tong Xia and Diana Lee

UNDERSTANDING THE EFFECTIVENESS OF BANK DIRECT MARKETING Tarun Gupta, Tong Xia and Diana Lee UNDERSTANDING THE EFFECTIVENESS OF BANK DIRECT MARKETING Tarun Gupta, Tong Xia and Diana Lee 1. Introduction There are two main approaches for companies to promote their products / services: through mass

More information

The Data Mining Process

The Data Mining Process Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

More information

USING LOGIT MODEL TO PREDICT CREDIT SCORE

USING LOGIT MODEL TO PREDICT CREDIT SCORE USING LOGIT MODEL TO PREDICT CREDIT SCORE Taiwo Amoo, Associate Professor of Business Statistics and Operation Management, Brooklyn College, City University of New York, (718) 951-5219, Tamoo@brooklyn.cuny.edu

More information

Neural Networks & Boosting

Neural Networks & Boosting Neural Networks & Boosting Bob Stine Dept of Statistics, School University of Pennsylvania Questions How is logistic regression different from OLS? Logistic mean function for probabilities Larger weight

More information

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

More information

Weight of Evidence Module

Weight of Evidence Module Formula Guide The purpose of the Weight of Evidence (WoE) module is to provide flexible tools to recode the values in continuous and categorical predictor variables into discrete categories automatically,

More information

Easily Identify Your Best Customers

Easily Identify Your Best Customers IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do

More information

Experian s UK Credit Bureau Scores. Version 1.6

Experian s UK Credit Bureau Scores. Version 1.6 Experian s UK Credit Bureau Scores Version 1.6 January 2014 About Experian Decision Analytics Experian Decision Analytics enterprise-wide solutions combine data intelligence, predictive analytics, decisionenabling

More information

THE USE OF PREDICTIVE MODELLING TO BOOST DEBT COLLECTION EFFICIENCY

THE USE OF PREDICTIVE MODELLING TO BOOST DEBT COLLECTION EFFICIENCY CREDIT SCORING AND CREDIT CONTROL XIII EDINBURGH 28-30 AUGUST 2013 THE USE OF PREDICTIVE MODELLING TO BOOST DEBT COLLECTION EFFICIENCY MARCIN NADOLNY SAS INSTITUTE POLAND Many executives fear that the

More information

White Paper. Data Mining for Business

White Paper. Data Mining for Business White Paper Data Mining for Business January 2010 Contents 1. INTRODUCTION... 3 2. WHY IS DATA MINING IMPORTANT?... 3 FUNDAMENTALS... 3 Example 1...3 Example 2...3 3. OPERATIONAL CONSIDERATIONS... 4 ORGANISATIONAL

More information

Reevaluating Policy and Claims Analytics: a Case of Non-Fleet Customers In Automobile Insurance Industry

Reevaluating Policy and Claims Analytics: a Case of Non-Fleet Customers In Automobile Insurance Industry Paper 1808-2014 Reevaluating Policy and Claims Analytics: a Case of Non-Fleet Customers In Automobile Insurance Industry Kittipong Trongsawad and Jongsawas Chongwatpol NIDA Business School, National Institute

More information

This paper is directed to small business owners desiring to use. analytical algorithms in order to improve sales, reduce attrition rates raise

This paper is directed to small business owners desiring to use. analytical algorithms in order to improve sales, reduce attrition rates raise Patrick Duff Analytical Algorithm Whitepaper Introduction This paper is directed to small business owners desiring to use analytical algorithms in order to improve sales, reduce attrition rates raise profits

More information

Turning Data into Action: How Credit Card Programs Can Benefit from the World of Big Data

Turning Data into Action: How Credit Card Programs Can Benefit from the World of Big Data Turning Data into Action: How Credit Card Programs Can Benefit from the World of Big Data A Capital Services White Paper by Dr. Alfred Furth Introduction Scientists tell us that enough sunlight falls on

More information

Utilizing Experian next generation decision management software to bring customer management to the next level of client experience and value creation

Utilizing Experian next generation decision management software to bring customer management to the next level of client experience and value creation Utilizing Experian next generation decision management software to bring customer management to the next level of client experience and value creation Susan Duffy Scotiabank Robert Stone Experian Christopher

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

More information

DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKERS. Rajanish Dass Indian Institute of Management Ahmedabad rajanish@iimahd.ernet.in.

DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKERS. Rajanish Dass Indian Institute of Management Ahmedabad rajanish@iimahd.ernet.in. DATA MINING IN BANKING AND FINANCE: A NOTE FOR BANKERS Rajanish Dass Indian Institute of Management Ahmedabad rajanish@iimahd.ernet.in Abstract Currently, huge electronic data repositories are being maintained

More information

Next-Generation Predictive Analytics. Research Report Executive Summary. Using Forward-Looking Insights to Gain Competitive Advantage.

Next-Generation Predictive Analytics. Research Report Executive Summary. Using Forward-Looking Insights to Gain Competitive Advantage. Next-Generation Predictive Analytics Using Forward-Looking Insights to Gain Competitive Advantage Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without

More information

Predicting Customer Default Times using Survival Analysis Methods in SAS

Predicting Customer Default Times using Survival Analysis Methods in SAS Predicting Customer Default Times using Survival Analysis Methods in SAS Bart Baesens Bart.Baesens@econ.kuleuven.ac.be Overview The credit scoring survival analysis problem Statistical methods for Survival

More information

Chapter 3: Scorecard Development Process, Stage 1: Preliminaries and Planning.

Chapter 3: Scorecard Development Process, Stage 1: Preliminaries and Planning. Contents Acknowledgments. Chapter 1: Introduction. Scorecards: General Overview. Chapter 2: Scorecard Development: The People and the Process. Scorecard Development Roles. Intelligent Scorecard Development.

More information

Customer Classification And Prediction Based On Data Mining Technique

Customer Classification And Prediction Based On Data Mining Technique Customer Classification And Prediction Based On Data Mining Technique Ms. Neethu Baby 1, Mrs. Priyanka L.T 2 1 M.E CSE, Sri Shakthi Institute of Engineering and Technology, Coimbatore 2 Assistant Professor

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Variable Selection in the Credit Card Industry Moez Hababou, Alec Y. Cheng, and Ray Falk, Royal Bank of Scotland, Bridgeport, CT

Variable Selection in the Credit Card Industry Moez Hababou, Alec Y. Cheng, and Ray Falk, Royal Bank of Scotland, Bridgeport, CT Variable Selection in the Credit Card Industry Moez Hababou, Alec Y. Cheng, and Ray Falk, Royal ank of Scotland, ridgeport, CT ASTRACT The credit card industry is particular in its need for a wide variety

More information

KINGS COLLEGE OF ENGINEERING

KINGS COLLEGE OF ENGINEERING KINGS COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING ACADEMIC YEAR 2011-2012 / ODD SEMESTER SUBJECT CODE\NAME: CS1011-DATA WAREHOUSE AND DATA MINING YEAR / SEM: IV / VII UNIT I BASICS

More information

Data Mining: Overview. What is Data Mining?

Data Mining: Overview. What is Data Mining? Data Mining: Overview What is Data Mining? Recently * coined term for confluence of ideas from statistics and computer science (machine learning and database methods) applied to large databases in science,

More information

The primary goal of this thesis was to understand how the spatial dependence of

The primary goal of this thesis was to understand how the spatial dependence of 5 General discussion 5.1 Introduction The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial

More information

Innovative Analytics for Traditional, Social, and Text Data. Dr. Gerald Fahner, Senior Director Analytic Science, FICO

Innovative Analytics for Traditional, Social, and Text Data. Dr. Gerald Fahner, Senior Director Analytic Science, FICO Innovative Analytics for Traditional, Social, and Text Data Dr. Gerald Fahner, Senior Director Analytic Science, FICO Hot Trends in Predictive Analytics Big Data the Fuel is high-volume, high-velocity

More information

Data Mining Part 5. Prediction

Data Mining Part 5. Prediction Data Mining Part 5. Prediction 5.7 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Introduction Linear Regression Other Regression Models References Introduction Introduction Numerical prediction is

More information

Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right

More information

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1

Data Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1 Data Mining 1 Introduction 2 Data Mining methods Alfred Holl Data Mining 1 1 Introduction 1.1 Motivation 1.2 Goals and problems 1.3 Definitions 1.4 Roots 1.5 Data Mining process 1.6 Epistemological constraints

More information

How to Optimize Your Data Mining Environment

How to Optimize Your Data Mining Environment WHITEPAPER How to Optimize Your Data Mining Environment For Better Business Intelligence Data mining is the process of applying business intelligence software tools to business data in order to create

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Car Insurance. Prvák, Tomi, Havri

Car Insurance. Prvák, Tomi, Havri Car Insurance Prvák, Tomi, Havri Sumo report - expectations Sumo report - reality Bc. Jan Tomášek Deeper look into data set Column approach Reminder What the hell is this competition about??? Attributes

More information

IBM SPSS Direct Marketing 23

IBM SPSS Direct Marketing 23 IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release

More information

Model-Based Recursive Partitioning for Detecting Interaction Effects in Subgroups

Model-Based Recursive Partitioning for Detecting Interaction Effects in Subgroups Model-Based Recursive Partitioning for Detecting Interaction Effects in Subgroups Achim Zeileis, Torsten Hothorn, Kurt Hornik http://eeecon.uibk.ac.at/~zeileis/ Overview Motivation: Trees, leaves, and

More information

Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables

Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables Paper 10961-2016 Improving performance of Memory Based Reasoning model using Weight of Evidence coded categorical variables Vinoth Kumar Raja, Vignesh Dhanabal and Dr. Goutam Chakraborty, Oklahoma State

More information

New D&B Failure Score and Recommended Credit Limit Models for UK and Ireland

New D&B Failure Score and Recommended Credit Limit Models for UK and Ireland New D&B Failure Score and Recommended Credit Limit Models for UK and Ireland Please click the links below Customer Frequently Asked Questions What is changing? When will the changes happen? Why are we

More information

WHITEPAPER. Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk

WHITEPAPER. Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk WHITEPAPER Creating and Deploying Predictive Strategies that Drive Customer Value in Marketing, Sales and Risk Overview Angoss is helping its clients achieve significant revenue growth and measurable return

More information

A new paradigm in P&C Industry Pricing

A new paradigm in P&C Industry Pricing The New Paradigm of Property & Casualty Insurance Pricing: Multivariate analysis and Predictive Modeling The ability to effectively price personal lines insurance policies to accurately match rate with

More information

1 Choosing the right data mining techniques for the job (8 minutes,

1 Choosing the right data mining techniques for the job (8 minutes, CS490D Spring 2004 Final Solutions, May 3, 2004 Prof. Chris Clifton Time will be tight. If you spend more than the recommended time on any question, go on to the next one. If you can t answer it in the

More information