Fidor OS Modul: Community Scoring - Alexander Thamm - Pirates of Banking, Fidor Developer Day 2014,...

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Bild durch Klicken auf Symbol hinzufügen Community Score & Credit Scoring FIDOR TecS / ALEXANDER THAMM GMBH

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Speaker: Alexander Thamm, Alexander Thamm GmbH

Transcript of Fidor OS Modul: Community Scoring - Alexander Thamm - Pirates of Banking, Fidor Developer Day 2014,...

Page 1: Fidor OS Modul: Community Scoring - Alexander Thamm - Pirates of Banking, Fidor Developer Day 2014, Munich

Bild durch Klicken auf Symbol hinzufügen

Community Score & Credit Scoring

FIDOR TecS / ALEXANDER THAMM GMBH

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AGENDA

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CONTENT PAGE

A. COMPANY PROFILE 3B. AT-ANALYTICS APPROACH 4C. CHALLENGES AT HAND

5D. OUR PRODUCTS

A. COMMUNITY SCORE 6-9B. CREDIT SCORE 10-13

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COMPANY OVERVIEW

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FACTS, FIGURES, CUSTOMERS AND PARTNERS OF THE ALEXANDER THAMM GMBH AT A GLANCE

FACTS AND FIGURES

MISSION We enable our customers to gain competitive advantage with

Big Data and Predictive Analytics.

CORPORATE PHILOSOPHYOUR PARTNERS

OUR CUSTOMERS

Location: Munich, Germany Team: 20+ interdisciplinary Data Scientists and

Analytic Experts from top universities Sector: Automotive, Energy, Finance, Consumer

Goods Performance: Data Science consulting, customized Predictive

Analytics and Reporting Tools

EXCELLENCE. We define the state of the art for analytics. PASSION. We love what we do. COMMITMENT. We dedicate ourselves to excellent results. SUSTAINABILITY. We implement lasting solutions.

„Most valuable Data Science Boutique“ - customer testimonial

Leading companies in the Automotive, Energy, Finance, Consumer Goods Industry.

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AT-ANALYTICS APPROACH

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THE KEY TO A SUCCESSFUL AND SUSTAINABLE IMPLEMENTATION OF BIG DATA ANALYTICS IS THE COMBINATION OF FIVE ELEMENTS

BUSINESS PROCESSES Holistic analysis of fields with potential for optimization within the

company.

DATA INTELLIGENCE Identification, preparation and consolidation of the necessary data as

well as enrichment of additional data.

PREDICTIVE ANALYTICS Generation of valid forecast models through statistical modeling

techniques and data mining methods. Derivation of relevant business decisions.

INSIGHTS VISUALIZATION Creation of interactive applications in easy to understand stakeholder-

specific design or/ and visualization of analysis results.

ANALYTICS SERVICES MANAGEMENT Preparation and transfer of the application into operation, daily

maintenance as well as documentation of process, data and user. Guarantee of reliable operations including data integrity.

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CHALLENGES AT HAND

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COMMUNITY SCORE

CREDIT SCORE

Quantification of user activity and quality of postings within the Fidor community

Creation of user incentives to increase quality and quantity of user activity

Minimization of credit defaults, without negative effects on the number of credits granted

Allocation of individual credit products according to the individual customer‘s risk in a standardized, automated credit approval process

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COMMUNITY SCORE

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VISUALIZATION OF CORRELATION MATRIX

Visualization of pairwise Correlations– Red: negative correlation– Orange: no observable correlation– Yellow/white: positive correlation– Color intensity = correlation intensity

Visual Blocks are consolidated to factors

Subjective approach requires principal component analysis (PCA)

Heatmap

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COMMUNITY SCORE

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VISUALIZATION OF COMMUNITY SCORE

Division of scores into 10 classes Division of scores related to the

whole community Weighting of total scores based

on significance of individual components

Community Score

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COMMUNITY SCORE

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DEMO

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CREDIT SCORE

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PROCESSING A CREDIT APPLICATION

Credit Application

SQL Data Engineering

SQL Scoring

Defaultprobability

20,5%

Parameter

Credit approval

1. A costumer applies for a line of credit.2. Using SQL commands, costumer data is generated at a

costumer account level. 3. The costumer data are put through a scoring model,

the parameters of which are generated using R-code4. The probability of default for the applied credit line is

calculated. 5. A decision is made regarding the approval of the credit

line based on this probability of default.

AT-Algorithm

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CREDIT SCORE

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STATISTICAL CLASSIFICATION PROCESS

To date limited defaults with „Geldnotruf“ Prevent Overfitting: 10 fold Cross Validation and

Resampling

Approach

Random Forest Logistic Regression Neuronal Network Support Vector Machine

Tested classifcation methods

Random Forest Logistic Regression

Support Vector Machine Neuronal Network

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CREDIT SCORE

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DEMO

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THANK YOU FOR YOUR ATTENTION

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Alexander Thamm Managing Director Wilhelm-Wagenfeld-Str. 20, D-80807 MunichT +49 89 30760880M +49 176 235 [email protected]