Difference between revisions of "IS480 Team wiki: 2013T1 Kungfu Panda X-Factor"
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===Teaching tool Features (Output)=== | ===Teaching tool Features (Output)=== | ||
[[Image:KP-TTFeatures2.PNG|319x118px]]<br/><br/> | [[Image:KP-TTFeatures2.PNG|319x118px]]<br/><br/> | ||
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+ | == Teaching Tool Mock Up== | ||
+ | ===Teaching Tool Data Generation=== | ||
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+ | ===Teaching Tool Rule Customization=== | ||
+ | [[Image:KP-TeachingToolMockup2-1.PNG|590x428px]] | ||
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+ | ===Teaching Tool Summary of Results (Output)=== | ||
+ | [[Image:KP-TeachingToolMockup3-1.PNG|586x428px]] | ||
+ | [[Image:KP-TeachingTool DisplayRessult2.png|586x428px]] | ||
== Teaching Tool Mock Up== | == Teaching Tool Mock Up== |
Revision as of 18:53, 24 November 2013
Contents
X-Factor Component
Credit Engine Technology Evaluation
We evaluated three mainstream commercial decision engines to use as the base of our credit engine.
As Jess did not have a frontend rule repository and FICO was too costly, we finally settled on Drools as it satisfied our main criterias listed in the table below.
Credit Approval
The Credit scoring engine is a complex decision service that performs automatic credit evaluation and approval for mass consumer products such as Home Loans, Auto Loans, and Education Loans.
Credit scoring Process Flow
Credit approval process diagram
Credit Decision Engine Proof of Concept
POC for Credit Decision Engine
Market Research of Credit Engines
FICO Website: FICO Scoring Overview
FICO is a leading company in predictive analytics, specialising in scoring individual's credit-worthiness over a scale of 800 points. Our group used FICO's scoring model as our main reference when developing the rules for our Credit Engine.
Examples
____________________________________________________________________________________________________________________________
Mockup of Credit Approval Form
Teaching Tool
The teaching tool allows users to perform modeling and simulations across varying customer profiles (credit capacity) in order to optimize the the rules of the scoring engine for a particular customer profile.
Teaching tool process flow
Demographic Information for Generating Teaching Tool Loan Profiles
We retrieved our demographic information from various sources. When presented with alternative sources, we chose the source which was more reliable (e.g. from an authority such as the Government).
The demographics are modeled after the Singapore population whenever possible.
Income:
- Median of $41760
- Typically ranges between $12, 240 to $300,000 a year
- Sources: http://stats.mom.gov.sg/iMAS_PdfLibrary/mrsd-msib2013.pdf, http://www.mom.gov.sg/Publications/mrsd_singapore_workforce_2012.pdf
Residence:
- Home ownership rate of 90.1%
- Source: http://stats.mom.gov.sg/iMAS_PdfLibrary/mrsd-msib2013.pdf
'Residence Stability:
- Average of 6 years (according to US survey as there was limited SG information)
- Source: http://www.creditsesame.com/blog/how-long-are-americans-staying-in-their-homes/
Job Stability:
- Median: 4.4 years (US survey)
- Source: http://www.forbes.com/sites/jeannemeister/2012/08/14/job-hopping-is-the-new-normal-for-millennials-three-ways-to-prevent-a-human-resource-nightmare/
No of Credit Cards
- Median of 3.3 cards per individual
- 75% of eligible cardholders have 2 or more cards
- Source: http://sg.finance.yahoo.com/news/singapore-top-asia-credit-cards-105414790.html
Credit and Banking History
- 1.85% derogatory records on average
- Source: http://www.btinvest.com.sg/system/assets/16730/Moody's%20-%20Banking%20System%20Outlook%20-%20Singapore%20071513.pdf
Loan Quantum (HDB Flat)
- Ranges from $320,000 to $820,000
- Average 3 room HDB, $355,444
- Average 4 room HDB, $490,352
- Average 5 room HDB, $563,427
- Source: http://www.hdb.gov.sg/fi10/fi10321p.nsf/w/BuyResaleFlatMedianResalePrices?OpenDocument
Age
- Source: http://www.singstat.gov.sg/statistics/browse_by_theme/population.html
- Normalized for average loan takers
- 25 - 54 years (74.1%)
- 55-64 years (14.4%)
- 65 years and > (11.5%)