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Difference between revisions of "IS480 Team wiki: 2013T1 Kungfu Panda X-Factor"

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== X-Factor Component ==
 
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Revision as of 23:03, 24 November 2013

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Home About Us Project Overview Project Management User Testing Project Documents


SMU tBank Our Project Scope Our X-Factor Technologies Going Forward

X-Factor Component

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Bank Teller User Interface

The User Interface of our SMUtBank Teller Application has been made to be as cutting-edge as possible. Utilizing a couple of important features:

  1. Single-Page Design (Back and Forward arrows are still usable)
  2. Front-end validation for every field
  3. Strong usage of visual effects to guide the user
  4. High performance
  5. Page caching
  6. Minimal Clicks
  7. Fully tab-traversable
  8. Instructions for first-time users
  9. Tight and fixed layout for familiarity and fast learing


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.

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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.
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Credit scoring Process Flow

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Credit approval process diagram

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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.


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Examples

FICO's calculation based on Length of Credit History



FICO Types of Credit Used



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Mockup of Credit Approval Form

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User Interface of Credit Approval Form

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Teaching Tool


The teaching tool allows users to simulate varying customer profiles (credit capacity) and also different weightages and thresholds for each of the credit rules. This gives students a hands on learning experience on generic credit scoring rules utilize by banks.


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Teaching tool process flow

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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:

Residence:

'Residence Stability:

Job Stability:

No of Credit Cards

Credit and Banking History

Loan Quantum (HDB Flat)

Age


Teaching tool Features (Input)

Data Generation

  • Loan Profiles (Demographic information of loan applicants)

Scoring Engine Customization

  • Rules customized to user’s preference
    • Customizable Threshold/Ranges
    • Customizable Weightage



Teaching tool Features (Output)

Breakdown

  • # of approved/rejected/pending loans
  • Min, Max, Average Credit Score
  • Score-breakdown for individual loan profile



Teaching Tool Mock Up

Teaching Tool Data Generation

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Teaching Tool Rule Customization

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Teaching Tool Summary of Results (Output)

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Teaching Tool User Interface

Teaching Tool Data Generation

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Teaching Tool Rule Customization

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Teaching Tool Summary of Results (Output)

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