IS480 Team wiki: 2013T1 Kungfu Panda X-Factor
Contents
- 1 X-Factor Component
- 2 Bank Teller User Interface
- 3 Credit Engine Technology Evaluation
X-Factor Component
Bank Teller User Interface
The User Interface of our SMUtBank Teller Application has been made to be as cutting-edge and as Native-like as possible. Much of which is absent from typical Websites.
Utilizing a couple of important features:
- Single-Page Design (Back and Forward arrows are still usable)
- Front-end validation for every field
- Useful Visual Effects
- High performance
- Page caching
- Minimal Clicks
- Simple to use
- Tight and fixed layout
Another important feature of User-Interface is a strong automated testing
Single Page Design
We make use of a Single Page Design for a very strong reason: Speed. Many of the page "changes" rely on Backbone.js to swap elements in and out and manage data through the use of AJAX (Asynchronous Javascript and XML). The advantages of this are clear:
- All resources only needed to be loaded once
- Page requests are minimized to AJAX requests
- Better user experience and no page "whiteouts" (White page when loading), making it closer to the performance of a native application.
<pictures of single page in action here>
However, there are also some disadvantages to this method:
- Increased complexity in managing the web application, much more Javascript to manage the AJAX requests
- Much more logic checks in place.
Front-end validation
Every field and button click is validated and checked. After the field has been edited by the User, it will be checked immediately via Javascript to ensure basic requirements have been met, such as if it is an Integer, a Float, or a Varchar. There are some additional checks based on the functionality of the page, such as LTV ratio or if the account has a sufficient balance. Certain aspects of validation have to be handled by the backend service, such as checking for duplicate Identification numbers.
<pictures of error happening>
Validation is placed directly on the input field itself, through the use of HTML5 data attributes e.g. data-type="varchar". These data attributes allow the Javascript to view through the element and determine what validation process to apply to it.
<pictures of html code>
Useful Visual effects
Almost every action, successful or not will produce a notification message 10 (15 for errors) seconds long and manually cleared. This is to give the user active feedback on his/her actions, it obstructs the screen slightly and forces the user to actively click it to ensure his/her acknowledgement.
<picture of notifications>
As part of every important validation process, Signposts are extremely clear as to what the user should do. The input field is coloured red, or there will be a red notification message on the top right to signify a negative event. Should that not be enough, the borders of the screen will glow a slight red hue to clearly warn that the user has made a mistake. This idea itself was copied from modern First-Person shooter games who use reddish screens to warn of impending danger. It is applied to our application in great effect, without producing a conspicuous and inhibiting alert message as do most web applications.
<pictures of sign posts>
Use of highlights and automated-screen scrolling is important too. Upon clicking certain unique elements, the user will be automatically scrolled to the point we want them to focus on. A good example is the Transaction History page, where a user is able to click a point on the graph and it will direct the user to a readable text version of the transaction. Errors that are out of sight will be scrolled to to ensure that the user sees the first error of the page, so as not give bring confusion as to why there is an error but not being able to see one (When it is actually out of view).
<pictures of automated scrolling>
- High performance
- Page caching
- Minimal Clicks
- Simple to use
- Tight and fixed layout
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
User Interface of Credit Approval Form
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.
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%)
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