Alchemist - PowerPoint PPT Presentation

1 / 38
About This Presentation
Title:

Alchemist

Description:

Alchemist The Software for Norkom s Solutions Alchemist Design Principles Norkom s View of CRM Where is Value Added and When? Functional Architecture ... – PowerPoint PPT presentation

Number of Views:267
Avg rating:3.0/5.0
Slides: 39
Provided by: ijaffaNet
Category:
Tags: alchemist

less

Transcript and Presenter's Notes

Title: Alchemist


1
Alchemist The Software for Norkoms Solutions
2
What is Alchemist?
Multiple Delivery Channels
Information Dissemination Deliver Intelligence
Information where when it is needed
Notification Technologie
Apply Intelligence
Algorithms for caching and optimising data
harvesting
Enterprise and Legacy data
3
Alchemist Design Principles
  • Modular in design and roll-out options
  • Robust Open Technology Platform
  • Real-time and batch transaction monitoring
  • User-friendly, easy to use
  • Based on Web-enabled technology

4

Alchemist CRM Intelligent Marketing Automation
5
Norkoms View of CRM
6
Where is Value Added and When?
Provide a consistent message accross
channels Propose the right product _at_ rigth time
_at_ the right customer/prospect
Identify new Value propositions based on enriched
customer/prospects knowledge and market profiles
Full Leverage requires ability to drive
product/services design according to findings
Learn from experience and drive Business process
7
Analysis and Customer Lifecycle
Profitability
Churn
Cross- Sell
Fraud
Winback
Promotion
Customer Relationship Management - Micro
Credit Scoring
Account Application
Monthly Statement
Rate Enquiry
Complaint
Usage
Upgrade
Exit
Customer entry
Customer Management
Customer Exit
Competitor Promotion
New Home
New Job
Marriage
Children
Retirement
Competitor Promotion
8
Functional Architecture
9

Alchemist Intelligence
10
Alchemist Intelligence Workbench FeaturesData
Mining Suite
  • DESCRIPTIVE
  • Alchemist offers a comprehensive range of
    descriptive techniques including
  • Association Rules
  • Kohonen Neural Network
  • K-means
  • Decision Trees eg C4.5 (This can be viewed as
    both a descriptive and predictive technique)
  • Data audit tools
  • PREDICTIVE
  • Alchemists strong portfolio of predictive
    techniques includes
  • Robust Regression
  • Naïve Bayes
  • Back Propagation Neural Network
  • Decision Trees eg. C4.5

11
Predictive Modelling Outputs of a Predictive
Exercise
  • Scored list
  • Model Contributions
  • Individual Contributions

T OReilly Ltd Score 0.9 from turnoverhigh
and ave_contract_classC AR Building contractors
Ltd Score 0.9 from turnoverlow and
ave_contract_classR
12
Use of information you have (today and in the
future)
Billing
No. of dropped
Tenure
No. of calls
Dealer
No. Premium Rate
No. of Intl
Usage Information
Customer Information
Dependants
Intelligence Workbench
No. of SMS
Occup
Price Plan
Insurance
Corporate
Handset
Dealer
Product Information
Upgrades
Roaming
Discount
Pre/Post
supplemental

13

Alchemist Marketing Automation
14
Marketing Automation
  • The Automation of the marketing task of managing
    intelligent contact with Customers
  • Closed Loop from Customer data through Modelling
    and Insight to Customer Contact
  • Customer Intelligence Capability Modelling,
    Profiling Model Management
  • Campaign Management Batch, Event Driven and
    Real-Time contact with customers through all
    channels (Phone, DM, E-Mail, Call Centre etc.)

15
Benefits of Marketing Automation
  • Drive up response rates to campaigns
  • Timely and relevant customer offerings
  • Intelligence driven customer contact strategy
  • Easy selection of the right customer for right
    campaign
  • Better models driven by central contact and
    response repository
  • Optimise message and channel selection
  • Run 100 focused campaigns not 10 broad-brush
    campaigns
  • Drive down the cost of acquisition and conversion
  • Capture and report on campaign effectiveness
  • Optimal re-use of effective campaigns (learn by
    doing)
  • Reduce waste by contacting only good prospects

16
Alchemist Marketing Automation Features
  • Campaign Definition
  • Automatic customer list generation based on
    business theme being addressed (e.g. Cross-sell)
    taking into account
  • volumes, profiles, campaign content and channel
  • Automatic Campaign Execution
  • Execute across multi-channel environment while
  • Achieving channel optimisation
  • Include necessary authorisation logic
  • Automatic Campaign Reporting
  • Review predicted vs. actual campaign performance
  • Understand customer to channel preference
  • Build product to customer profile by channel
    learning from
  • previous campaigns

17
  Operational CRM Platform
 
  Enterprise Automated Marketing Platform
Predictive Applications Intelligent Information
Workflow Campaign Management
Prepayment Risk
Customer Retention
Cross-Sell
Life-time Value
Channel Optimisation
Customer Acquisition
Credit Risk
    Critical Business Issues
Demographic/ Marketing Databases
    Customer and Third Party Information
Other Data Sources - House Price Indices -
Pricing Credit Data
Customer Database
- Mortgage Servicing Database - Retail Bank
Database - Origination Database - Marketing
Database
- Solimar/Base100 - DataQuick - CSW
- Equifax - MRAC - TRW -
MIC - Experian
- Acxiom InfoBase - MITI
18

Alchemist Risk Fraud Management
19
Integrated Solution for Financial Crime
  • Alchemist for AML modules for
  • Case Management
  • Scenario detection engine
  • Customer Profiling engine
  • Advanced Name matching with prepopulated data
    dictionary and language rules
  • List management
  • Reporting and audit
  • Security and user administration
  • Real time and batch operation
  • Standard Interfaces
  • Automated 3rd party list integration
  • E.g. World Check
  • Provision of World Check list of PEPs and
    Sanction lists etc.


20
Alchemist Configuration
Watch Lists
OFAC, BOE EU World-Check etc
Manual Reporting
Dashboard
FIU Email Fax
  • Business Rules
  • Unusualness Tests
  • Predictive Modelling
  • Network Analysis

Compliance Workflow
Source Transactions Data
Alchemist Data Model
Analysis Reporting
  • Watch List Monitoring
  • Watch List Rules

The Alchemist HUB
21
Alchemist Fraud Functional Architecture
Alchemist AML
Analysis Reporting
Case Management
Name Matching
Portal Knowledge Base
Profiling Segmentation Engine
Intelligent Agents Alerts Notification
Rules Engine
22
Alchemist Name Matching
  • Data dictionary per country
  • Multiple character sets
  • Consolidated list management
  • From multiple sources
  • Sanction lists PEPs
  • External and internal lists
  • Automated list update and refresh
  • Fuzzy logic search capability
  • Support for multiple jurisdictions
  • Flexible audit and reporting capability
  • Integration with Alchemist Case Development,
    Workflow and reporting

23
Search Matching Ability
  • peoples names
  • account or compound names
  • delivery or postal addresses
  • additional attributes
  • Covered by Population Rules
  • Covered by Language Rules
  • Covered by Character set Rules
  • regardless of spelling variation, phonetics,
    abbreviation, nicknames, missing or extra words,
    word sequence variation or data quality .
  • with controllable performance and response times

24
Rules Engine
  • Detailed Rules insurance, banking, investments
  • Flexible
  • Detect unusual behaviour for accounts and
    accounts in segments
  • Robust, scalable
  • Rules maintained by MLRO, not IT, easy to use
  • Operate automatically

Rule Triggers
Account Transaction Profiles
Suspicious Cases
Early policy cancellation in unusual
circumstances Cash Deposit, many ATM withdrawals
25
Case Management
  • Rules that are triggered or predictions above
    thresholds create suspicious customer records.
  • Detailed customer profile and transaction history
    sourced
  • Several states
  • Triggered, Monitored, Reported, Clear,
    Investigation
  • All cases are prioritised by further rules
  • Historical activity/cases
  • Severity of rule Predictions
  • Full audit trail and security

Automatic Prioritisation
26
Segmentation and Risk Scoring
  • Based on continuing behaviour, behavoural profile
    models are built/rescored for each account,
    trading house and/or company
  • Notification of Unusual account / trading
    behavour
  • Client behavioural clustering / movement
  • Increase in business knowledge (client, house,
    market)
  • Early indication of equity trading outside
    normal price bands
  • Based on historical records, predictive models
    for each segment are built
  • Propensity to exhibit market abuse activity
    scores created for each account
  • Allow for stastical tuning of business rules

27
Fraud Modules
  • Card Fraud
  • Debit Cards
  • Credit Cards
  • Skimming
  • Kiting
  • Devices
  • Branch Fraud
  • e-Banking Fraud
  • Identity Theft
  • Access Behaviour
  • Device Analytics
  • Tax Fraud
  • VAT Fraud
  • Insider trading
  • Market Timing
  • Loan Application Fraud
  • Loss recovery
  • Internal Fraud

28

Business Intelligence and Performance Management
29
What are we trying to achieve?
We want to put in place an environment that
facilitates proactive, strategic, business
decisions at the appropriate level within Banks
abd Telcos by exploiting thedata contained
intheir InformationSystems
BalancedScore Card CustomerReport
Card CrossFunctional Analysis
INTELLIGENCE
On-Line Analytical Processing
INFORMATION
Subject Area Databases
Customer Segmentation
Micro-Marketing
On-Line Transactional Processing
DATA
Sales
External Customer Data
Marketing
Engineering
Financial Data
30
Event-based Reporting / Decisioning
Portal KPIs
  • Personalised
  • Digital Dashboard
  • Dedicated KPI Module

Analysis Reporting
Alchemist Rules Engine
  • Executive Alerting
  • Survey distribution
  • Survey Collection
  • Alert Escalation

Balanced Scorecard Datamart
  • End-user Query
  • Production Reporting
  • OLAP Analysis

Alchemist Agents
Alchemist Intelligence
  • Simulated Business Modelling
  • What-If Analysis
  • Forecasting
  • Data Collection
  • Transformation
  • Data Loading

31
Visualisation
32
(No Transcript)
33
An example showing some chart type variations
34
Example of an Architecture
35
System Architecture
36
Technology Options
  • Technology

Technology Options
Web Server
IIS, Sun ONE, Oracle 9I AS Apache, Websphere,
Weblogic
AML Application server
Apache Tomcat, Websphere, Weblogic, Sun ONE,
Oracle 9I AS
AML Database
Oracle, DB2, SQLServer, Teradata, any JDBC
compliant relational database
Operating system
SUN 2.6, 2.7 .28, IBM AIX 4.3 5.1, HP 11 11i,
Microsoft 2000, NT 4
Client platforms
Standard Win98, WinNT, Windows 2000, Win XP
desktop, IE5.5
37
Alchemist standard options
  • Supported Hardware
  • Supported Operating Systems
  • Supported Databases

38
Hub based approach
Write a Comment
User Comments (0)
About PowerShow.com