RECOM BANKING SOLUTION

BANKING SYSTEMINFORMATION REQUIREMENTS ARE FOR:• BANK HEADQUARTERS• BANK BRANCH OFFICES • ON-LINE BANKING • ATM BANKING• FIXED DEPOSITS• INVESTMENTS• LOANS• CASH RESERVES• CUSTOMERS• COMPANIES

INFORMATION REQUIRED TO• MANAGE ACCOUNTS• TRANSACTIONS ON-LINE, OFF-LINE AND ATM • MAINTENANCE OF RESERVES• STATISTICS• GENERATE REPORTS• INCREASE BUSINESS• IMPROVE PROFITABILITY• INCREASE CUSTOMER BASE• INCREASE NUMBER OF ACCOUNTS• DEPLOY FUNDS ON NEW LOANS• RISK CONTROL• FRAUD CONTROL• PERFORMANCE MONITORING

CONSOLIDATED INFORMATION

• TOO LARGE TO ANALYZE QUICKLY AND EFFICIENTLY USING REGULAR INFORMATION TECHNOLGY TOOLS

• REGULAR INFORMATION TECHNOLOGY TOOLS LACK ANALYTICAL POWER

• REGULAR INFORMATION TECHNOLOGY TOOLS LACK FORECASTING POWER

• STRATEGIC INFORMATION IS DIFFICULT TO CONSOLIDATE AND ANALYZE

RECOM BANKING SOLUTION PROVIDES:

• STUDY OF REQUIREMENTS TO MEET THE SPECIFIC REQUIREMENTS OF THE BANK

• TRANSLATE REQUIREMENTS INTO WORKING INFORMATION TECHNOLOGY MODELS

• CREATE DATABASE AND DATA WAREHOUSE• IMPORT DATA FROM VARIOUS SOURCES• EXPLORE DATA FOR EXCEPTIONS• CLEAN DATA USING ADVANCED TECHNIQUES• PARTITION DATA FOR EFFICIENT ALALYSIS

CONTINUED RECOM BANKING SOLUTION PROVIDES:

• CREATE DATA WAREHOUSE• USE BUSINESS INTELLIGENCE APPLICATIONS• GENERATE REPORTS• USE ARTIFICIAL INTELLIGENCE ALGORITHMS• ANALYZE DATA AND GENERATE FORECAST

MODELS• GENERATE FORECAST• GENERATE REPORTS

SOLUTION COMPONENTS• HIGH LEVEL SOLUTION DESIGN• LOW LEVEL SOLUTION DESIGN• TEST BENCH DESIGN• NETWORK REQUIREMENTS DESIGN• TELECOM REQUIREMENTS DESIGN• DATA INTEGRATION DESIGN• DATABASE DESIGN• DATA WAREHOUSE DESIGN• ANALYSIS• USING BUSINESS INTELIGENCE TOOLS• USING ARTIFICIAL INTELLIGENCE TOOLS

SOLUTION MANAGEMENT

• ADVANCE TECHNIQUES TO ANALYZE AND MANAGE CUSTOMER REQUIREMENTS

• MANAGE PROJECT AS PER INTERNATIONAL STANDARDS

• QUALITY CHECKS AND PROCESSES AS PER INTERNATIONAL STANDARDS

• COMPLETE DOCUMENTATION • CUSTOMER TRAINING • AFTER SALES SUPPORT

Data Mining

• Machine learning of patterns in data• Application of patterns to new data

Mining Model

What does Data Mining do?Illustrated

DMEngine

Data To Predict

DMEngine

Predicted Data

Training Data

Mining Model

Mining Model

DB dataDB dataClient dataClient dataApplication dataApplication data

DB dataDB dataClient dataClient dataApplication dataApplication data““Just one rowJust one row””

Salients

What Does Data Mining Do?

Explores Your Explores Your DataData

Finds Finds PatternsPatterns

Performs Performs PredictionPrediction

ss

““Putting Data Mining Putting Data Mining to Work”to Work”

““Doing Data Doing Data Mining”Mining”

Business Business UnderstandingUnderstanding

Data Data UnderstandingUnderstanding

Data Data PreparationPreparation

ModelingModeling

EvaluationEvaluation

DeploymentDeployment

DataData

Data Mining ProcessCRISP-DM

www.crisp-dm.orgwww.crisp-dm.org

SSASSSAS(Data(Data

Mining)Mining)

SSAS SSAS (OLAP)(OLAP)DSVDSV

SSISSSISSSAS(OLAP)SSAS(OLAP)SSRSSSRSFlexible APIsFlexible APIs

SSISSSISSSASSSAS(OLAP)(OLAP)

Business Business UnderstandingUnderstanding

Data Data UnderstandingUnderstanding

Data Data PreparationPreparation

ModelingModeling

EvaluationEvaluation

DeploymentDeployment

DataData

Data Mining Process in SQLCRISP-DM

www.crisp-dm.orgwww.crisp-dm.org

DataData

Algorithm Matrix

Time Series

Sequence Clustering

Neural Nets

Naïve Bayes

Logistic Regression

Linear Regression

Decision Trees

Clustering

Association Rules

Classificatio

n

Classificatio

n

Estimatio

n

Estimatio

n

Segmentation

Segmentation

Associa

tion

Associa

tion

Forecasting

Forecasting

Text Analysis

Text Analysis

Advanced Data

Advanced Data

Exploration

Exploration

What Do Data Mining Applications Do?

Explores Your Explores Your DataData

Finds Finds PatternsPatterns

Performs Performs PredictionPrediction

ss

Automatic Automatic MiningMining

Pattern Pattern ExplorationExploration

Perform Perform PredictionPrediction

ss

Algorithm Training

Algorithm Module

Case Processor

(generates and prepares all

training cases)

StartCases

Process One Case

Converged/complete?NoYes

Done!

Persist patterns

PredictionParser Validation-I &

InitializationAST

Binding &Validation-II

DMX treeExecution Planning

DMX tree

Input data

Read / Evaluate one row

Push response

Untokenize results

Income Gender

$50,000 F

1 2

50000 2

1 2 3

50000 2 1

Income Gender Plan

$50,000 F Attend

Multi-Cube Multi-Dimension Design

• Banking Solution is based on Multi-Cube Structure for Data Mining Applications

• Each Cube has multiple Dimensions• Multiple Location Deployment• Business Intelligence Applications running on

Multiple Cubes using Complex Calculations• Artificial Intelligence Applications Running on

Different Dimensions