The document outlines a comprehensive banking solution that addresses the information requirements for various banking operations, including account management, transactions, and risk control. It details the components of the solution, including advanced data mining and artificial intelligence techniques for data analysis and forecasting, integrated with a multi-cube data structure. Additionally, it emphasizes the importance of adhering to international project management standards and provides support for customer training and documentation.

1. RECOM BANKING SOLUTION

2. BANKING SYSTEM INFORMATION REQUIREMENTS ARE FOR: BANK HEADQUARTERS BANK BRANCH OFFICES ON-LINE BANKING ATM BANKING FIXED DEPOSITS INVESTMENTS LOANS CASH RESERVES CUSTOMERS COMPANIES

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Data Mining Machine learning of patterns in data Application of patterns to new data

10. What does Data Mining do? Illustrated DM Engine DM Engine Predicted Data DB data Client data Application data DB data Client data Application data “ Just one row ” Mining Model Data To Predict Training Data Mining Model Mining Model

11. Salients

12. What Does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions

13. Data Mining Process CRISP-DM “ Putting Data Mining to Work” “ Doing Data Mining” Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

14. Data Mining Process in SQL CRISP-DM SSAS (Data Mining) SSAS (OLAP) DSV SSIS SSAS(OLAP) SSRS Flexible APIs SSIS SSAS (OLAP) Data www.crisp-dm.org Data Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment

15. Algorithm Matrix Time Series Sequence Clustering Neural Nets Naïve Bayes Logistic Regression Linear Regression Decision Trees Clustering Association Rules Classification Estimation Segmentation Association Forecasting Text Analysis Advanced Data Exploration

16. What Do Data Mining Applications Do? Finds Patterns Performs Predictions Explores Your Data Automatic Mining Pattern Exploration Perform Predictions

17. Algorithm Training Algorithm Module Case Processor (generates and prepares all training cases) StartCases Process One Case Converged/complete? No Yes Done! Persist patterns

18. Prediction Parser Validation-I & Initialization AST Binding & Validation-II DMX tree Execution 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

19. 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