Design and Implementation of Enterprise Financing Decision Model Based on Data Mining

7/27/2019 Design and Implementation of Enterprise Financing Decision Model Based on Data Mining

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Design and Implementation of Enterprise Financing Decision Model Based

on Data Mining

Abstract

Based on the data warehouse , data mining techniques, using the method of system, The

paper analyzed the use of data mining strategy in enterprise financing decisions, Studied the

financing decision system design goal, the function and the logic structure model, explored

the data warehouse model and data mining model structure and realization method. It is

found that heterogeneous data integration is the basis for financing decision system design,

Key lies in the multidimensional data warehouse model building, data mining algorithm

design and expression. These results are important significance on promoting the construction

of enterprise financing decision system, realizing enterprise financing decision automation

and intelligent.

Introduction

Financing decision is the enterprise according to the amount of capital demand in production

and business operation activities, selecting the appropriate financial institutions, financial

markets, financing ways to obtain funds needed for the decision-making behaviour. The

enterprise when carries on financing decisions, needs according to the cost of financing,

financing risk and the difficulty degree of obtaining fund, through the analysis to determine a

reasonable fundraising plan, and to use specific indexes for fundraising plan feasibility

analysis and evaluation, these indexes including financial evaluation index, environmental

evaluation index, risk evaluation index and evaluation expert knowledge and experience, etc.

The raising fund is business capital movement beginning, is decided that the fund movement

scale and the production operation degree of development's important link, is the enterprise

financial management and decision-making important content. In the present globalization,

information, network under the background of knowledge economy, improve enterprise

financing decision quality, efficiency, optimize capital structure is the important means to

promote the enterprise competitiveness. Establish financing decision support system for

enterprise managers can provide various auxiliary decision information, and many of the

financing solutions, thereby reduced managers in low level information processing and


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analysis of the burden, they focus on the most in need of wisdom and experience of decision-

making work, therefore improved the policy-making quality and the efficiency. In the mid

1990s, the data warehouse, the data mining and so on new technology's starting has provided

the new way for the development financing decision support system. This article has

discussed the data warehouse and data mining technology, has constructed financing decision

system model based on data mining, and key which realizes on the model: The data

warehouse design and data mining model design and implementation are studied.

Literature Survey

Data Mining as Financial Data Analysis

Financial data is mainly collected from banks and from other financial sectors. This financial

data is usually reliable, complete and has high quality. Financial data need a systematic

method for data analysis. Data Mining plays an important in analysis of financial data. Data

Mining follows steps such as data collection and understanding, data refinement, model

building and model evaluation and deployment. These steps help to deal with analysis of

financial data. The proper analysis of financial data enables us to better decisions makingcapabilities according to the market analysis. Data Mining tools and techniques helps to

analyze the financial data in the following ways:

Data collected from the various financial institutes like banks are first collected in the data

warehouse. Multidimensional data analysis techniques are used to analyze such data collected

in data warehouse for its general properties.

One of main task related to analyze about the prediction about loan payments and customer

credit policies. For such analysis , Data Mining methods such as feature selection that helps

to identifies the various features like customer income level, payment to income ratio and

credit history etc. By analyze of such features the bank can decide about the loan grant

policies on the basis of relatively low risks.

Clustering and Classification techniques of Data Mining help the financial institutes to

cluster various customers that have the similar features. Effective clustering and filtering


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methods helps the bank to identify the customer groups,relate new customer with the present

cluster and facilitate them some common benefits.

Data Mining tools helps the financial institutes to detect the frauds or crimes by interrelate

data from the various data bases and from the history transactions done by that customers.

Data Visualization tools help to present data in the different format like graphs based on the

certain attributes. By viewing data from different angles the bank can view the customers

who have performed some illegal operations and then the detail investigation of these

suspicious cases helps to find the frauds and crimes.

The use of big data will become a key basis of competition and growth for individual firms.

From the standpoint of competitiveness and the potential capture of value, all companies needto take big data seriously. In most industries, established competitors and new entrants alike

will leverage data-driven strategies to innovate, compete, and capture value from deep and

up-to-real-time information. Indeed, we found early examples of such use of data in every

sector we examined.

The use of big data will underpin new waves of productivity growth and consumer surplus.

For example, we estimate that a retailer using big data to the full has the potential to increase

its operating margin by more than 60 percent. Big data offers considerable benefits to

consumers as well as to companies and organizations. For instance, services enabled by

personal-location data can allow consumers to capture $600 billion in economic surplus.

While the use of big data will matter across sectors, some sectors are set for greater gains. We

compared the historical productivity of sectors in the United States with the potential of these

sectors to capture value from big data (using an index that combines several quantitative

metrics), and found that the opportunities and challenges vary from sector to sector. The

computer and electronic products and information sectors, as well as finance and insurance,

and government are poised to gain substantially from the use of big data.

Several issues will have to be addressed to capture the full potential of big data. Policies

related to privacy, security, intellectual property, and even liability will need to be addressed

in a big data world. Organizations need not only to put the right talent and technology in

place but also structure workflows and incentives to optimize the use of big data. Access to

data is criticalcompanies will increasingly need to integrate information from multiple data

sources, often from third parties, and the incentives have to be in place to enable this.


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Impractical Manual Data Analysis

The traditional method of turning data into knowledge relies on manual analysis and

interpretation. For example, in the health-care industry, it is common for specialists to

analyze current trends and changes in health-care data on a quarterly basis. The specialists

then provide a report detailing the analysis to the sponsoring health-care organization; the

report is then used as the basis for future decision making and planning for health-care

management. In a totally different type of application, planetary geologists sift through

remotely sensed images of planets and asteroids, carefully locating and cataloging geologic

objects of interest, such as impact craters.

For these (and many other) applications, such manual probing of a dataset is slow,expensive, and highly subjective. In fact, such manual data analysis is becoming impractical

in many domains as data volumes grow exponentially. Databases are increasing in size in two

ways: the number N of records, or objects, in the database, and the number d of fields, or

attributes, per object. Databases containing on the order of N=109 objects are increasingly

common in, for example, the astronomical sciences. The number d of fields can easily be on

the order of 102 or even 103 in medical diagnostic applications. Who could be expected to

digest billions of records, each with tens or hundreds of fields?

Yet the true value of such data lies in the users ability to extract useful reports, spot

interesting events and trends, support decisions and policy based on statistical analysis and

inference, and exploit the data to achieve business, operational, or scientific goals. When the

scale of data manipulation, exploration, and inference grows beyond human capacities,

people look to computer technology to automate the bookkeeping. The problem of

knowledge extraction from large databases involves many steps, ranging from data

manipulation and retrieval to fundamental mathematical and statistical inference, search, and

reasoning.

Different techniques and planning has been used in past for the building financial model in

enterprises. so far data mining has never used for modelling the strategy for business or

financial decision. so we have proposed datamining technique for enterprise decision model.

we have proposed datamining techniques which will take care of finance, expense project

timeline for tracking the deadline etc. In future it can be used at different levels in enterprise

for accounting and managing financial values. Datamining integrates the every aspect of


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enterprise to a centralized software. This software helps the enterprise for making decisions

and fulfilling the needs of the company.

By following these operation and tedious tasks, the management faces following problems:

Production delays due to raw-material non-availability Stuck-up of investments in raw-material inventories Ineffective control over raw material issuance and wastages.

Data mining System is cost-effective solution for managing raw material & inventory.

Managing the inventory reduces the inventory carrying cost by Inventory procurement and

assisting the management in just in time decision-making. Datamining War house inventory

System is ideal business solution for manufacturers and Producers who want to reduce their

operational costs and become more competitive.

Warehouse System provides elegant, effective, and practical solution to automate the

Procurement process and other up-stream supply chain operations. High ROI (return on

Investment) is guaranteed in the form of optimized inventories.

Warehouse System Inventory provides a competitive edge to manufacturers by reducing theinventory Carrying cost and by avoiding production delays through timely availability of raw

Materials. Also the cost of production goes down due to control in the raw material

Warehouse SYSTEM optimize the raw material inventories by adopting an intelligent

inventory procurement process. Inventory procurement is completely automated.

Warehouse System manages the procurement of inventory on the basis of purchase orders.

System performs the analysis of required inventory items for manufacturing orders, and

generates the demand for a particular item to the particular vendor of each of the inventory

items. It then generates the purchase orders for vendors (automated procurement process).

The inventory is procured only when its required, and thus the total stock available in the

Warehouse is reduced and inventory-carrying cost goes down.

Management of large number of orders, starting in different calendar dates, involving

different inventory items, become easy and effortless operation. Ware house System not only

informs the users about the current level of inventory available in the Ware house, but also it


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gives the details about the utilization of inventory items for future manufacturing orders by

generating demands.

Data Mining

Data mining, from a technical point of view to define, is from massive, incomplete, noisy,

fuzzy and random data sets identify effective, innovative and potentially useful and

ultimately understandable patterns of the process. Defines from the commercial angle, data

mining is a commercial information processing technology, its main characteristic is to

commercial business data extraction, conversion, analysis and other model processing,

discovers the valuable hideaway information in large amounts of data, analyzes, makes the

induction inference, helps the enterprise policy-makers to adjust the market strategy, provides

the reference for the enterprise decision-making. In today's rapidly changing business

environment, the main way of competition is the competition information, the traditional

model of data analysis, after analysis method was prior to explore the type of data mining.

Meanwhile, between the information provider also has the keen competition, like between

financial information and non-financial information competition. Not only the data mining

technology's application increased the competition chip for the information provider, alsoimpelled data mining expansion directly.


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System Architecture

Data warehouse

Data warehouse is a better support enterprise or organization for decision-making analysis

and processing, subject-oriented, integrated, not updated, with time changing, the collectionof data used to support management decisions. According to this definition, data warehouse

is different from the existing operational database, is made of several heterogeneous data pre-

processing, according to certain rules collection, and is the data management further

development.

Multi-dimensional data processing

The multi-dimensional data processing is by analysis, management and decision-making

executives from several angles from the original data conversion to the user, can truly

understand the information quickly and consistent, interactively access to data, so as to

achieve a deeper understanding of software technology

Financing Decisions

In financing decision system is mainly used in the following data mining strategy.


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1. Classification, classification is a function to each data item is mapped to a predefinedcategory, or is excavates about this kind of data description or the model, with

emphasis on model building, the new instance assigned to a group of defined as a

class. For example: to determine whether a high-risk financing to all funding

programs are classified as high risk or low risk groups and so on.

2. Estimation, and the classification model is similar, estimation model aims todetermine an unknown value of the property. However, unlike the classification of

property is an estimation problem, the output attributes (one or more) is a numerical

rather than classification. For example: Estimates a financing risk degree, Estimates

the cost of financing, etc.

3. Prediction, prediction model aims to determine the future output rather than thecurrent behaviour. Through establishing model of representing data patterns and

trends inherent, so that the model can be used to predict the outcome of future events,

in financing decision system, there are often some of the examples of forecasts:

Forecasting margins of financing, Forecasting the use effect of capital, Prediction of

project investment funds requirements, etc.

4. Without guide clustering, For without guidance clustering, no dependent variableguide learning process. without guidance clustering strategy aims to find the data

concept structure, is one kind to has the common tendency and the pattern data

element group carries on the grouping the method

System Analysis

The financing decisions system based on data mining design goal is: according to historical

data and enterprise development goal establishment enterprise fund raising plan; Establishesa set of perfect fund raising evaluating index system and the corresponding measurable

standard, causes the fund raising procedure standardization; Take the cost theory as the

foundation, carries on the cost appraisal to the fund raising plan; Take the risk theory as the

foundation, the determination fund raising plan risk factor; Through the correct appraisal

fund raising's cost and the risk, improve the effectiveness of financing decisions. Finally

realize enterprise financing decision process optimization, scientific, intelligent target.

To achieve these goals, the system should have the following main functions:


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Financing business and planning functions: According to historical data accumulated from

the business operating environment, its operating conditions, analyzing operational and

financial benchmarks, and funding opportunities, develop practical business and financing

plans.

Model establishment and management functions: in a proper way, financing decision

model is stored and in an interactive way, realize financing decision model establishment,

modify and management.

Financing decision functions: According to the establishment fund raising evaluating index

system and the corresponding measurable standard, using the appropriate model and expert's

knowledge and experience, auxiliary enterprises to choose and determine funding financing

opportunity, determination fund raising scale, choice fund raising way, determination debt

redemption way decisions making; through the advanced intelligent optimal selection of a

more systematic project, the project on mutually exclusive decision-making choices, etc.

Modules

Admin Function Expense tracking Project timeline Timetable salary module

Proposed System

This is high performance software, which speeds up the business operations of the

organization. Every organization, which deals with the raw materials, put its great effort in

the efficient utilization of its raw material according to its need and requirement. The

organization has to perform number of tasks and operations in order to run its business in a

manual system. For example:

Estimation of new raw material required. Preparation of purchase order. Preparation of Inward gate pass/purchase invoice. Preparation of Outward gate pass /sale invoice.


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Preparation of Debit note.

Advantages of systems are as follows:

1. Inventory information can be handled easily.2. The manager can easily view when the updates are done at the point of

sale devices.

3. The manager can make decisions very fast.4. The manager can plan the goods production.5. Automatic value generation.6. Expense Tracking7. Project Timeline

Major functions of Data mining warehouse SYSTEM include the following:

Inventory Procurement

Manage procurement schedule Keep minimum required inventory levels Purchase only when required for productionprocurement automation

Monitor and improve Vendor relations

Monitor supplier commitments Manage supplier payments

Payments

Payments into/from Purchase Orders are recorded providing Purchase OrdersBalances

Customizability and Flexibility

User defined attributes for inventory items


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Definition of inventory items, their vendors, vendor prices Definition of customers and their relevant information Definition of departments and their employees

Data Flow Diagram (DFD)

Level 0

Report

Level 1

User

1.0

DM

Warehouse

System

User

1.0

DM

System

Tool


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Level 2

Database

.

1.1

Financial

Decision

1.2

Payment

1.3

Expense

1.4

Project

User2.1

Authenti

cation

2.2

Expense

2.3

Software

Tool

2.3

Project


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System Specification

System Requirements:

Hardware Requirements:

Processor : Intel Duel Core.

Hard Disk : 60 GB.

Floppy Drive : 1.44 Mb.

Monitor : LCD Colour.

Mouse : Optical Mouse.

RAM : 512 Mb.

Software Requirements:

Operating system : Windows XP.

Coding Language : JAVA

Data Base : SQL Server 2005

Tool : Netbeans 7.0

SYSTEM STUDY

FEASIBILITY STUDY

The feasibility of the project is analyzed in this phase and business proposal is put

forth with a very general plan for the project and some cost estimates. During system

analysis the feasibility study of the proposed system is to be carried out. This is to ensure

that the proposed system is not a burden to the company. For feasibility analysis, some

understanding of the major requirements for the system is essential.


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Three key considerations involved in the feasibility analysis are

ECONOMICAL FEASIBILITY TECHNICAL FEASIBILITY SOCIAL FEASIBILITY

ECONOMICAL FEASIBILITY

This study is carried out to check the economic impact that the system will have on

the organization. The amount of fund that the company can pour into the research and

development of the system is limited. The expenditures must be justified. Thus the developed

system as well within the budget and this was achieved because most of the technologies

used are freely available. Only the customized products had to be purchased.

TECHNICAL FEASIBILITY

This study is carried out to check the technical feasibility, that is, the technical

requirements of the system. Any system developed must not have a high demand on the

available technical resources. This will lead to high demands on the available technical

resources. This will lead to high demands being placed on the client. The developed system

must have a modest requirement, as only minimal or null changes are required for

implementing this system.

SOCIAL FEASIBILITY

The aspect of study is to check the level of acceptance of the system by the user. This

includes the process of training the user to use the system efficiently. The user must not feel

threatened by the system, instead must accept it as a necessity. The level of acceptance by the

users solely depends on the methods that are employed to educate the user about the system

and to make him familiar with it. His level of confidence must be raised so that he is also able

to make some constructive criticism, which is welcomed, as he is the final user of the system.

Design and Implementation of Enterprise Financing Decision Model Based on Data Mining

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