A SIMULATOR FOR FORECASTING THE LOCATION OF ATMs IN BANKING INDUSTRY P.K.Suri 1, Dilbag Singh 2 and Ramesh Chander 3

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1 A SIMULATOR FOR FORECASTING THE LOCATION OF ATMs IN BANKING INDUSTRY P.K.Suri 1, Dilbag Singh 2 and Ramesh Chander 3 I. ABSTRACT The introduction of Automatic Teller Machines (ATMs) has changed the way banking is done. The location of ATMs affects the business of banks heavily. Hence, the major issue that is addressed in the study is the location of ATM. To grab this opportunity the present study emphasis on designing the location of ATMs. The location depends on the transactions demanded by the customer of proprietary ATM and non-proprietary ATM. Different ATMs have been studied to formulate the probability distribution for demand pattern using pseudo random generators. The use of this simulator will be an asset to bank s policy maker. II. Keywords: ATM, Banking, Simulator, Model and Probability Distribution INTRODUCTION The present study discusses the forecasting the locations of ATM as the banks are deploying ATMs. The feasibility of ATM at particular location depends on the expected demand of transactions from the customers of proprietary ATM and non proprietary ATM. To grab this opportunity the present study deals with the design of simulator to carry out the said 1 Dr. P.K. Suri, Professor, Department of Computer Sc. & Applications, Kurukshetra University, Kurukhetra , pksuritf25@yahoo.com 2 Mr. Dilbag Singh, Lecturer, Department of Computer Science & Engineering, CDLU, Sirsa , e_mail. dbs_beniwal@rediffmail.com 3 Dr. Ramesh Chander, Department of Business Administration, CDLU, Sirsa

2 analysis. It requires deep knowledge of the business operations, business organizations, and financial structures are required to develop system models (Lianjun, 2005). Modeling can be considered as a process of knowledge acquisition about the target business operations. Business process model concretizes activities, information, and flow implanted in business operations into business tasks. A formal business process model enables the simulation of target business operations in real world (Wyssusek et al., 2001). A business process model contains different levels of granularity on operational specification. An appropriate business process simulation would provide the insights of resource usage patterns and the performance of the organization where the business processes may be deployed and functioning. A simulation is the imitation of operation of a real-world process or system over time. May be done by hand or on a computer, simulation involves artificial history of a system, and observation of that artificial history to draw interferences concerning the operating characteristics of the real system. The behaviour of a system as it evolves over time is studied by developing a simulation model. This model usually takes the set of assumptions concerning the operation of the system. These assumptions are expressed in mathematical, logical, and symbolic relationship between entities, or objects of interest, of the system. Changes in the input to the system are applied to predict their impact on system performance. Simulation can also be used to study the system at design stage (Banks et al., 2003). The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred. In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or 2

3 proposed, under different configurations of interest and over long periods of real time (Maria, 1997). Modeling enables to evaluate business process through simulation hoping to possible outcomes through what-if analysis and to help modelers understand the business processes in order to provide some insight to manage and improve the business processes. Simulation may reduces the duration of running scenario to manageable time, thus making what-if-analysis becomes possible (Banks et al., 2003). The present study deals with developing the simulator for forecasting the location for ATM installation followed by conclusion. III. FORECASTING THE LOCATION FOR ATM Forecasting the location of installing an ATM poses a challenge for the banking industry. The business of banking industry depends heavily on the location of an ATM; hence the banking industry tries to locate ATMs strategically so as to be the cost effective (Cygnus, 2004). A large number of ATM has already been installed by commercial banks throughout the country. The utility of an ATM is gaining popularity dayby-day among the customers and the benefit of these ATMs spread to all customers. The ATMs are emerging as the most useful tool for Any- Time Banking and Any-where Banking or Any-Time Money. Given the enormous benefits of ATM, the cost of ATM, is continuously declining, but still unaffordable. The cost of installing an ATM is around Rs Lacs, besides the annual maintenance cost of 8 to 12% of installation cost (Prasad, 2004). 3

4 Forecasting the location of an ATM depends on customer demand. The bank which forecasts its demand tomorrow, next week, or next month, may gain strategic advantage over its competitors. Banks can accurately predict the location of an ATM just by using forecasting techniques throughout the banking network. Advanced and easy-to-use technologies are increasingly available to help banks to forecast the customer demand (Prasad, 2004). Forecasting is a method of predicting both the future transaction volumes for a set of ATMs, and the anticipated resources needed to serve these transactions. Forecasting enable banks to gain visibility into future trends in customer demand optimize full-time/part-time employee utilization to meet that demand and to leverage what-if scenario capabilities to examine the impact of service level or staffing changes. Banks can significantly improve customer service levels by ensuring that the right people are in the right place at the right time. Installation of a large number of ATMs is not advisable due to high costs of installation. Thus, in view of the high initial cost on installation of an ATM, the banking industry agreed to pool their resources together by establishing ATM switches, by linking the respective ATM systems of banks through the switch. As a result of these agreements, the customers of a specific bank are able to carry out the transactions at the ATM of any other participating banks, enabling banks to get same or more business by lowering the number of ATM installation (Balachandher et al.). 4

5 Assuming, on an average 150 customers of a particular bank shares an ATM of some other banks, that means that the bank utilizing the other banks ATM is liable to pay Rs. 3,750/- per day or Rs. 13,68,750/- per year. On the other hand, if the bank had installed its own ATM, the cost would have been Rs. 15, 60,000/- per year which means a saving of Rs. 2.00/- Lacs per year. Just imagine that if the number of customers using other banks ATM is just 50 per day then the saving would be around Rs. 11 Lacs per year. So it could be seen that future is not towards mass deployment of ATMS but in ATM sharing. The feasibility of an ATM, at a given location, depends on the transactions carried out by the customers of the bank itself and the transactions carried out by the customers of network sharing banks. It is very difficult to study the problem analytical, as the transactions carried out by the customers are random in nature. If analysis is made analytically, it will be time consuming too, and will be very costly. Hence, the present study has been carried out using simulation process. In the present simulator, by taking into account the annual maintenance cost and one time installation cost, the income from an ATM at a given location is carried out on the basis of number of transactions carried out on the monthly basis. If the maintenance and one time cost is less than the income than the ATM is feasible at a given location, otherwise it will not be feasible to setup an ATM at a given location. The study aims to find out some sort of trend, using statistical forecasting techniques, for predicting the location of an ATM. Such forecasting is carried out simply by manipulating a time series data, has a 5

6 very serious disadvantage, because it does not tell the reason behind the movement of the data. An entirely different approach to forecasting is through seeking casual relationship. This essentially means that we first identify various deterministic factors that appear to determine the operating cost in the past; then build a model showing how these various factors produce the operating cost. Hence, the analysis of the present problem is carried out through the simulation as follow: IV. SIMULATOR //THIS IS A SIMULATOR TO ASSESS THE FEASIBILITY //OF AN ATM AT A GIVEN LOCATION. //THIS DEPENDS ON THE NO OF TRNSACTIONS //CARRIED OUT BY AN ATM. // NUMBER OF TRANSACTIONS CARRIED OUT IN A //DAY ARE RANDOM IN NATURE. import java.awt.*; import javax.swing.*; import javax.swing.event.*; import java.awt.event.*; import java.util.*; public class Simulator extends JFrame implements ActionListener Object data[][] = new Object[360][3]; 6

7 String ColumnNames[] = "Day","Trans through proprietor ATM ","Trans through non-proprietor ATM"; Container c = getcontentpane(); Panel p = new Panel(); JButton btnok = new JButton("Simulate"); JButton btncancel = new JButton("Cancel"); JLabel lbla = new JLabel("Income/Transaction thru same bank"); JLabel lblb = new JLabel("Income/Transaction thru N/W share"); JLabel lblmca = new JLabel("Maintainance Cost of ATM "); JLabel lblcostatm = new JLabel("Income through ATM"); JLabel lbldecision = new JLabel("Feasibiltiy"); JTextField txta = new JTextField("22"); JTextField txtb = new JTextField("50"); JTextField txtmca = new JTextField("400.00"); JTextField txtcostatm = new JTextField("0.00"); JTextField txtdecision = new JTextField(""); Simulator() super("simulator for feasibility of an ATM"); setsize(1000,800); p.setlayout(null); p.setbounds(0,0,800,600); lbla.setbounds(30,20,250,25); 7

8 txta.setbounds(290,20,70,25); lblb.setbounds(450,20,250,25); txtb.setbounds(700,20,70,25); lblmca.setbounds(30,50,180,25); txtmca.setbounds(220,50,70,25); lblcostatm.setbounds(30,480,200,25); txtcostatm.setbounds(240,480,150,25); lbldecision.setbounds(400,480,100,25); txtdecision.setbounds(510,480,200,25); btnok.setbounds(280,530,100,25); btncancel.setbounds(450,530,100,25); final JTable table = new JTable(data,ColumnNames); table.setpreferredscrollableviewportsize(new Dimension(200,100)); JScrollPane scrollpane = new JScrollPane(table); scrollpane.setbounds(20,170,750,300); p.add(lbla); p.add(txta); p.add(lblb); p.add(txtb); p.add(lblmca); p.add(txtmca); p.add(lblcostatm); p.add(txtcostatm); p.add(lbldecision); 8

9 p.add(txtdecision); p.add(btnok); p.add(btncancel); p.add(scrollpane); c.add(p); btnok.addactionlistener(this); btncancel.addactionlistener(this); public void actionperformed(actionevent ae) try String cmd = ae.getactioncommand(); Formatter fm=new Formatter(); if (cmd.equals("simulate")) Double MCA,A,B,FN,FNN=0.0; int X,Y,lamda1=200,lamda2=50; A=Double.parseDouble(txtA.getText()); B=Double.parseDouble(txtB.getText()); MCA=Double.parseDouble(txtMCA.getText()); for(int day=1;day<=360;day++) 9

10 X=poissonRandom(lamda1); //DEMAND B/W DAILY Y=poissonRandom(lamda2); //DEMAND B/W 0-50 DAILY FN=A*X+B*Y; FNN=FNN+FN; data[day-1][0]=day; fm=new Formatter(); data[day-1][1]=x; fm=new Formatter(); data[day-1][2]=y; double FNavg=(double)(FNN/360); String cca=double.tostring(fnavg); txtcostatm.settext(cca); if (FNavg>MCA) txtdecision.settext("atm is feasible"); else txtdecision.settext("atm is not feasible"); 10

11 if (cmd.equals("cancel")) this.dispose(); catch(exception e) JOptionPane.showMessageDialog(this,"Invalid Data"); public static void main(string arg[]) Simulator s = new Simulator(); s.setvisible(true); s.setdefaultcloseoperation(exit_on_close); int poissonrandom(float lamda) double z,rn,prod=1.0; int k=0; z=math.exp(-lamda); while(prod>=z) RN=Math.random(); 11

12 prod=prod*rn; k=k+1; return k; V. Results and Conclusions: The location forecasting simulator describes the process by which a bank can formulate future planning regarding installation of ATM. Installing an ATM at a location without using forecasting techniques, may cause loss of Business and goodwill to a bank. 12

13 (Fig: Output of the Simulator Showing Feasibility or non-feasibility) Hence, the forecasting of location of an ATM is crucial decision for a bank. The present simulator provided the methods for a bank to express its goals and priorities, to ensure the consistency and to increase the business. The forecasting approach presented in this study is forecasted the location of an ATM. Using the simulator the future demand, profit and expenditures for ATMs is forecasted. The decision of installation of an ATM will depend on the future demand of transactions. In the present simulator, pseudo random number generators were used to generate the number in the limit 0 to 200 for transactions demanded by proprietary ATM and 0 to 50 through non-proprietary ATM using poison distribution. The income from the transactions carried through an ATM at a given location by the customer holding an ATM of same bank and the customers holding an ATM of network sharing bank has been calculated. Then income from both of the group has been compared with the setting up cost and running cost of an ATM. If the income through an ATM at a given location is greater than the running cost then the ATM will be viable at a given location, otherwise, not. Hence, the present simulator provides the clear guidelines for a bank when the bank is looking for location to install an ATM. It enables the banking industry to take advantage of future demand, to formulate the policy for setting of an ATM. An ATM should generate efficiency and productivity for the bank in the most effective manner. Hence, present simulator is used to generate the demand of the customers and accordingly the location of an ATM and 13

14 the system has been studied for 180 days to remove any variation in the result. REFERENCES: 1. Banks Jerry, Carson-II John S., Barry L. Nelson, David M. Nicol, Discrete-Event System Simulation, Prentice Hall of India, Pvt. Limited, n. Delhi, Maria Anu, Introduction to Modeling and Simulation, Proceedings of the 1997 Winter Simulation Conference Pp Wyssusek, B., M. S. Chwartz, B. K. Remberg, F.Baier, H. Krallmann Business Process Modeling as an Element of Knowledge Management- A Model Theory Approach, Conference of Managing Knowledge: Conversations and Critiques. 4. Banks Jerry, Carson John S., Nelson Barry L., Nicol David M., Discrete-Event System Simulation, Prentice Hall Private Limited, New Delhi, Industry Insight, Indian Retail Banking. Cygnus Economics & Business Research, December Prasad Shri P. Shiva Rama, Cost Benefit Analysis of Automated Teller Machine, SBI Monthly Review November 2004, Prasad Shri P. Siva Rama, Cost Benefit Analysis of Automated Teller Machine, SBI monthly Review, November

15 8. Balachanddher Krishnan Guru, Santha Vaithilingam, Norhazlin Ismail, Rejendra Prasad: Electronic Banking in Malaysia: A note on evalution of services and consumer reactions. 9. Banks Jerry, Carson-II John S., Barry L. Nelson, David M. Nicol, Discrete-Event System Simulation, Prentice Hall of India, Pvt. Limited, new Delhi, Maria Anu, Introduction to Modeling and Simulation, Proceedings of the 1997, Winter Simulation Conference pp