Description
This is document about Decision Support System.
Title:
Price-Strat: A Decision Support System for Determining Building Society and Bank Interest Rate Mixes
Author(s): Madan G. Singh, Rod Cook Journal: International Journal of Bank Marketing The important distinction that is being made here is between the operational DecisionMaking Level (II) where the time horizon of interestis relatively small (a week to, say, three months) and the Strategic Level(I) where the horizon of interest is much longer (up to several years).
At the operational level, responses to moves by competitors are primarily on interest rates for savers and mortgage lending where one can observe competitor moves (e.g., introduction of a new savings product by a competitor which effectively increases his weighted average interest rate) and decide whether to respond or not depending on one's prediction of what impact the competitors' moves will have on one's own market share.
At the Strategic Level, the decisions one is taking have a longer-term impact (e.g., shift from wholesale funds to retail savings for banks which involves investment in the improvement of the branch network and the introduction of new services which will only affect the operational problems over a certain period of time). The Price-Strat system that will be described is primarily of interest at the operational decision-making level, although it may also be used to examine some of the strategic issues. Another aspect about the hierarchy in is that, if one examines the data on the basis of which the decisions are being made, then one could argue that the higher one goes up the hierarchy, the morei mportant becomes the
proportion of managerial intuition versus the "hard" data. Similarly, the data get more aggregated as one goes up the hierarchy.
There is a clear need for developing decision support systems to help senior managers to take both operational and strategic decisions where one can combine both hard and soft data and then utilise optimisation in order to compute the best decisions within any defined set of strategic assumptions.
The Price-Strat system does this. It comprises a knowledge base into which one can feed both "hard" data (about the past) and "softer" data (obtained perhaps from market research). In addition, a knowledge acquisition mechanism can be used to elicit the intuition and "gut feel" of managers through their responses to a series of "what-if" scenarios.
The resulting knowledge base can be parameterised for a particular situation and an optimisation-based inference mechanism can compute the optimal decisions within a given set of constraining assumptions. One can also test the impact on the solution of changing the assumptions.
The Price-Strat System The Price-Strat system can be used to tackle four key problems facing a bulding society or a bank at the operational decision making level. (a) On the savings side, given a budgeted target of bringing in a specified amount of money over a specified short period of time (say one-three months) from the retail savings market, what is the cheapest way of doing so within a competitive environment by varying one's interest rate structure? (b) Again on the savings side, given the current weighted average interest rates (or some key interest rate applicable to a specific market segment) of a number of named competitors, how would the shares of net inflows of funds change if any of the competitors were to change their rates? (c) Similarly, on the mortgage (or other lending) side, there are two similar problems, i.e., given a budgeted target of lending out a specified amount of money over a specified period of time (say, one-three months) between the different types of mortgages (repayment, endowment, commercial, etc,) or any other lending sectors (e.g., personal, corporate for
small, medium or large companies, etc,), what is the most expensive way of achieving it in a highly competitive environment? (d) Given the current weighted average mortage interest rate (or some other key interest rate) of a number of named competitors, how would the market shares of each of the competitors vary if the bank/building society or any of its competitors varied their mortgage rates? In order to respond to the above four questions, the Price-Strat system allows the user to parameterise a knowledge base for each application. Essentially, in the case of (a) above, a model is constructed which relates the rates offered by a building society or a bank on its savings products to the net amount of money which flows in over the specified period of time. This model is then linked into an optimiser into which one can feed the diffferent transactions or other costs for each of the accounts, as well as the fixed costs, and also the net money inflow target, and it then calculates the optimal interest rate mix which should bring the inflow required in the cheapest possible way. Similarly, for (c), a predictive model for the mortgage market replaces the savings market model. For (b) and (d), models can be constructed using Price-Strat. These models are based on the existence of different direct and cross elasticities for each product/each competitor in each of the markets depending upon image, size of branch networks and a host of other factors.
Parameterisation of Price-Strat In order to construct the models shown in Figures 2-5, it is necessary to compute the various direct and cross-elasticities for any particular situation. This can be done in a variety of ways, i.e: (a) through market research studies; (b) through the analysis of historical data; (c) through the elicitation of managerial perceptions, particularly about competitors, by asking one or multiple managers to respond to a series of "what-if" scenarios.It should be pointed out that market research, though valuable, is often quite expensive, whilst historical data may not be easily available and/or might be difficult to interpret. The latter is particularly so when one needs time series about historical moves by competitors but where their balance sheets only provide partial information. In addition, even for one's own product mix, sufficiently long time series may not be available if one has changed the nature of some of one's own products over time. Also, given the regulated nature of the UK financial services industry up to the recent past, the information content of the historical data might well be somewhat limited. Managerial intuition ((c) above), on the other hand, has the significant
merit that it allows one to feed in a whole lot of factors which managers perceive will have an impact in the future but which are not easy to pick out using an analysis of historical data. It should be emphasised that the Price-Strat system is able to accept market research data and/or historical data and/or managerial intuition and allows the user to construct models based on any combination of the above.
Transforming GE Real Estate with Innovative Data-driven Decision Support
by D. J. Power GE Real Estate, a unit of GE Commercial Finance, is one of the world's leading commercial real estate investors and lenders. Beginning in 2000, when Michael Pralle joined the firm as President and Chief Executive Officer and Hank Zupnick became Chief Information Officer, computerized decision support has been a major strategy enabler. Pralle has transformed GE Real Estate from a U.S. focused firm into one of the world’s largest and most diversified commercial real estate investment firms. The company does business in 20 countries and has a portfolio valued at more than USD $48 billion. In 2006, GE Real Estate contributed 8 percent of GE’s total net income. Compared to 2000, net income more than tripled to USD $1.8 billion. GE Real Estate's performance is attributed to the three pronged growth strategy that Pralle implemented: "manage the business through real estate cycles; establish a deeper presence in large, underserved countries, including Mexico and in Eastern Europe; and maximize opportunities in emerging markets, particularly India and China." To make this strategy work GE Real Estate needed to expand its use of the World Wide Web, improve computerized transaction processes and implement innovative decision support capabilities. According to the GE Real Estate web site (www.gerealestate.com), Pralle "has used the power of technology to improve service quality in what is essentially a face-to-face business. GE Real Estate has spent $50 million on information technology since 2002. Those investments have reduced transaction cycle times by 65 percent and improved the accuracy and reliability of Real Estate deals."
The company has also used computerized decision support to improve its risk management practices. Non-performing assets were at an industry-low of 0.55 percent in 2005. Pralle "believes this risk management discipline will be a critical competitive advantage as the real estate business expands into emerging markets characterized by differing legal systems, currency regulations and concepts of property rights."
Decision Support development at GE Real Estate As Chief Information Officer of GE Real Estate, Hank Zupnick’s challenge was to use information technology to help drive global growth. When Zupnick joined the company in 2000, he and his staff began to identify where technology could help its real estate professionals become more productive and make investment decisions more effectively. Another important application for technology was to help the firm better manage transaction risks due to incomplete or inaccurate information. Hank oversees a team of professionals located in North America, Europe and Asia, and continues to apply information technology strategically to help GE Real Estate operate more efficiently, responsively and profitably. Since his arrival at GE Real Estate, Hank has overseen the multimillion dollar investments in technological improvements. His team has implemented a number of projects that have yielded quantifiable improvements throughout the business. By automating deal origination, approval and closing processes, the transaction cycle has been shortened dramatically and the company now responds faster to client requests. With other technology innovations, IT projects have made deal information more detailed and reliable, while the current status of each deal in progress worldwide is available from a Web portal. Decision support improvements have also strengthened GE Real Estate’s controllership capabilities by implementing worldwide financial control systems. Perhaps most important, Zupnick initiated and led projects to create business intelligence databases that enable the company to more accurately assess marketplace conditions and manage portfolio risks. In an article at CIO.com, Zupnick explained how the CFO at GE Real Estate offered to fund a businesswide data warehouse that would help us grow and manage our global commercial real estate business, instead he chose to start with low-cost applications and built a data mart. GE Real Estate was pricing between 20,000 and 30,000 deals a year. Much of the information
GE Real Estate's managers relied on was stored in spreadsheets or in hard-copy reports. Figuring out how GE's loans were performing in each particular market and what kind of risks should be factored into a $30 million deal required employees to gather data manually from several sources. "Errors are unavoidable in a manual process, and they could be costly: Misunderstanding GE's loan-portfolio performance in Denver or Dublin could mean charging a customer too low an interest rate and exposing GE to too much risk. Charging too high a rate could prompt the customer to take a lower rate from another lender." "We decided that a data warehouse and Web-based reporting system would be the ideal solution. But we also knew it could be very expensive and time-consuming to develop, and the process is full of uncertainties." GE’s theory of technological evolution has entailed the automation of 600-plus individual transactional and servicing-related processes, which is more than a third of the company’s processes.
References: http://dssresources.com/ http://www.emeraldinsight.com/Insight/viewPDF.jsp?contentType=Article&Filename=html/ Output/Published/EmeraldFullTextArticle/Pdf/0320040405.pdf
doc_710404924.docx
This is document about Decision Support System.
Title:
Price-Strat: A Decision Support System for Determining Building Society and Bank Interest Rate Mixes
Author(s): Madan G. Singh, Rod Cook Journal: International Journal of Bank Marketing The important distinction that is being made here is between the operational DecisionMaking Level (II) where the time horizon of interestis relatively small (a week to, say, three months) and the Strategic Level(I) where the horizon of interest is much longer (up to several years).
At the operational level, responses to moves by competitors are primarily on interest rates for savers and mortgage lending where one can observe competitor moves (e.g., introduction of a new savings product by a competitor which effectively increases his weighted average interest rate) and decide whether to respond or not depending on one's prediction of what impact the competitors' moves will have on one's own market share.
At the Strategic Level, the decisions one is taking have a longer-term impact (e.g., shift from wholesale funds to retail savings for banks which involves investment in the improvement of the branch network and the introduction of new services which will only affect the operational problems over a certain period of time). The Price-Strat system that will be described is primarily of interest at the operational decision-making level, although it may also be used to examine some of the strategic issues. Another aspect about the hierarchy in is that, if one examines the data on the basis of which the decisions are being made, then one could argue that the higher one goes up the hierarchy, the morei mportant becomes the
proportion of managerial intuition versus the "hard" data. Similarly, the data get more aggregated as one goes up the hierarchy.
There is a clear need for developing decision support systems to help senior managers to take both operational and strategic decisions where one can combine both hard and soft data and then utilise optimisation in order to compute the best decisions within any defined set of strategic assumptions.
The Price-Strat system does this. It comprises a knowledge base into which one can feed both "hard" data (about the past) and "softer" data (obtained perhaps from market research). In addition, a knowledge acquisition mechanism can be used to elicit the intuition and "gut feel" of managers through their responses to a series of "what-if" scenarios.
The resulting knowledge base can be parameterised for a particular situation and an optimisation-based inference mechanism can compute the optimal decisions within a given set of constraining assumptions. One can also test the impact on the solution of changing the assumptions.
The Price-Strat System The Price-Strat system can be used to tackle four key problems facing a bulding society or a bank at the operational decision making level. (a) On the savings side, given a budgeted target of bringing in a specified amount of money over a specified short period of time (say one-three months) from the retail savings market, what is the cheapest way of doing so within a competitive environment by varying one's interest rate structure? (b) Again on the savings side, given the current weighted average interest rates (or some key interest rate applicable to a specific market segment) of a number of named competitors, how would the shares of net inflows of funds change if any of the competitors were to change their rates? (c) Similarly, on the mortgage (or other lending) side, there are two similar problems, i.e., given a budgeted target of lending out a specified amount of money over a specified period of time (say, one-three months) between the different types of mortgages (repayment, endowment, commercial, etc,) or any other lending sectors (e.g., personal, corporate for
small, medium or large companies, etc,), what is the most expensive way of achieving it in a highly competitive environment? (d) Given the current weighted average mortage interest rate (or some other key interest rate) of a number of named competitors, how would the market shares of each of the competitors vary if the bank/building society or any of its competitors varied their mortgage rates? In order to respond to the above four questions, the Price-Strat system allows the user to parameterise a knowledge base for each application. Essentially, in the case of (a) above, a model is constructed which relates the rates offered by a building society or a bank on its savings products to the net amount of money which flows in over the specified period of time. This model is then linked into an optimiser into which one can feed the diffferent transactions or other costs for each of the accounts, as well as the fixed costs, and also the net money inflow target, and it then calculates the optimal interest rate mix which should bring the inflow required in the cheapest possible way. Similarly, for (c), a predictive model for the mortgage market replaces the savings market model. For (b) and (d), models can be constructed using Price-Strat. These models are based on the existence of different direct and cross elasticities for each product/each competitor in each of the markets depending upon image, size of branch networks and a host of other factors.
Parameterisation of Price-Strat In order to construct the models shown in Figures 2-5, it is necessary to compute the various direct and cross-elasticities for any particular situation. This can be done in a variety of ways, i.e: (a) through market research studies; (b) through the analysis of historical data; (c) through the elicitation of managerial perceptions, particularly about competitors, by asking one or multiple managers to respond to a series of "what-if" scenarios.It should be pointed out that market research, though valuable, is often quite expensive, whilst historical data may not be easily available and/or might be difficult to interpret. The latter is particularly so when one needs time series about historical moves by competitors but where their balance sheets only provide partial information. In addition, even for one's own product mix, sufficiently long time series may not be available if one has changed the nature of some of one's own products over time. Also, given the regulated nature of the UK financial services industry up to the recent past, the information content of the historical data might well be somewhat limited. Managerial intuition ((c) above), on the other hand, has the significant
merit that it allows one to feed in a whole lot of factors which managers perceive will have an impact in the future but which are not easy to pick out using an analysis of historical data. It should be emphasised that the Price-Strat system is able to accept market research data and/or historical data and/or managerial intuition and allows the user to construct models based on any combination of the above.
Transforming GE Real Estate with Innovative Data-driven Decision Support
by D. J. Power GE Real Estate, a unit of GE Commercial Finance, is one of the world's leading commercial real estate investors and lenders. Beginning in 2000, when Michael Pralle joined the firm as President and Chief Executive Officer and Hank Zupnick became Chief Information Officer, computerized decision support has been a major strategy enabler. Pralle has transformed GE Real Estate from a U.S. focused firm into one of the world’s largest and most diversified commercial real estate investment firms. The company does business in 20 countries and has a portfolio valued at more than USD $48 billion. In 2006, GE Real Estate contributed 8 percent of GE’s total net income. Compared to 2000, net income more than tripled to USD $1.8 billion. GE Real Estate's performance is attributed to the three pronged growth strategy that Pralle implemented: "manage the business through real estate cycles; establish a deeper presence in large, underserved countries, including Mexico and in Eastern Europe; and maximize opportunities in emerging markets, particularly India and China." To make this strategy work GE Real Estate needed to expand its use of the World Wide Web, improve computerized transaction processes and implement innovative decision support capabilities. According to the GE Real Estate web site (www.gerealestate.com), Pralle "has used the power of technology to improve service quality in what is essentially a face-to-face business. GE Real Estate has spent $50 million on information technology since 2002. Those investments have reduced transaction cycle times by 65 percent and improved the accuracy and reliability of Real Estate deals."
The company has also used computerized decision support to improve its risk management practices. Non-performing assets were at an industry-low of 0.55 percent in 2005. Pralle "believes this risk management discipline will be a critical competitive advantage as the real estate business expands into emerging markets characterized by differing legal systems, currency regulations and concepts of property rights."
Decision Support development at GE Real Estate As Chief Information Officer of GE Real Estate, Hank Zupnick’s challenge was to use information technology to help drive global growth. When Zupnick joined the company in 2000, he and his staff began to identify where technology could help its real estate professionals become more productive and make investment decisions more effectively. Another important application for technology was to help the firm better manage transaction risks due to incomplete or inaccurate information. Hank oversees a team of professionals located in North America, Europe and Asia, and continues to apply information technology strategically to help GE Real Estate operate more efficiently, responsively and profitably. Since his arrival at GE Real Estate, Hank has overseen the multimillion dollar investments in technological improvements. His team has implemented a number of projects that have yielded quantifiable improvements throughout the business. By automating deal origination, approval and closing processes, the transaction cycle has been shortened dramatically and the company now responds faster to client requests. With other technology innovations, IT projects have made deal information more detailed and reliable, while the current status of each deal in progress worldwide is available from a Web portal. Decision support improvements have also strengthened GE Real Estate’s controllership capabilities by implementing worldwide financial control systems. Perhaps most important, Zupnick initiated and led projects to create business intelligence databases that enable the company to more accurately assess marketplace conditions and manage portfolio risks. In an article at CIO.com, Zupnick explained how the CFO at GE Real Estate offered to fund a businesswide data warehouse that would help us grow and manage our global commercial real estate business, instead he chose to start with low-cost applications and built a data mart. GE Real Estate was pricing between 20,000 and 30,000 deals a year. Much of the information
GE Real Estate's managers relied on was stored in spreadsheets or in hard-copy reports. Figuring out how GE's loans were performing in each particular market and what kind of risks should be factored into a $30 million deal required employees to gather data manually from several sources. "Errors are unavoidable in a manual process, and they could be costly: Misunderstanding GE's loan-portfolio performance in Denver or Dublin could mean charging a customer too low an interest rate and exposing GE to too much risk. Charging too high a rate could prompt the customer to take a lower rate from another lender." "We decided that a data warehouse and Web-based reporting system would be the ideal solution. But we also knew it could be very expensive and time-consuming to develop, and the process is full of uncertainties." GE’s theory of technological evolution has entailed the automation of 600-plus individual transactional and servicing-related processes, which is more than a third of the company’s processes.
References: http://dssresources.com/ http://www.emeraldinsight.com/Insight/viewPDF.jsp?contentType=Article&Filename=html/ Output/Published/EmeraldFullTextArticle/Pdf/0320040405.pdf
doc_710404924.docx