Bluetooth based smart sensor network Essay

Currently, huge electronic info repositories atomic number 18 being maintained by banks and other financial institutions. Valuable bits of training be embedded in these entropy repositories. The huge size of these selective information sources make it im practical for a human analyst to come up with elicit nurture (or patterns) that depart befriend in the decision qualification process. A number of commercial enterprises give been quick to recognize the value of this concept, as a consequence of which the software grocery store itself for selective information mine is expected to be in excess of 10 billion USD. This paper is intended for those who would like to get aware of the realizable exertions of information tap to enhance the performance of some of their core business processes. In this paper discussion is some the broad areas of occupation, like jeopardize anxiety, portfolio management, trading, node compose and node care, where data mining techniques end be utilize in banks and other financial institutions to enhance their business performance. INTRODUCTIONAs acquaintance is graceful more and more synonymous to wealth creation and as a st gradegy plan for competing in the market place back toothnister be no better than the information on which it is found, the importance of knowledge and information in todays business can never be seen as an exogenous factor to the business. Organizations and individuals having access to the right information at the right moment, confound greater chances of being successful in the epoch of globalization and cut-throat competition. Business scholarship focuses on discovering knowledge from various electronic data repositories, both internal and external, to support better decision making. information mining techniques become classical for this knowledge discovery from databases. In recent years, business intelligencesystems have played pivotal roles in helping organizations to fine tun e the business goals such as improving node guardianship, market penetration, profitability and efficiency. In most teddys, these insights are driven by analyses of historical data. Global competitions, dynamic markets, and rapidly decreasing cycles of expert innovation provide important challenges for the banking and finance industry. Worldwide just-in- cartridge clip availability of information allows enterprises to repair their flexibility. In financial institutions considerable developments in information technology have led to huge demand for continuous analysis of resulting data.Data mining can contribute to solving business problems in banking and finance by purpose patterns, causalities, and correlations in business information and market determines that are not conterminously apparent to managers because the volume data is too large or is generated too apace to screen by experts. The managers of the banks may go a step further to stripping the sequences, episodes and periodicity of the transaction demeanour of their customers which may help them in very better segmenting, targeting, acquiring, retaining and maintaining a profitable customer base. Business Intelligence and data mining techniques can alike help them in directing various classes of customers and come up with a class found product and/or pricing approach that may garner better revenue management as well. The broad categories of application of Data Mining and Business Intelligence techniques in the banking and financial industry vertical may be viewed as followsRisk ManagementManaging and measurement of find is at the core of every financial institution. Todays study challenge in the banking and restitution world is therefore the implementation of risk management systems in order to identify, measure, and control business exposure. Here opinion and market risk present the central challenge, one can observe a major change in the area of how to measure and deal with them, establish on the advent of advanced database and data mining technology.( Other types of risk is also on tap(predicate)in the banking and finance i.e., liquidity risk, operational risk, or concentration risk. ) Today, integrated measurement of different kinds of risk (i.e., market and credit entry risk) is moving into focus. These all are based on fabrics representing single financial instruments or risk factors, their behaviour, and their interaction with overall market, making this field passing important topic of research. Financial trade RiskFor single financial instruments, that is, stock indices, interest rates, or currencies, market risk measurement is based on models depending on a set of fundamental risk factor, such as interest rates, stock indices, or economic development. One is interested in a functional form between instrument price or risk and underlying risk factors as well as in functional dependency of the risk factors itself. Today different market risk me asurement approaches exist. All of them rely on models representing single instrument, their behaviour and interaction with overall market. Many of this can only be built by using various data mining techniques on the proprietary portfolio data, since data is not publicly visible(prenominal) and needs consistent supervision. identification RiskCredit risk assessment is key component in the process of commercial channeling. Without it the lender would be unable to make an objective judgement of weather to lend to the surveyive borrower, or if how much charge for the loan. Credit risk management can be classified into two basic groupsCredit scoring/credit rating Assignment of a customer or a product to risk level. (i.e., credit approval) Behaviour scoring/credit rating migration analysis. Valuation of a customers or products probability of a change in risk level within a given time. (i.e., omission rate volatility) In commercial impart, risk assessment is usually an attempt to q uantify the risk of loss to the lender when making a particular lending decision. Here credit risk can quantify by the changes of value of a credit product or of a whole credit customer portfolio, which is based on change in the instruments ranting, the inadvertence probability, and retrieval rate of the instrument in case of default. Further diversification make influence the result on a portfolio level. Thus a major part of implementation and care ofcredit risk management system entrust be a typical data mining problem the modelling of the credit instruments value through the default probabilities, rating migrations, and recovery rates. Three major approaches exist to model credit risk on the transaction level accounting analytic approaches, statistical prediction and option conjectural approaches. Since large amount of information about client exist in financial business, an adequate way to build such models is to use their own database and data mining techniques, fitting mod els to the business needs and the business current credit portfolio.Portfolio ManagementRisk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected return or price of a financial instrument. Both make it possible to manage firm wide portfolio actively in a risk/return efficient manner. The application of modern risk theory is therefore within portfolio theory, an important part of portfolio management. With the data mining and optimization techniques investors are able to allocate capital crosswise trading activities to maximise profit or minimise risk. This feature supports the ability to generate trade recommendations and portfolio structuring from user supplied profit and risk requirement. With data mining techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset prices or ret urns and the risk involved. With this functionality, what if simulations of varying market conditions e.g. interest rate and exchange rate changes) cab be run to assess impact on the value and/or risk associated with portfolio, business unit counterparty, or trading desk. Various scenario results can be regarded by considering actual market conditions. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub portfolio can be benchmarked against common international benchmarks. trafficFor the last few years a major topic of research has been the building of quantitative trading tools using data mining methods based on prehistoric data asinput to predict short-term movements of important currencies, interest rates, or equities. The goal of this technique is to spot times when markets are meretricious or expensive by identifying the factor that are important in determining market returns. The trading system examines the relationship between relevant information and piece of financial assets, and gives you buy or sell recommendations when they suspect an under or overvaluation. Thus, even if some traders find the data mining approach too mechanical or too risky to be used consistently, they may want to use it selectively as further opinion. Trading is based on the idea of predicting short term movements in the price/value of a product (currency/equity/interest rate etc.). With a reasonable guesstimate in place one may trade the product if he/she thinks it is going to be overvalued or undervalued in the coming future(a). Trading traditionally is done based on the instinct of the trader. If he/she thinks the product is not priced properly he/she may sell/buy it. This instinct is usually based on past experience and some analysis based on market conditions.However, the number of factors that even the most expert of traders can account for are limited. Hence, quite a lot these predictions fail. The price of a financial as set is influenced by a variety of factors which can be broadly classified as economic, political and market factors. Participants in a market observe the relation between these factors and the price of an asset, account for the current value of these factors and predict the future determine to finally arrive at the future value of the asset and trade accordingly. Quite often by the time a trained eye detects these favourable factors, many a(prenominal) others may have discovered the opportunity, decreasing the possible revenues otherwise. Also these factors in turn may be related to several other factors making prediction difficult. Data mining techniques are used to discover hidden knowledge, unknown patterns and new rules from large data sets, which may be useful for a variety of decision making activity.With the increasing economic globalization and improvements in information technology, large amounts of financial data are being generated and stored. subjected to data mining t echniques to discover hidden patterns and mother predictions for trends in the future and the behaviour of the financial markets. With the immediacy offered by data mining, latest data can be mined to obtain crucial information at the earliest. This in turn would result in an improved market placeresponsiveness and awareness leading to reduced costs and increased revenue. Advancements make in technology have enabled to create faster and better prediction systems. These systems are based on a combination of data mining techniques and artificial intelligence methods like Case Based Reasoning (CBR) and Neural Networks (NN). A combination of such a forecasting system together with a rock-steady trading strategy offers tremendous opportunities for massive returns. The value of a financial asset is dependent on both macroeconomic and microeconomic variables and this data is available in a variety of disparate formats. NN and CBR techniques can be applied extensively for predicting thes e financial variables. NN are characterized by learning capabilities and the ability to improve performance over time. Also NN can generalize i.e. recognize new objects which may be similar but not exactly identical to prior objects.NN with their ability to derive meaning from imprecise data can be used to detect patterns which are otherwise too complex to be find by humans. NN act as experts in the area that they have been trained to work in. these can be used to provide predictions for new situations and work in authentic time. Thus, historic data available about financial markets and the various variables can be used to train NN to simulate the market. CBR methodology is based on reasoning from past performances. It uses a large repository of data stored as cases which would include all the market variables in this case. When a new case is fed in (in the form of a case containing the concerned variables), the CBR algorithm predicts the performance/result of this case based on the cases it has in its repository. Data mining techniques can be used to detect hidden patterns in these cases which may then be used for further decision making. CBR methods can be used in real time which makes analysis really quick and helps in real time decision making resulting in immediate profits. Thus data mining and business intelligence (CBR and NN) techniques may be used in conjunction in financial markets to predict market behaviour and obtain patterned behaviour to influence decision making. client Profiling and Customer Relationship ManagementBanks have many and huge databases containing transactional and other lucubrate of its customers. Valuable business information can be extracted from these data stores. But it is unfeasible to support analysis and decision makingusing traditional interrogation languages because human analysis breaks down with volume and dimensionality. Traditional statistical methods do not have the capacity and scale to analyse these data, and hence modern data mining methodologies and tools are increasingly being used for decision making process not only in banking and financial institutions, but across the industries. Customer profiling is a data mining process that builds customer profiles of different groups from the companys existing customer database. The information obtained from this process can be used for different purposes, such as understanding business performance, making new marketing initiatives, market segmentation, risk analysis and revising company customer policies. The advantage of data mining is that it can handle large amounts of data and learn inherent structures and patterns in data. It can generate rules and models that are useful in enabling decisions that can be applied to future cases. Customer Behaviour Modeling (CBM) or customer profiling is a tool to predict the future value of an individual and the risk category to which he belongs to based on his demographic characteristics, life-style and previous behaviour. This helps to focus on customer retention. The two important facts that have important implication in selecting customer profiling methods are Profiling information can consist of many variables (or dozens of them). Majority of them are categorical variables (or non-numeric variables or nominal variables).Customer profiling is to characterize features of special customer groups. Many data mining techniques search profiles of special customer groups systematically using Artificial Intelligence techniques. They generate accurate profiles based on beam search and incremental learning techniques. Customer profiling also uses many predictive modeling methods. Predictive modelling techniques applicable can be categorized into two broad approaches. They depend on the type of predicted information or variables, also called target variables. If the type of predicted values is categorical, classification techniques is preferred to be used. Classification MethodsIn this approach, risk levels are organized into two categories based on past default history. For example, customers with past default history can beclassified into risky group, whereas the rest are placed as safe group. utilise this categorization information as target of prediction, Decision Tree and Rule Induction techniques can be used to build models that can predict default risk levels of new loan applications. Value Prediction MethodsIn this method, for example, instead of classifying new loan applications, it attempts to predict expected default amounts for new loan applications. The predicted values are numeric and thus it requires modelling techniques that can take numerical data as target (or predicted) variables. Neural Network and regression are used for this purpose. The most common data mining methods used for customer profiling are Clustering (descriptive) Classification (predictive) and regression (predictive) Association rule discovery (descriptive) and consequent pat tern discovery (predictive)In CRM, data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will behave in a particular way. For example, a score could measure the propensity to respond to a particular insurance or credit card offer or to switch to a competitors product. Data mining can be useful in all the three phases of a customer relationship-cycle customer acquisition, increasing value of the customer and customer retention. For example, a typical banking firm let say sends 1 million direct mails for credit card customer acquisition. Past researches have shown that typically 6% of such target customers respond to these direct mails. Banks use their credit risk models to classify these respondents in good credit risk and bad credit risk classes. The proportion of good credit risk respondents is only 16% out of the total respondents. So, as net result, roughly only 1% of the total targeted customers are co nverted into the credit card customers through direct mailing. seeing the huge cost and effort involved in such marketing process, data mining techniques can significantly improve the customer conversion rate by more focused marketing. Using a predictive test model using decision tree techniques like CHAID (Chi-squared Automatic Interaction Detection), drop behind (Classification And Regression Trees), Quest and C5.0 it can beanalyzed which customers are more probable to respond. And using this with the risk model using techniques like neural net can help build a test model. The way data mining can actually be built into the CRM application is determined by the nature of customer interaction. The customer interaction could be inbound (when the customer contacts the firm) or outbound (when the firm contacts customers). The deployment requirements are quite different. Outbound interactions such as direct Building Profitable Customer Relations with Data Mining, Herb Edelstein mail ca mpaign involve the firm selecting the people whom to be mailed by applying the test model to the customer database. In other outbound campaigns like advertising, the profile of good prospects shown by the test model needs to be matched to the profile of the people the advertisement would reach. For inbound transactions such as telephone or internet order, the application must respond in real time. Therefore the data mining model is embedded in the application and actively recommends an action. In either case, one of the key issues in applying a model to new data set is the transformations that are made in building the model. The ease with which these changes are embedded in the model determines the productivity of deploying these tools. Marketing and customer careBecause high competitions in the finance industry, intelligent business decisions in marketing are more important than ever for better customer targeting, acquisition, retention and customer relationship. There is a need fo r customer care and marketing strategies to be in place for the success and survival of the business. It is possible with the help of data mining and predictive analytics to make such strategies. Financial institutions are finding it more difficult to locate new previously unsolicited buyers, and as a result they are implementing aggressive marketing program to acquire new customer from their competitors. The uncertainties of the buyer make planning of new services and media usage nigh impossible. The classical solution is to apply subjective human expert knowledge as rules of thumb. Until recently, replacing the human expert by computer technology has been difficult.An interesting tool available in marketing and financial institution is analysis of clients data. This allows analysis and calculation of key indicators that help bank to identify factors that affected customers demand in the past and customer need in the future. Information about the customers personal data can also g ive indications that affect future demand. In case of analysis of retail debtors and small corporations, marketing tasks will typically include factors about the customer himself, his credit record and rating made by external rating agencies. With the advent of data mining and business intelligence tools it has become possible for banks to strengthen their customer acquisition by direct marketing and establish multi- channel contacts, to improve customer development by cross selling and up selling of products, and to increase customer retention by behaviour management.It is possible for the banks to use the data available to retain its best customers and to identify opportunities to sell them supernumerary services. The profiling of all the valuable accounts can be done and the top most say 5-10 % can be assigned to Relationship Managers, whose job will be to identify new selling opportunities with these customers. It is also possible to bundle various offers to meet the need of th e valued customers. Data mining can also help the banks in customizing the various promotional offers. For example the direct mails can be customized as per the segment of the account holders in the bank. It is also possible for the banks to find out thepr oblem customers who can be defaulters in the future, from their past payment records and the profile and the data patterns that are available. This can also help the banks in adjusting the relationship with these customers so that the loss in future is kept to its minimum.Data mining can improve the response rates in the direct mail campaigns as the time required to classify the customers will be reduced, this in turn will increase the revenues, improve the sales force efficiency from the target group. Data mining helps the banks to optimize their portfolio of services, delivery channels. A record of past transactions can give useful insight to the bank and different locations /branches of same branch can also follow some patterns that when noticed can be used as past records to learn from and base the future actions upon.Data Mining techniques can be of immense help to the banks and financialinstitutions in this arena for better targeting and acquiring new customers, fraud detection in real time, providing segment based products for better targeting the customers, analysis of the customers purchase patterns over time for better retention and relationship, detection of emerging trends to take proactive stance in a highly competitive market adding a lot more value to existing products and services and launching of new product and service bundles. Reference