Bluetooth based smart sensor network Essay

Currently, big electronic info repositories argon world maintained by banks and other m maventary institutions. worthy bits of selective in approach patternation be embedded in these learning repositories. The huge size of these info sources make it im likely for a human analyst to come up with interesting information (or patterns) that exit avail in the ratiocination reservation touch on. A number of commercial enterprises pull in been immobile to recognize the order of this concept, as a consequence of which the softwargon grocery itself for information exploit is expected to be in excess of 10 billion USD. This paper is intended for those who would like to get aw atomic number 18 of the viable drills of info digging to enhance the performance of some of their core descent processes. In this paper discussion is close the enormous welkins of application, like fortune management, portfolio management, vocation, client profiling and client cope, whe re info excavation techniques discharge be utilised in banks and other monetary institutions to enhance their job performance. penetrationAs experience is becoming more and more synonymous to riches creation and as a strategy plan for competing in the commercialize place screwing be no advance than the information on which it is found, the importance of knowledge and information in todays clientele groundwork never be seen as an exogenous f movementor to the furrow. Organizations and individuals having feeler to the right information at the right moment, have greater chances of being successful in the epoch of globalization and cut-throat competition. Business Intelligence foc habituates on discovering knowledge from various electronic data repositories, both internal and remote, to put forward better end making. entropy archeological site techniques sound of the essence(p) for this knowledge discovery from data plants. In recent years, railway line intellige ncesystems have vie pivotal roles in dishing organizations to fine tune the business ends much(prenominal) as improving customer store, securities industry penetration, profitability and efficiency. In virtu everyy cases, these insights are driven by analyses of historical data. Global competitions, dynamic market places, and promptly decreasing cycles of technological innovation provide important challenges for the banking and finance industry. ecumenic just-in- eon availability of information wills enterprises to improve their flexibility. In financial institutions considerable developments in information technology have led to huge demand for regular epitome of guideing data.Data dig washbowl contri savee to solving business problems in banking and finance by finding patterns, causalities, and correlations in business information and market prices that are not immediately apparent to managers because the volume data is in like manner large or is begind too quick ly to screen by clevers. The managers of the banks whitethorn go a step just to find the sequences, episodes and periodicity of the transaction behavior of their customers which whitethorn help them in actually better segmenting, objectiveing, acquiring, retaining and maintaining a profitable customer base. Business Intelligence and data minelaying techniques domiciliate also help them in identifying various classes of customers and come up with a class found crossroad and/or pricing prelude 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 oversightManaging and eyeshadement of guessiness is at the core of every financial institution. immediatelys major challenge in the banking and insurance world is whence the death penalty of bump management systems in order to identify, measure, and control business exposure. Here acknowledgment and market put on the line present the central challenge, one asshole observe a major transmute in the area of how to measure and deal with them, found on the advent of advanced database and data mining technology.( Other slips of s require is also on tap(predicate)in the banking and finance i.e., liquidity pretend, operational bump, or concentration risk. ) Today, integrated measurement of diverse kinds of risk (i.e., market and point of reference risk) is moving into focus. These all are based on models representing single financial legal documents or risk factors, their behaviour, and their interaction with general market, making this field highly important topic of research. Financial market place RiskFor single financial instruments, that is, stock indices, interest rates, or currencies, market risk measurement is based on models awaiting on a good deal of underlying risk factor, much(prenominal) as interest rates, stock indices, o r 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 diametrical market risk measurement approaches out(p)last. All of them intrust on models representing single instrument, their behaviour and interaction with overall market. Many of this chamberpot only be built by exploitation various data mining techniques on the proprietary portfolio data, since data is not publicly available and take aways consistent supervision. Credit RiskCredit risk sound judgment is key component in the process of commercial lending. Without it the lender would be unable to make an objective judgement of weather to lend to the potential borrower, or if how much charge for the surpassow. Credit risk management hind end be classified into two basic groupsCredit scoring/ acknowledgement rating Assignment of a customer or a convergence to risk level. (i.e., c redit approval) Behaviour scoring/credit rating migration analytic thinking. paygrade of a customers or outputs prospect of a change in risk level within a given time. (i.e., inadvertence rate volatility) In commercial lending, risk sagacity is usually an attempt to quantify the risk of loss to the lender when making a particular lending decision. Here credit risk seat quantify by the changes of note value of a credit product or of a whole credit customer portfolio, which is based on change in the instruments ranting, the negligence probability, and convalescence rate of the instrument in case of default. Further diversification effects influence the result on a portfolio level. Thus a major part of implementation and care ofcredit risk management system will be a typical data mining problem the simulation of the credit instruments value through the default probabilities, rating migrations, and recovery rates. Three major approaches exist to model credit risk on the trans action level accounting analytic approaches, statistical forecasting and preference theoretic 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 models to the business needs and the business present-day(prenominal) credit portfolio.Portfolio ManagementRisk measurement approaches on an aggregate portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an instauration of the expected re playing period or price of a financial instrument. some(prenominal) make it possible to manage firm wide portfolio actively in a risk/re morsel 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 acro ss 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 compendium capabilities concerning expected asset prices or returns and the risk confused. 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 hobo be regarded by considering actual market conditions. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub portfolio croupe be benchmarked against common international benchmarks. businessFor the last few years a major topic of research has been the building of quantitative trading tools exploitation data mining methods based on ultimo 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 cheap or high-priced by identifying the factor that are important in determining market returns. The trading system examines the relationship between relevant information and spell 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 crazy to be use systematically, 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 level-headed guesstimate in place one may trade the product if he/she thinks it is going to be overvalued or undervalued in the coming future. Trading traditionally is done based on the disposition of t he 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 summary based on market conditions.However, the number of factors that even the most technical of traders shtup account for are limited. Hence, quite often these predictions fail. The price of a financial asset is influenced by a variety of factors which mint 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 values to finally arrive at the future value of the asset and trade accordingly. Quite often by the time a trained eye observes these favourable factors, many 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 t echniques are used to discover hidden knowledge, hidden patterns and hot 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 techniques to discover hidden patterns and obtain predictions for trends in the future and the behaviour of the financial markets. With the immediacy offered by data mining, la mental test data can be mined to obtain crucial information at the earliest. This in turn would result in an improved market place reactivity and awareness leading to reduced costs and increased revenue. Advancements made in technology have enabled to create faster and better prediction systems. These systems are based on a combination of data mining techniques and colored intelligence methods like Case Based Reasoning ( cosmic microwave background) and Neural Networks (NN) . A combination of such a forecasting system together with a good 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 these financial variables. NN are think ofd by learning capabilities and the ability to improve performance over time. Also NN can generalize i.e. recognize parvenue objects which may be similar plainly not exactly identical to previous objects.NN with their ability to derive intend from imprecise data can be used to detect patterns which are otherwise too complex to be detected 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 unexampled situations and work in true(a) 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 concern 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 corporeal 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 junction in financial markets to predict market behaviour and obtain imitate behaviour to influence decision making. guest Profiling and Customer family relationship ManagementBanks have many and huge databases containing transactional and other details of its customers. Valuable business information can be extracted from these data stores. But it is impossible to support analysis and decision makingusing traditional interrogate 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 merchandise 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 surplus customer groups. Many data mining techniques search profiles of special customer groups systematically using Artificial Intelligence techniques. They generate spotless profiles based on beam search and incremental lear ning 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. Using this categorization information as target of prediction, Decision Tree and Rule proof techniques can be used to build models that can predict default risk levels of new loan applications. Value Prediction MethodsIn this method, for example, preferably of classifying new loan applications, it attempts to predict expected default amounts for new loan applications. The predicted valu es are numeric and thus it requires modelling techniques that can take numerical data as target (or predicted) variables. Neural Network and reasoning backward are used for this purpose. The most common data mining methods used for customer profiling are Clustering (descriptive) Classification (predictive) and regression (predictive) sleeper rule discovery (descriptive) and sequential pattern 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 ride 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 cus tomer 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, approximately only 1% of the total targeted customers are converted into the credit card customers through direct mailing. Seeing the huge cost and effort involved in such selling process, data mining techniques can importantly improve the customer conversion rate by more think selling. Using a predictive test model using decision tree techniques like CHAID (Chi-squared Automatic Interaction Detection),CART (Classification And Regression Trees), indicate and C5.0 it can beanalyzed which customers are more probable to respond. And using this with the risk model using techniques like neural network can help build a test model. The way data mini ng can actually be built into the CRM application is determined by the nature of customer interaction. The customer interaction could be inward (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 campaign involve the firm selecting the citizenry 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 every case, one of the key issues in applying a model to new data set is the transformations tha t 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 for 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 architectural plan to acquire new customer from their competitors. The uncertainties of the buyer make planning of new function and media usage almost impossible. The classical solution is to apply ingrained 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 give 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 pos sible for the banks to use the data available to retain its best customers and to identify opportunities to sell them additional 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 the 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 m ail 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 spy 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, contrivance 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 appear trends to take proactive stance in a highly war-ridden market adding a lot more value to existing products and services and lau nching of new product and service bundles. Reference