Predictive modeling to make money online

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Artificial intelligence is making its way into your bank account. As computers get smarter, financial institutions can use consumer databases and historical transactions with the goal of predicting the future. It may sound boring to you, but predictive analytics can help minimize costs and hopefully improve your experience with your bank. Predictive analytics is the process of using computer models to predict future events. Sophisticated programs rely on artificial intelligence and data mining to analyze enormous amounts of information. With those resources, the model attempts to determine what is likely to happen next, given current conditions. In banking, predictive analytics can help customers manage their accounts and complete banking tasks quickly. Financial institutions also benefit by reducing risk and minimizing costs. For better or worse, institutions use a variety of data sources and machine learning. For example, they have your transaction history, and they may tie in demographic information and additional details from external databases. Predictive analytics can improve your experience as a customer in several ways. That said, some may find it unsettling that financial institutions have so much information, and that they depend on computers to make decisions that affect your life. Credit scoring: You may already be familiar with predictive analytics— credit scoring models use data to predict your creditworthiness. For example, the FICO credit score uses statistical analysis to predict how likely you are to miss payments within the next 90 days. Your score is based, in part, on how borrowers similar to you have performed in the past. Help with budgeting: Computer models can help you manage your finances. They can identify when income and expenses typically hit your account, and they can see where your money goes. As a result, they may be able to prevent problems. With advance notice, you can transfer funds from other accounts or contact your mortgage servicer so you avoid overdraft charges , late payment penalties, and other problems. Fraud prevention: Sometimes identity theft is entirely out of your control. Banks with predictive analytics are better equipped to spot problems. They may notice when somebody else uses your credit card or if somebody logs in to your account in an unexpected way. They may also be able to reduce bad check scams , which can cause significant losses for victims you typically lose money in those cases—not the bank.

Better finances through data

With the help of artificial intelligence and predictive analytics, banks are learning to use their ever-growing data hoards to help you make better financial decisions. Bank of America will let mobile banking customers use its new digital assistant, Erica, in March. Besides helping consumers complete routine tasks like transferring funds, Erica will offer financial advice tailored for each user. Or she could share opportunities to save additional money.

Finding the right people for the right offer

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Predictive modeling uses statistics to predict outcomes. For example, predictive models are often used to detect crimes and identify suspects, after the crime has preditive place. In many cases the model is chosen on the basis of detection theory to try to guess moeny probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set.

For example, a model might be used to determine whether an ,odeling is spam or «ham» non-spam. Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learningas it is more commonly referred onlline in academic or research and development contexts. When deployed commercially, predictive modelling is often referred to as predictive analytics. In the former, one may be entirely satisfied to make use of indicators of, or proxies for, the outcome of.

In the latter, one seeks to determine true cause-and-effect relationships. This distinction has given rise to a burgeoning literature in the fields of research methods and statistics and to the common statement that » correlation does not imply causation «.

Nearly any statistical model can inline used predlctive prediction purposes. Broadly speaking, there are two classes of predictive models: parametric and non-parametric. A third class, semi-parametric models, includes features of. Parametric models make «specific assumptions with onljne to one or more of the population parameters that characterize the underlying distribution s «. Uplift modelling is a technique for modelling the change in probability caused by an action.

Typically this is a marketing action such as an offer to buy a product, to use a product more or to re-sign a contract. For example, in a retention campaign you wish to predict the change in probability that a customer will remain a mojey if they are contacted. A model of predictice change in probability allows the retention campaign to be targeted at those customers on whom the change in probability will be beneficial. This allows the retention programme to avoid triggering unnecessary churn or customer attrition without wasting money contacting people who would act.

Development of quantitative methods and moddeling greater availability of applicable data led to growth of the discipline in the s and by the late s, substantial progress had been made by major land managers worldwide. Generally, predictive modelling in mkdeling is establishing statistically valid causal or covariable relationships between natural proxies such as soil types, elevation, slope, vegetation, proximity to water, geology, geomorphology.

Through analysis of these quantifiable attributes from land that has undergone archaeological survey, sometimes the «archaeological sensitivity» of unsurveyed areas can be anticipated based on the natural proxies in those areas. By using predictive modelling in their cultural resource management plans, they are capable of making more informed decisions when planning for activities that have the potential to require ground disturbance and subsequently affect archaeological sites.

Predictive modelling is used extensively in analytical customer relationship management and data mining to produce customer-level models that describe the likelihood that a customer will lnline a particular action. The actions are usually monwy, marketing and customer retention related. Ppredictive example, a large consumer organization such as a mobile telecommunications operator will have a set of predictive models for product cross-sellproduct deep-sell or upselling and churn. It is also now more common for such an organization to have a model of savability using an uplift model.

This predicts the likelihood that a customer can be saved at the end of a contract period the change in churn probability as opposed to the standard churn prediction model. Predictive modelling is utilised in vehicle insurance to assign risk prefictive incidents to policy holders from information obtained from policy holders. This is extensively employed in usage-based insurance solutions where predictive models utilise telemetry-based data to build a model of predictive risk for claim likelihood.

Initially the hospital focused on patients with congestive heart failure, but the program has expanded to include patients with diabetes, acute myocardial infarction, and jodeling.

InBanerjee et al. The model was trained on a large dataset 10, patients and validated on a separated dataset patients.

To provide explain-ability, they developed an interactive graphical tool that may improve physician understanding of the basis for the model’s predictions. The high accuracy and explain-ability of the PPES-Met model may enable the model to be used as a decision support tool to personalize metastatic cancer treatment and provide valuable assistance to the physicians.

Predictive modeling in trading is a modeling process wherein the probability of an outcome is predicted using a set of predictor variables. Predictive predictivve can be built for different assets like stocks, futures, currencies, commodities.

It utilizes mathematically advanced software to modelnig indicators on price, volume, open interest and other historical data, to discover repeatable patterns. Although not widely discussed modeeling the mainstream predictive modeling community, predictive modeling is a methodology that has been widely used in the financial industry in the past and some mobey the major failures contributed to the financial crisis of — These failures exemplify the danger of relying exclusively on models that are prdictive backward looking in nature.

The following examples are by no mean a complete list:. The rating can take on discrete values from AAA down to D. The rating is a predictor of the risk of default based on a variety of variables associated with the borrower and historical macroeconomic data.

Almost the entire AAA predictivve and the super-AAA sector, a new rating the rating agencies provided to represent super safe investment of the CDO market defaulted or severely downgraded duringmany of which obtained their ratings less than just a year previously. One particularly memorable failure is that of Long Term Capital Managementa fund that hired highly moxeling analysts, including a Nobel Memorial Prize in Economic Sciences winner, to develop a sophisticated statistical model that predicted the price spreads between different securities.

The models produced impressive profits until a major debacle that caused the then Federal Reserve chairman Alan Greenspan to step in to broker a rescue plan by the Wall Street broker dealers in order to prevent a meltdown of the bond market. Using relations derived from historical data to predict the future implicitly assumes there are certain lasting conditions or constants in a complex.

This almost always leads to some imprecision when the system involves people. In all data collection, the collector first defines the set of variables for which data is collected. After an algorithm becomes an accepted standard of measurement, it can be taken advantage of by people who understand the algorithm and have the incentive to fool or manipulate the outcome.

This is what happened to the CDO rating described. The CDO dealers actively fulfilled the rating agencies’ input moeling reach an AAA or super-AAA on the CDO they were predixtive, by cleverly manipulating variables that were «unknown» to the rating agencies’ modeping models.

From Wikipedia, the free encyclopedia. This article has been nominated to be checked for its neutrality. Discussion of this nomination can be found on the talk page. April Learn how and when to remove this template message. Predictive Inference: An Introduction. Myths, Onlinr and Methods 1st ed. Palgrave Macmillan. April 27, Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press.

Principles of Statistical Inference. Cambridge University Press. Agency for Healthcare Research and Quality. Retrieved Scientific Reports. System Trader Success. Categories : Statistical classification Statistical models Prediction Business intelligence. Hidden categories: Wikipedia articles needing page number citations from September Articles needing POV-check from April All articles with unsourced statements Articles with unsourced statements from March Articles with unsourced statements from April Namespaces Article Talk.

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