Data Science

/Data Science

The Impact of Insurance Fraud

By | 2018-03-20T17:58:02+00:00 July 3rd, 2017|Categories: Data Science, fraud analytics|

Amstat Analytics Group has applied an effective analytical approach to evaluate the cost of insurance fraud and to quantify the value of investments in counter fraud analytics capabilities in order to reduce our client's risk exposure. By incorporating this empirical data into its simple Monte Carlo model, our analysis shows that the annualized cost of [...]

Predicting Advertising Campaign Success

By | 2017-06-22T17:42:36+00:00 June 22nd, 2017|Categories: Data Science, Predictive Models|

The purpose of this study was to predict the success of advertising campaigns by analyzing numerous data sources to determine the ROI of an advertising campaign. (e.g., Reach, Influence). We predicted the success of a particular advertising campaign based on brand, talent, external factors, and trends.  We built predictive models. There is a 90% probability that [...]

Forecasting Sales for New Sites

By | 2017-05-22T17:41:24+00:00 May 22nd, 2017|Categories: Data Science, Location Analytics, Predictive Models|

The purpose of the study was to forecast sales of new sites based on the data and sales of the operating companies. The hypothesis being tested was: H1: The investments in new companies are getting a high return. We built predictive models. We used location analytics. There is a 72.35% probability that the investments in [...]

Friendly-fraud (Chargeback) Detection Predictive Model

By | 2017-04-15T18:29:32+00:00 April 15th, 2017|Categories: Data Science, Predictive Models|

The Problem Our client has to actively seek friendly fraud (aka chargeback fraud) risk. Friendly fraud occurs when an individual makes a purchase online via their credit or debit card then requests a chargeback from the bank once the goods or services have been consumed. A completed chargeback cancels the original transaction and refunds the [...]

Customer Churn

By | 2017-04-10T17:45:48+00:00 April 10th, 2017|Categories: Data Science, Predictive Models|

The purpose of the study was to identify customers at risk of leaving. The research question being tested was: Research Question 1: What is the probability that customers will leave? We built predictive models. We performed churn analyses for the utility industry by analyzing the data including addresses, gas consumption, products, contract terms and more. [...]

Fraud Detection and Fraud Prevention Analytics

By | 2017-03-22T17:38:16+00:00 March 22nd, 2017|Categories: Data Science, Fraud Prevention Analytics, Machine Learning|

The purpose of the study was to 1) iterate through fraud pattern changes, 2) detect anomalies using strong data profiling, and 3) study fraud indicators, derived from the current transaction attributes as well as cardholder’s historical activities. We built models using algorithms and machine learning. We provided more predictive capabilities that could identify and mitigate fraud. [...]

Text Analytics

By | 2017-02-22T17:35:36+00:00 February 22nd, 2017|Categories: Data Science, Text Analytics|

The purpose of the study was to develop an understanding of the relevant online conversation, both pre- and post-FDA approval of [competitor 1] and to identify stakeholders and discover discussion patterns within the set of conversations. This in-depth “competitive analysis” focused primarily on conversations with patients and caregivers. The data included posts from Twitter, blogs, [...]

Voice Analytics to Predict Customer Behavior

By | 2017-01-10T17:33:48+00:00 January 10th, 2017|Categories: Data Science, Natural Language Processing|

  The purpose of the study was to measure if the phone call data that our clients capture can be used to determine predictive behavior. The hypothesis being tested was: H1: The phone call data that we capture (every interaction) can be used to determine predictive behavior. We used Natural Language Processing. We found that [...]

Sales Prediction Algorithm

By | 2016-11-04T17:31:15+00:00 November 4th, 2016|Categories: Data Science, Machine Learning|

  The purpose of the study was to predict the sales per day for each of the next 100 days. We created a general purpose data science module in python that could Digest the daily sales data for the past 3 years and develop a model Identify and use any publicly available sources of data [...]

Real Estate Forecast Algorithm

By | 2016-10-03T17:29:25+00:00 October 3rd, 2016|Categories: Data Science, Machine Learning, Predictive Models|

The purpose of this study was to forecast the sales and leasing revenue derived from non-managed properties for the next six-month period, based on the historical information in the Input data sheet for the previous six months. The hypothesis being tested was: H1: The sales and leasing revenue derived from non-managed properties will improve. We [...]

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This Is A Custom Widget

This Sliding Bar can be switched on or off in theme options, and can take any widget you throw at it or even fill it with your custom HTML Code. Its perfect for grabbing the attention of your viewers. Choose between 1, 2, 3 or 4 columns, set the background color, widget divider color, activate transparency, a top border or fully disable it on desktop and mobile.