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STATISTICA Data Miner Predictive Modeling Solutions for the Banking Industry
To understand customer needs, preferences, and behaviors, financial institutions such as banks, mortgage lenders, credit card companies, and investment advisors are turning to the powerful data mining techniques in STATISTICA Data Miner. These techniques help companies in the financial sector to uncover hidden trends and explain the patterns that affect every aspect of their overall success. Financial institutions have long collected detailed customer data - oftentimes in many disparate databases and in various formats. Only with the recent advances in database technology and data mining software have financial institutions acquired the necessary tools to manage their risks using all available information, and exploring a wide range of scenarios. Now, business strategies in financial institutions are developed more intelligently than ever before.
Areas of Application
Fraud Detection
Banking fraud attempts have seen a drastic increase in recent years, making fraud detection more important than ever. Despite efforts on the part of financial institutions, hundreds of millions of dollars are lost to fraud every year.
STATISTICA Data Miner helps banks and financial institutions to anticipate and quickly detect fraud and take immediate action to minimize costs. Through the use of sophisticated data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
Identify causes of risk; create sophisticated and automated models of risk.
Tools and Techniques
STATISTICA Data Miner will empower your organization to provide better services and enhance the profitability of all aspects of your customer relationships. Predict customer behavior with STATISTICA Data Miner's General Classifier and Regression tools to find rules for organizing customers into classes or groups. Find out who your most profitable, loyal customers are and who is more likely to default on loans or miss a payment. Apply state-of-the-art techniques to build and compare a wide variety of linear, non-linear, decision-tree based, or neural networks models. Recognize patterns, segments, and clusters with STATISTICA Data Miner's Cluster Analysis options and Generalized EM (Expectation Maximization) and K-means Clustering module. For example, clustering methods may help build a customer segmentation model from large data sets. Use the various methods for mapping customers and/or characteristics of customers and customer interactions, such as multidimensional scaling, factor analysis, correspondence analysis, etc., to detect the general rules that apply to your exchanges with your customers. STATISTICA Data Miner's powerful General Neural Networks Explorer offers tools including classification, hidden structure detection, and forecasting coupled with an Intelligent Wizard to make even the most complex problems and advanced analyses seem easier. Uncover the most important variables from among thousands of potential measures with Data Miner's Feature Selection and Variable Filtering module, or simplify the data variables and fields using the Principal Components Analysis or Partial Least Squares modules.
Advanced forecasting methods
STATISTICA Data Miner also features Linear and Nonlinear Multiple Regression with link functions, Neural Networks, ARIMA, Exponentially Weighted Moving Average, Fourier Analysis, and many others. Learn from the data available to you, provide better services, and gain competitive advantages when you apply the absolute state-of-the-art in data mining techniques such as generalized linear and additive models, MARSplines, boosted trees, etc.