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Banking Industry

Banking Industry: A Mine of Information

The success or failure of business decisions is based largely on the quantity & quality of information at your disposal. Some industries have access to more information than others; one particularly good example is the banking sector. Like healthcare, the banking industry is subject to a wide array of stringent regulations that govern their day-to-day operations; remaining compliant within this environment requires banking institutions to memorialize nearly all of aspects of their operations. For example, conversations need to be recorded, transactions need to be saved, and customer transcripts need to be maintained. This is in addition to the normal, unregulated aspects of data production, such as marketing campaigns, social media content, and website management. In short, the banking sector produces reams of raw data on a daily basis, most of which is scarcely analyzed or even reviewed.

Banking Analytics: Opening the Data Vaults

Dataiku's Data Science Studio (DSS) is a powerful platform that enables companies to take advantage of this raw data by transforming it into highly useful analytical forecasts. Like diamonds amidst coal, this raw data frequently includes critically useful information that is left undiscovered because its extraction is often too costly and burdensome. Dataiku's DSS facilitates this data exploration via a 3-step process:

  • Collection: DSS can collect data from any source, from SQL databases to simple Excel spreadsheets, or to Hadoop clusters;
  • Cleansing: Raw data is often unformatted, unparsed, and contains missing values. The cleansing process homogenizes the format & structure of the data and removes extraneous data, effectively levelling the dataset playing field. To clean the data, analysts can point, click, and build, and developers and data scientists can code with the languages they know best;
  • Analytics: DSS lets users leverage powerful machine learning algorithms (Scikit Learn, H2O, or your very own) to create models designed to predict future events. You are in complete control of which algorithms or even which datasets to include for analysis. For example, social media content could be compared to campaign sales to analyze customer trends and predict untapped marketing segments. Likewise, transactional data, customer information, and international transfers could be used to formulate anti-banking fraud policies.

Using Dataiku for Banking Analytics

Dataiku's DSS enables your financial institution to combine structured & unstructured data to benefit your financial margins and improve customer satisfaction by optimizing cross-selling, up-selling, and retention offers at the perfect point-in-time. A growing number of world-renowned banks are now using DSS to build their own predictive banking analytics solutions, with applications in a variety of fields:

  • Complaint Management and CRM: Don’t just collect and store customer feedback. DSS empowers your firm to proactively analyze customer feedback to create actionable steps to address issues before they become problems;
  • Risk Management: Understand all potential aspects of new ventures before committing both time & resources to their implementation. DSS can use historical data, along with any other pertinent datasets, to help you understand the expected level of risk involved;
  • Customer Trend Analytics: Make intelligent marketing decisions by analyzing how customers are interacting with your products & services;
  • Increase Operational Efficiency: Predictive banking analytics can help you to understand which business segments within your organization are dragging down your company’s productivity;
  • Estimate Reliability of Current Forecasts: Comparison between real-world results and forecasted analysis helps to sharpen the accuracy of predictive analytics. In addition, DSS can be used side-by-side with your existing forecasting tools in order to determine the reliability of current processes;
  • Detect and Prevent Banking Fraud: Use customer, transactional, and social channel data to create more in-depth profiles of potentially risky customer relationships or fraudulent interactions. Whether you take a customer or transaction-centric approach, with DSS you can use an array of disparate data to predict the possibility of banking fraud before it happens;
  • Reduce Churn: Churn is a measurement of the number of clients who discontinue a service in a given time period. DSS enables your financial institution to accurately determine your current churn rate and predict future churn rates based on changes to the underlying data. The predictive capability of DSS is critical in order to anticipate and manage risks associated with customer churn;
  • Retain Valuable Customers: The output produced from customer churn analysis can also be used to help your organization retain valuable customers. Use DSS to understand which factors specifically impact your VIP customers and how negative reactions can be addressed and mitigated.

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