Financial Services Use Cases

Below are a few of the use cases that illustrate how big data and Hadoop are being integrated into the financial services industry, providing companies with insights into their operations, their customers, and their markets.

Fraud Detection

Flagging anomalous activities in real time can help prevent potential security attacks or fraud. The RedGate Analytic Platform gives banks the ability to build usage models of “normal” behavior from histories of consumer behavior, analyze incoming transactions against individual and aggregate purchasing histories and take appropriate action if the activity falls outside the confidence level of normal behavior. As more data is ingested, more precise models can be built so the system can more accurately separate the atypical but legitimate behavior from the suspicious activities.

Customer Segmentation Analysis

Banks can create a more meaningful and effective context for marketing to customers if they can define distinct categories or “segments” in which each customer belongs. Often, these segments are defined based on demographic information, but the more cohesive and useful segments are also defined by customer behavior. Banks can define better customer segments by using the RedGate Analytic Platform to collect and analyze all of the data that they have about their customers, such as daily transaction data, interaction data from multiple customer touch-points (e.g., online, call centers), home value data, and merchant records. Banks can then analyze these data sets to group customers into one or more segments based on their needs in terms of banking products and services, and plan their sales, promotion and marketing campaigns accordingly.

Customer Sentiment Analysis

The growing number of channels through which customers communicate has resulted in banks needing to understand what their customers are saying about their products and experiences in order to ensure customer satisfaction. Banks can use the Red Gate Analytic Platform to analyze comments on social media or product review sites, enabling them to quickly respond to negative or positive comments. With this new insight, banks can respond to emerging problems in a timely manner while also effectively connecting with their customers to gain a better understanding of the types of banking products and services such customers find valuable.

Risk Aggregation

Big data techniques can be used to gather and process risk data in order to 1) satisfy risk reporting requirements, 2) measure financial performance against risk tolerance, and 3) slice and dice financial reports. The Red Gate Analytic Platform can benefit risk managers as they can perform on-demand historical analysis of risk data as well as receive real-time alerts when limits are breached.

Counterparty Risk Analytics

Whenever a firm engages in a business transaction with another party, the risk of doing business with that party must be priced into the terms of the deal. Since calculating counterparty risk requires more than computing a formula, firms typically run long and complex “Monte Carlo simulations” to get a complete picture of risk exposure at many points in time in the future. These simulations require huge volumes of data, massive parallel compute power, and system reliability to ensure firms can continue with business operations with no downtime. The RedGate Analytic Platform provides the performance, scalability, reliability, and the easy access and delivery of data to drive the key components of a counterparty risk analytics system.

New Products and Services for Consumer Credit Card Holders

Making new products and services available to consumer card holders is an ongoing initiative for banks. Improved marketing campaigns and ads through effective targeting are required in order to deliver services to consumers and increase revenue for banks. The RedGate Analytic Platform is used to provide new products and services for consumers in real time at a leading credit card company. Advanced machine learning and statistical techniques are employed over data that is stored in a highly available Analytical cluster. RedGate Analytic Platform gives the credit card company the ability to use machine learning techniques for multiple purposes, including fraud detection and recommendations.

Credit Risk Assessment

Due to the global financial crisis, there are now much more stringent rules for approving customer loan requests, so banks need more accurate ways to determine a person’s credit risk. A number of quantitative indicators are used for credit risk assessment and credit scoring. The RedGate Analytic Platform enables banks to pull in customer data on everything from deposit information, through customer service emails, to credit card purchase history in order to gain a holistic view of their customers. With the RedGate Analytic Platform, financial institutions now have the tools they need to construct an in-depth view of their customers so they can properly provide accurate credit scoring and analysis.

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