Banking and Financial Services Big Data 

Fraud Detection

Banks and financial services firms use analytics to differentiate fraudulent interactions from legitimate business transactions. By applying analytics and machine learning, they are able to define normal activity based on a customer's history and distinguish it from unusual behavior indicating fraud. The analysis systems suggest immediate actions, such as blocking irregular transactions, which stops fraud before it occurs and improves profitability.

Compliance and Regulatory Requirements

Financial services firms operate under a heavy regulatory framework, which requires significant levels of monitoring and reporting. They require deal monitoring and documentation of the details of every trade. This  data is  used for trade surveillance that recognizes abnormal trading patterns.

Customer Segmentation

Big data enables banks to  group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns are then targeted to customers according to their  segments.

Personalized Marketing

One step beyond segment-based marketing is personalized marketing, which targets customers based on understanding of their individual buying habits. While it’s  supported by big data analysis of merchant records, financial services firms can also incorporate unstructured data from their customers' social media profiles in order to create a fuller picture of the customers' needs through customer sentiment analysis. Once those needs are understood, big data analysis can create a credit risk assessment in order to decide whether or not to go ahead with a transaction.

Risk Management

 The risks of algorithmic trading are managed through backtesting strategies against historical data. Big data analysis can also support real-time alerting if a risk threshold is surpassed.

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