According to Forbes, 87% of companies think big data will make big changes to their industries before the end of the decade. Even more think that not having a big data strategy will cause their companies to fall behind.
There's plenty of big data in every industry, especially banking and financial services. Except for dispensing cash from ATMs, there's nothing tangible—every customer interaction simply generates electronic records that must be retained due to regulatory requirements. Thanks to big data analytics, financial services firms are no longer simply storing data as required; they're actively using it in order to generate business insights and add value.
They aren't waiting to conduct historical analyses, either. Most of the big data analytics that these businesses perform happen in real time to drive immediate decision-making. Here are five of the most common use cases where banks and financial services firms are finding value in big data analytics.
1. 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.
2. Compliance and Regulatory Requirements
Financial services firms operate under a heavy regulatory framework, which requires significant levels of monitoring and reporting. The Dodd–Frank Act, enacted after the 2008 financial crisis, requires deal monitoring and documentation of the details of every trade. This data is used for trade surveillance that recognizes abnormal trading patterns.
3. Customer Segmentation
Banks have been under pressure to change from product-centric to customer-centric businesses. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them 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.
4. 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.
5. Risk Management
While every business needs to engage in risk management, the need may be largest in the financial industry. Regulatory schemes such as Basel III require firms to manage their market liquidity risk through stress testing. Financial firms also manage their customer risk through analysis of complete customer portfolios. 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.
More than 25% of financial firms have already implemented big data projects and are already obtaining a competitive advantage. Due to both regulatory requirements and the perceived value of big data analytics, financial firms will continue to implement big data analytics projects. This will require increased investments in data center technology as well as increased hiring of staff with big data skills. For value-added resellers, understanding their use cases will lead to additional opportunities to sell big data products and services.