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Big Data

Big Data Use Cases in Financial Services

By 2014-06-21April 8th, 2020No Comments

In a hyper-competitive, customer-driven environment, Financial Services Institutions (FSIs) must capitalize on internal and external data sources to gain an accurate understanding of customers, markets, products, services, channels, and competitors. In addition to structured data, a vast amount of unstructured but valuable data is generated through social media. FSIs must index, consume, and integrate structured and unstructured data using Big Data technology to realize the value of data.

The Big Data market is worth over USD 5 billion and is expected to exceed USD 50 billion by 2017. Over 2.5 quintillion bytes of data is generated daily. With rapid advances in technologies like MapReduce, Hadoop, NoSQL, and the cloud, there is significant innovation in data. In addition, the cost of hardware (e.g., NAS-based storage, in-memory data grids/ RAM, etc.) is reducing. Further, software-enabled storage products are now available at reasonable prices. A combination of these factors facilitates highly scalable architecture required for Big Data implementations.

Key use cases of Big Data technology for FSIs Risk management: Big Data helps FSIs manage liquidity, credit, default, enterprise, counterparty, reputational, and other risks. It also enables centralized risk data management. Real-time individual risk profiles can be created for customers based on their social networking activities, purchase behaviour, and transaction data.

Big Data can help meet regulatory requirements in a cost-effective manner. Regulatory mandates require storing and analyzing transactional data dating back several years. Big Data helps build dynamic data structures that comply with changing reporting requirements. It also enables instant analysis of risk scenarios for institutions with growing data volumes.

A comprehensive view of aggregated counterparty risk exposures, positions, and impact enhances performance and reduces default. Big Data helps analyze behaviour profiles, cultural/ demographic segments, and spending habits of customers to enhance the lender’s risk management capability. Predictive credit risk models based on a large amount of payment data helps prioritize collection activities. In addition, market events across regions can be captured and insights gleaned in real time from news, audios, visuals, and social media.

Fraud detection: Big Data can help in fraud mitigation, Know Your Customer (KYC) and Anti-money Laundering (AML) monitoring, and rouge trading/ insider trading prevention programs. Big Data analysis enables detection of deviation from a standard pattern of customer behaviour for proactive fraud identification and prevention. For instance, real-time outlier detection and analysis can be undertaken for a credit card used in distant locations within a short span of time. Similarly, real-time analysis of transactions based on diverse data sets is possible. When fraud is anticipated, the transaction can be blocked even before it takes place. Significantly, Big Data can help in ATM fraud reduction through proactive analysis of geographical and other data points, and identification of ATMs that are likely to be targeted by fraudsters.

Customer delight: Big Data can help FSIs better understand the needs of their customers. Petabytes of data can be analyzed in real time to deliver bespoke services and products to customers. Real-time analysis of unstructured data from social media and other sources enables customer and trading sentiment analysis (find out how customers feel about a new product/ service, or assess influencers and customer sentiment in response to broad economic trends/ specific market indicators). FSIs will be able to better manage their brand image by proactively anticipating customer needs and issues, and responding to negative opinions.

Big Data aids in micro-level understanding of clients and enables targeted and personalized offers. Significantly, it offers a 360-degree view of the customer. Issue resolution at contact centres can be improved through real-time analysis of unstructured data (voice recordings) for content quality, sentiment analysis, and trends and patterns identification. Internal customer logs and social media updates can be analyzed to identify customer sentiment and dissatisfaction points for timely action. Big Data can recommend robust call centre data integration with transaction data to reduce customer churn, enhance up-sell and cross-sell; and enable proactive alerts. It facilitates extraction of unstructured information from IVR and other customer service systems, and enables blending of internal data with social media inputs.

Sales enhancement and cost reduction: FSIs can gain useful insights into when and where customers use their credit/ debit cards, and customer behaviour patterns from Big Data. Based on the monitoring of customer behaviour, FSIs can take predictive actions and enhance their cross-sell and up-sell capabilities. Sentiment analysis-enabled lead management and sales forecasting can be initiated through social media analytics. It can also facilitate real-time and proactive micro-segmentation, and smart location-based offerings.

Several FSIs are challenged by legacy systems that are costly to maintain. These institutions can migrate their legacy data to integrated Big Data platforms and add valuable data sources to mine rich and valuable insights. Operational efficiencies can be further improved with Big Data platforms that enable monitoring and analysis of transactional and unstructured data (voice recognition, social media comments, and e-mails). The workload at financial service enterprises can be predicted and staffing needs in branches and call centres can be optimized.

Operations and execution: The operations of FSIs that have undergone mergers and acquisitions can be challenging. New core infrastructure solutions enabled by Big Data can streamline operations. For example, Big Data enables standardization of loan servicing time across channels and entities. In addition, institutions can adopt data processing approaches and optimize the supply chain. Enterprise payments hub optimization provides a better view of payments platform utilization.

Big Data can improve operational capabilities of FSIs and enhance global, regional and local services. Real-time insights from transactions help provide the right services to customers and at the right price using the right channel. Capital markets firms have multiple data sources and data silos across the front, middle and back office. Big Data allows operational data store consolidation. When data tagging is undertaken using Big Data, trades/ events can be identified, thereby preventing duplicate, invalid or missed trades. Big Data enables storage of a large quantity of historical market data and allows feeding dynamic trading predictive models and forecasts. It also facilitates analysis of complex securities with market, reference and transaction data from diverse sources. In addition, organizational intelligence can be improved through employee collaboration analytics.



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