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Through the ages, data analytics has been a key aspect of every financial institution. From invest banking to credit scoring to securities trading – data analytics has played a major role in arriving at a data-driven decision. With the advent of technology, big data analytics has gained significant ground in the banking and finance sector. In the last decade, the explosion of big data has opened up enormous potential for banks to grow and stay relevant. While basic data analytics is a critical component of banking strategies, the use advanced and predictive data analytics is growing to help provide deeper insights.

In order to adopt advanced analytics, banks have to understand the components that make up the technology. The main components of advanced analytics can be broken down to four categories:

  • Reporting: focuses on conversion of raw data into information, building data repositories using basic analytics. For example, reporting suspicious activity.
  • Descriptive analytics: processing, identifying patterns, and summarizing the information gathered in reporting. For example, customer segmentation based on spending behavior
  • Predictive analytics: using the above patterns to predict future actions or scenarios. For example, personalization of customer offerings based on customer segmentation
  • Prescriptive analytics: gathering results from descriptive and predictive analytics to determine what, why and how a situation is likely to occur. For example, decision optimization based on economic and consumer trends

Together, these components drive advanced analytics that enables business users to search, conduct, and analyze forecasts and predictions. For financial executives, timely and precise data is critical to arriving at business decisions. Banks worldwide are recognizing the importance of analytics and increasing their advanced analytics investments. Advanced analytics solutions are helping banks vastly improve decision making. Applications range from optimizing everyday activities to enhancing productivity. But the main application of advanced analytics has been in improving customer experience. With digital transformation overtaking the banking sector, customer-centricity is of utmost importance to banks. Banks having a growing need to assess customer behavior to understand their wants and needs, engage customers to improve customer loyalty and retention, and deliver exceptional service to improve customer satisfaction. Other benefits of advanced analytics include:

  • Fraud prevention

Fraud detection is a critical activity in banking. Usually, fraudulent activity is detected by using transaction monitoring systems that require manual intervention and are time-consuming. With advanced analytics, banks are able to predict customer behavior and identify suspicious spending patterns. The alerts are sent out in real-time, impeding further fraudulent activity with quick actions (freezing the account, alerting the customer). Predictive analytics and machine learning can further be deployed to secure and safeguard accounts against repeated cyber-attacks. For example, Danske Bank deployed an artificial intelligence-driven platform to identify and tackle fraud. The system analyzes data and scores online transaction in real-time to provide actionable insights for fraudulent activity. The system has reduced the number of false-positives and the cost of fraud investigations.

  • Identify & acquire customers

Banks are adopting advanced analytics to help obtain more customers through target optimization. Analytics help develop deeper customer segmentation and profiles for  the marketing team to identify the right targets on the right channel. For example, Citi Bank leverages big data analytics for customer retention and acquisition. Using machine learning, Citi analyzes consumer data and target promotional spending.

  • Customer retention

In order to maximize the lifetime value of a customer, banks have to work on their customer retention strategies. Customer retention requires paying attention to the quality of service (QoS), identifying at-risk customers, and providing attractive retention offers. With the help of advanced analytics, banks can delve deeper into customer service, identify behavior patterns and paths, and use insights and conversion results to arrive at a ‘churn score’ to take preventive measure for customer retention. For example, American Express relies on sophisticated predictive models to forecast and prevent customer churn. By analyzing past transactions, the system identifies accounts that are most likely to close and take preventive actions.

  • Cross-selling/Up-selling

In a revenue draining atmosphere, predictive analytics is helping banks open effective revenue streams by cross-selling or up-selling of financial products and services. With predictive analytics, banks can understand customers on a granular level, their usage and spending behavior, digital media sentiments. Powered by this information banks can now create a hyper-personalized sale strategy. For example, First Tennessee Bank leveraged predictive analytics solutions to optimize its market strategy. The highly-targeted campaigns helped increase customer response rate by 3.1 percent and cutting marketing costs by nearly 20 percent. Their targeted offers within high-value customer segments resulted in a 600 percent return on investment.

Banks have to evolve and understand the rapid changed in data analytics technologies. Adopting advanced analytics and inculcating it into the existing banking environment is one of the key elements of surviving in this digital era. The future of banking revolves around leveraging data and advanced analytics towards enhancing the accuracy of predictive models.

This article was originally published on Finextra

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Maveric Systems