Banking on Hadoop: 7 Use Cases for Hadoop in Finance

Hadoop is present in nearly every vertical today that is leveraging big data in order to analyze information and gain competitive advantages. Many financial organizations firms are already using Hadoop solutions successfully and the ones who are not have plans to do so. If they don’t, they risk enormous market share loss. Following are a few of the most intriguing and essential big data and Hadoop use cases.

Fraud detection: Fraud, financial crimes and data breaches are some of the most costly challenges in the industry. Hadoop analytics help financial organizations detect, prevent and eliminate internal and external fraud, as well as reduce the associated costs. Analyzing points of sale, authorizations and transactions, and other data points help banks identify and mitigate fraud. For example, big data technology can alert the bank that a credit or debit card has been lost or stolen by picking up on unusual behavior patterns. This then gives the bank time to put a temporary hold on the card while contacting its account owner.

Risk management: Every financial firm needs to assess risk accurately, and big data solutions enable them to to do so by effectively evaluating credit exposures. Banks analyze transactional data to determine risk and exposure based on simulated market behavior, scoring customers and potential clients. Hadoop solutions allow for a complete and accurate view of risk and impact, enabling firms to make the best, most informed decisions.

Contact center efficiency optimization: Ensuring customers are satisfied is of utmost importance when it comes to finances, and big data can help resolve problems quickly by allowing banks to anticipate customer needs ahead of time. Analyzing data within the contact center provides agents with timely and concise insight that satisfies customers quickly and efficiently, ensuring cost effectiveness and even improving cross-sales success rates.

Customer segmentation for optimized offers: Big data provides a way to understand customers’ needs at a granular level so that banks and financial organizations can deliver targeted offers more effectively. In turn, these more personalized offers result in higher acceptance rates, increased customer satisfaction, higher profitability and greater retention. Detailed information about customers derived from social media and transactions can be utilized to reduce customer acquisition costs as well as turnover.

Customer churn analysis: Everybody knows that it’s cheaper to keep a customer than it is to go out and find a new one. Big data and Hadoop technologies can help financial firms keep retain more of their customers by analyzing behavior and identifying patterns that lead to customer abandonment. When are customers most likely to leave for the competition, and why? What causes customer dissatisfaction? Where did the firm fail? This information for determining how to avoid customer abandonment is priceless. It’s imperative for financial firms to learn the right steps to implement in order to meet customer needs and save their most profitable customers.

Sentiment analysis: Hadoop and advanced analytics tools help analyze social media in order to monitor user sentiment of a firm, brand or product. If a bank is running a campaign, big data tools can monitor social media by name and report on it by hashtag, campaign name or platform. Analytics on the fine-grained details are insightful, and the bank could then make decisions more accurately based on these insights in terms of timing, targeting and demographics.

Customer experience analytics: As consumer-facing enterprises, financial institutions need to take advantage of the customer data that resides in all of the silos across various lines of business. These include portfolio management, customer relationship management, loan systems, contact center, etc. Big data can provide better insight and understanding, allowing firms to match offers to a customer or prospect’s needs. This then helps the firm to optimize and improve profitable and long-term customer relationships.

The bottom line is that all enterprises, especially financial firms, need to use big data and Hadoop technologies to their fullest potential now, particularly with the overwhelming amount of data and transactions amassed on a daily basis. In order to remain competitive and maintain current customers while attracting new ones, financial firms should start planning to utilize big data technologies today or risk losing more customers to competitors utilizing these tools. That doesn’t necessarily mean in every way possible – it just means in the best way possible for each organization.

Big data and Hadoop technologies are powerful and help financial organizations stay ahead in the market. Set them in motion and watch them deliver results.

Check out some of these technologies and financial use cases here.