4 Real-World Examples of How Financial Institutions are Making Use of Big Data and Data Integration

Big data has moved beyond ‘new tech’ status and into mainstream use. Most businesses are now leveraging data integration to consolidate systems, eliminate data silos, and streamline operations. Also commonplace is big data for things like improving customer service, delivering real-time business intelligence, and predicting future market trends. But within the financial industry (as in most industries), there are some specialized uses for data integration and big data analytics.

In addition, as part of those use cases, many financial institutions need to access key customer data from mainframe applications and integrate that data with Hadoop and Spark to power advanced insights. How are real financial institutions like banks using these technologies?

Fine-tuning their customer segmentation

Based on the data about previous purchases and activities, banks can identify which customers need specific investment products, insurance coverage, types of checking and savings accounts, types of mortgages and other banking products.

Before banks had customers and they had products. They tried to cross-sell their products to their existing customers and market these products to new customers. With data integration and analytics, they can now identify which customers are most likely to invest in what products. They can also identify new markets for their existing products and cleverly segment their customers to know exactly what products, offers, special deals, and incentives are most successful with a particular segment.

Expect to get fewer offers from your bank for things that are irrelevant to you, and more promotions that are targeted to your needs and financial goals.

Detecting fraudulent activities

Ann and her husband just bought a new home and spent quite a lot of time shopping for nice things to make the home comfortable and beautiful. This meant that the couple was shopping in stores and buying things outside of their normal activities. Ann received a call from her bank asking her to go over a few of the recent charges. Big data has allowed the bank to identify potential fraudulent activity and get it stopped before folks like Ann and her husband come home to an empty bank account and overloaded credit cards.

When data from all of the banks’ systems is integrated, banks can also identify network traffic that could indicate a cyber security breach, such as someone trying to steal their customers’ identities or credit accounts.

Developing new financial products

According to how well a product sells, doesn’t sell, or to whom it sells, banks can develop products that are likely to sell well in the future. They can also use external data, such as market activities during a recession or what mortgage products sell best when the housing market is stagnate, and use that data to create products that are both useful to their customers and lucrative for the bank.

Lowering investment risks

With data analytics, it’s no longer a game of roulette to determine which investments are most likely to produce a return versus those that will end up costing the investors.

Banks don’t make their money off of the nominal amounts they take in for overdraft protection and transaction fees. Their money comes from investing their deposits into ventures that make money. Until data analytics came of age, this involved a great deal of educated guesswork. With analytics, they can use data integration to consolidate data on market trends, the historical performance of startups, property values, plus their internal data, and determine with unbelievable accuracy which investments will generate a substantial return.

To power data advanced big data analytics, financial organizations need to collect, generate and process large data volumes with exceptional performance and reliability. With Precisely’s data integration solutions, any business can create a modern data architecture that includes any data source regardless of the data’s type, format, origin, or location in a manner that’s fast, easy, cost-effective, secure, and future-proof.

To learn more, read our eBook: How to Build a Modern Data Architecture with Legacy Data.