Big data has transformed the entire landscape of the financial services industry. Customers can now manage all of their finances digitally, and they rarely ever need to step foot into a bank. They can deposit checks digitally and have those funds reflected in their account immediately, as well as send and receive money in real time. The days of long lines at the bank are over.
However, financial institutions are still not taking full advantage of the massive amounts of data that they’re swimming in. Organizations have taken steps to enhance the customer experience, but in such an incredibly fast paced industry, every competitive advantage needs to be identified and exploited. And with so much data available in the financial services industry, there are massive opportunities to extract insights about customers to achieve the competitive upper hand.
The financial services industry has a considerable amount of valuable personal information about customers at their disposal. They know their customers’ borrowing habits, spending habits, income, transactions, etc. All of this information can be leveraged to create more attractive and personalized product and service offerings.
Big data can also be used to gain valuable insights into a customer’s financial needs. For example, if a financial institution notices that a customer has increased their spending on home improvement projects, they can send them a personalized message offering a home improvement loan or line of credit. Another example: if a customer opens a joint bank account for their child that could be an appropriate time to offer them help with a college savings plan.
By analyzing both structured and unstructured data, big data can be used to extract insights about individual preferences and spending patterns to advertise products and services that provoke customer loyalty. However, extracting insights from big data is much easier said than done. It requires the convergence of multiple technologies for things like data quality, data governance and analytics.
For financial institutions, the most effective method for turning raw data into insights is to utilize an all-inclusive big data platform with multiple capabilities. An integrated tool offers a full-service solution that can ingest, prepare, analyze, and act on data, enabling you to operationalize insights derived to positively impact customer experience.
To ensure the reliability of those insights, the platform needs to maximize data quality to improve analytical outcomes. It should perform high volume data quality checks such as data profiling, consistency, conformity, completeness, timeliness, reconciliations, and visual data prep, leveraged in combination with machine learning to foster end-user trust by verifying the quality of big data. Data quality can either be the foundation of a big data project or a latent liability. But data quality is just one of the requirements needed to produce high-value insights.
The platform must also include data governance capabilities to deliver an all-inclusive view of a financial institution’s data landscape. With a framework of data governance, financial institutions can easily define, track, and manage all aspects of their data assets, enabling collaboration, knowledge-sharing, and user empowerment through transparency across the entire enterprise. Now that we’ve established understanding through data governance and trust by ensuring data accuracy and quality, it is time to run analytics—the final and most crucial element for turning data into a competitive edge.
Analytics act as the engine that enables financial institutions to extract meaningful insights from big data. Ideally, a platform should apply machine-learning algorithms with intuitive drag-and-drop functionality to conduct ad hoc analysis – segmentation, classification, regression, recommendation, and forecasting to derive meaningful customer insights that can represent a competitive differentiator in a tough marketplace.
To learn more about integrating data governance and data quality with analytics, and turning financial data into actionable insights, download the data sheet below.
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