Financial Services companies globally are pushing forward with digital transformation initiatives as a strategy to innovate, accelerate growth, and combat the disruptive forces surrounding them. They are balancing demands such as risk management, financial crime prevention and regulatory requirements while forging ahead to offer advanced, personalized services to their customers. A recently issued Capgemini report indicates digital transformation spending is still led by compliance at 60%. At the same time, the competition is only getting more intense as more fintech’s and big techs, all using new technology to attract customers, enter the financial landscape. Recently, Facebook joined the ranks of the popular “pay” providers with the introduction of Facebook Pay and the promise of simplifying payments. Similarly, Google partnered with Citibank to offer checking accounts.
Data Driven Digital Transformation in Financial Services
As part of their digital transformation, financial services institutions are leveraging data and analytics to offer new capabilities and services to customers. Financial service providers have a wealth of history and knowledge about their customers, transaction and interactions history, socio economic background, banking preferences, location and spending patterns, clickstream data, that when aggregated and analyzed can help deliver an overall better customer experience. However, one of the major challenges has been getting a comprehensive, unified view of the diverse sources and types of data. Financial services companies are often burdened by silos of systems that have only become more intertwined and complicated over the years. The data tends to reside in a multitude of disparate systems and operational databases spread across multiple lines of business.
Adding to this, financial services institutions are dealing with key challenges related to the evolution and management of data. First and foremost, the data sets are growing larger. Regulatory requirements such as the Fundamental Review of the Trading Book (FRTB) could require 24-30x more data storage based on expanded historical storage and computational requirements. And then there’s the additional new sources of streaming data including social media streams, clickstreams and IoT data. Second, there is added value in the ability to manage and action data in real or near real-time to be most effective. For example, fraud prevention is most effective and relevant when you can act on potential fraud signals in real-time. On top of this, machine learning algorithms can be used to generate more precise risk scores based on historical and real-time data feeds in order to enhance existing fraud detection platforms with new rules and better insights.
Therefore, while the opportunity to better use the data is recognized by financial services, the path to achieve these benefits is not easy; especially within organizations that have a plethora of systems and technologies, old and new, to manage.
An Enterprise Data Cloud Strategy for Financial Services
The cloud offers a tremendous new opportunity to scale your infrastructure on-demand and offload some of the expense of data management, especially as it relates to new workloads or testbed environments. Yet the reality is, for most financial services institutions, much of the data resides on-premise in data centers and will continue to for a long time – dictated by regional jurisdictions, data security concerns or just historical preference to control the data.
Financial services organizations need a new approach. Flexibility to manage data across environments is critical. Today, organizations need an enterprise data cloud that offers the ability to ingest, process, store, analyze, model any type of data (structured, unstructured, or semi-structured data), regardless of where it lands — at the edge, on premise, in the data center, or in any public, private, or hybrid clouds. An enterprise data cloud unlocks the power of a financial services organization’s data to serve customers better, operate with greater efficiency, and strengthen security to protect your business.
An enterprise data cloud is
1) Optimized for hybrid and multi-cloud environments, delivering the same data management capabilities across data centers, private, and public clouds.
2) Enabled for multiple analytic functions to work together on the same data at its source, eliminating costly and inefficient data silos.
3) Secure – it maintains strict enterprise data security, governance, and control across all environments.
4) 100 percent open source, with open compute and open storage, ensuring zero vendor lock-in and maximum interoperability.
Ultimately, making the best use of an organization’s data is a journey that financial services organizations need to navigate. Financial services companies recognize the impact a comprehensive data and analytics strategy has on their ability to enhance their customers’ experience and manage regulatory compliance more efficiently. With the industry evolving at an unprecedented pace, it is crucial to have an enterprise data approach that enables agility.
Cloudera Data Platform
At Cloudera, we’re enabling an enterprise data and analytics strategy to help financial services companies provide better service to their customers. Watch this video where Steven Totman, Industry Leader for Financial Services at Cloudera, talks about how financial services organizations are leveraging the enterprise data cloud powered by Cloudera to do what they could never do before.
For more information about Cloudera in Financial Services and a sampling of customer case studies, visit our website https://www.cloudera.com/solutions/financial-services.html