Predictive Analytics in Finance Can Accelerate Data-Driven Enterprise Transformation

Predictive Analytics in Finance Can Accelerate Data-Driven Enterprise Transformation

Fintech for financial services is far-reaching. A deep pool of vendors, solutions and technologies promises to help with nearly any pain point, but while many of these offerings work well on their own, they often don’t integrate with other systems. This can result in fragmented systems and data becoming further siloed, making it more difficult to uncover transformative insights.

Every finance organization has rich, robust data pulsing through its systems around the clock. The challenge of implementing predictive analytics in finance lies in collecting, mining and manipulating high-quality data (i.e., making it “edible”) to ensure the optimum utilization that fosters transformation. The preferred path to data-driven BI is a holistic approach that analyzes all available data, shares insights across business functions and employs self-service analytics while ensuring data governance and security.

The Hierarchy of Needs

The first step toward data-driven digital transformation is understanding the hierarchy of needs for data science and machine learning. This hierarchy starts with big data, analytics, data science and machine learning—you can’t successfully implement machine learning without data science and analytics, which can’t be employed without big data. Most importantly, though, they must all operate on the same platform: these pyramid components work synergistically to enhance processes, cultivate efficiencies and increase productivity.

With this foundation established, you can start leveraging predictive analytics in finance to discover connections and correlations within trillions of bits of information that are invisible to the naked eye. As you do, machine learning algorithms will continuously and relentlessly refine models improving with usage and providing your organization with the agility it needs to accelerate innovation. Take Cloudera Data Science Workbench, for example: it can enable analytics teams to conduct data science at scale through the enterprise cloud and the convenience of self-service analytics.

Deep-learning algorithms and applications, including financial fraud and AML detection as well as predictive modeling and analytics, can also enable financial services organizations to evolve into data-driven enterprises faster.

Enhancing the use of data at
United Overseas Bank

When considering these factors in light of its ongoing digital transformation journey, United Overseas Bank(UOB) recently found themselves at a crossroads. With a network of more than 500 offices in 19 countries and territories globally, including subsidiaries in five ASEAN markets, UOB  wanted to deepen their data analytics capabilities and use data insights to enhance the bank’s performance. They wanted a cost-effective and scalable solution that could quickly deliver results, as well as meet the needs of a global organization and the demands of digitally savvy customers.

Cloudera’s big data platform solution provided the capacity, flexibility, speed and scalability to accommodate UOB’s data analytics transformation, supporting up to two petabytes of data. As a result, UOB was able to harness deeper insights from data more quickly. Guided by the insights such as those gleaned by analysing large sets of transactional data, UOB is able to enhance the banking experience for customers and to make it more intuitive and relevant to their needs.

UOB was also able to enhance the way it personalizes offers to its consumers and merchants and to ensure these offers are always relevant to them. AI-powered recommendation engines and real-time sentiment analysis enabled front-line specialists to deliver customer-centric solutions to meet the needs and preferences of individuals. From retail and wholesale banking to compliance and asset management, UOB sharpened tracking operational risk, including improved AML detection capabilities such as flagging suspicious transactions through hidden shell companies and high-risk individuals.

Accelerating UOB’s data-driven initiatives

Chief Data Officer of UOB Richard Lowe commented, “Our collaboration with Cloudera enabled us to be sharper in identifying patterns and linkages and to predict outcomes more accurately.”

Building a modern data platform on Cloudera gave UOB the flexibility and speed to develop new AI, machine learning and predictive analytics solutions, and create a data-driven enterprise. This approach has helped deliver a more personalized and frictionless banking experience, further strengthened security, and increased information sharing.

Lowe noted, “You don’t always know what data will be valuable for a particular use case, or what use cases lie ahead. Cloudera enables us to innovate, pursue new capabilities, and achieve outcomes that wouldn’t be possible otherwise.”

Enabling the right data platform can enable financial service organizations to deepen the way in which they analyse the data they hold, to discover transformational insights and to make actionable improvements to front-, middle-, and back-office operations. Cultivating a data-driven enterprise enhances the prowess of a diligent, modern, cutting-edge brand, relentless in the pursuit of world-class customer service and seamless operational excellence.

To learn more about United Overseas Bank and how they’re leveraging the Cloudera platform, read the full customer story.

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