Leveraging the Power of Data and Analytics … Yeah, Yeah. A Pragmatic Approach in 5 Steps

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In this special guest feature, Allan Brown, VP and General Manager of U.S. Digital Community Markets at Finastra, believes the lifecycle of data is circular, not linear and in order to put data into action, it is crucial that FIs take a holistic look at what that truly means. Understanding how to properly cleanse and utilize data will ultimately help successfully drive experience and innovation. Allan is accountable for driving Finastra’s Digital Banking business in North America. As a senior executive, Allan brings over 25 years of experience leading software businesses. Allan joined Finastra from Kodak Corporation where he served as Corporate Vice President and General Manager of Unified Workflow Solutions. During his tenure, he and his team rejuvenated the organizations innovation and sales engine delivering strong business results, disrupting the market with cloud services for print, operational excellence, and delivering delightful customer experiences.

In this era of digitalization, financial institutions (FIs) have access to more data than they ever anticipated – or often know what to do with.

At the same time, artificial intelligence and machine learning are rapidly replacing the traditional linear approach to data analytics.

The banking industry in particular is, of course, undergoing a period of unprecedented transformation, with new tech developments, regulatory requirements and changes in consumer behavior creating new demands. Customers increasingly expect their banks to offer the levels of personalization and user experience that they have come to expect from online retail and entertainment brands.

AI and machine learning present unparalleled opportunities for banks to unleash the power of their data to provide insights about their customers, competitors and operations. The key is knowing how to cleanse, enhance and analyze the data to make it actionable for the benefit of both the bank and its account holders.

The right data and analytics strategies can drive growth, enhance productivity, reduce risk, improve the customer experience, personalize product offerings and drive innovation.

Here are some key considerations for banks when planning and executing their data and analytics strategies, so that they make the most of the data available to them. 

1. Build a unified data solution

One of the biggest challenges facing most traditional banks is that data often exists in multiple legacy systems and tends to be owned by different business functions. This disconnected architecture prevents banks from accessing and organizing customer insights effectively. Similarly, without the right analysis tools and methods, normalizing and analyzing all the data can be a bewildering task.

Building a unified and streamlined data infrastructure incorporating robust analytics and AI techniques can help an institution to better manage and mobilize the information they hold. Coupled with the rise of open banking and APIs, this will provide more relevant, connected insights and outcomes for the business, along with better value and an improved experience for account holders.

2. Adopt a customer-first approach

Under the traditional service delivery model, customers have played only a limited role in banks’ product strategies. But this paradigm is now being flipped by fintechs and neobanks offering user-centric products and services. To compete with these new market entrants, it’s essential that banks start using data and technology to truly understand their account holders’ needs and behaviors – and use this intelligence to drive strategy, services and product development.

This requires FIs to step back and reimagine how they engage with their customers. Not only do they need to understand the rich and robust data sets they already have, and to better orchestrate and analyse the data, but match that data with other, external services and data sources to provide a more rewarding and engaging customer experience.

By analysing and leveraging aggregated data insights an institution can develop products and services to truly meet their account holders’ needs and, ultimately, reshape their business models.

3. Improve the customer journey through ‘hyper-personalization’

Creating a unified data architecture to create a 360° view helps FIs to improve engagement levels with account holders through high-quality, personalized relationships – and compete in today’s marketplace. But creating ‘types’, segments or demographics to personalize content is no longer personal enough.

Nowadays, data scientists are using AI and machine learning to take personalization to a whole new level of ‘hype-personalization’. FIs need to be using these technologies to leverage and analyze each individual’s data to deliver services and products that are specifically for them.

True customer-centricity is about using everything an FI knows about an account holder to help them move their financial life forward. By unleashing the power of their data and strategically employing analytics, banks can offer financial wellness tools that really make a difference. 

To compete in the future, the next generation of bankers and FIs will need to have a holistic view of account holders, and not just in terms of their money, but a complete picture of each individual’s lifestyle. 

4. Put data front and center

In the past, customer insight data has often been owned by an FI’s marketing function and simply used to ‘sell more stuff’. But, given the potential impact on revenue, customer experience, cost reduction and risk management, it’s clear that data and analytics need to be front and center and not just seen as a back office task.

While financial services is an enormously complex industry and there are many strategic imperatives at play within each institution, it’s essential that the data and analytics strategy aligns with business strategy. But fully leveraging data requires a paradigm shift – embedding data, intelligence and automation into every process and interaction to fundamentally change an institution’s architecture and operating model.

5. Be agile, flexible and responsive

Finally, it’s important to remember that the lifecycle of data is circular, not linear. So using data and analytics effectively is a continuous process of discovery, investigation and implementation.

Banks need to put their data into action, measure outcomes and respond appropriately – over and over again. Analyzing the impact of every data interaction will help optimize the impact and provide more, and better, data, thereby enabling an institution to make more informed decisions, improve and personalize engagement with account holders and increase share of wallet.

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