The use of technology in the financial services industry (FSI) has come a long way in the last decade. The space used to comprise solely large companies using legacy systems and their own behemothic databases to run fairly rigid business processes. Now, barely a week passes without a new player or new service entering the marketplace – for example:
- N26, a mobile-first bank operating in 17 European countries with 300,000 users, offers an in-app marketplace, allowing its users to access other FinTech providers.1
- The Royal Bank of Canada allows its customers to pay bills on their iPhone or iPad via Siri, Apple’s voice assistant.2
- The Bank of China (Hong Kong) has launched an enquiry service through popular social platform WeChat, which includes a smart ‘robot’ customer service feature that analyzes text inputs to provide tailored answers.3
For disruptors, success means being able to use multiple sources of very different types of data to gather and respond to insights in real time. It’s all about being agile, insightful and customer-centric.
This accelerated pace of change is likely to stick around, with the banking landscape of 2030 poised to look very different again from today’s.4 For example, banks may lose their customer-facing infrastructure completely and instead become B2B service providers. Or they may go the other way and embrace their customer data to become the ultimate practitioners of customer centricity.
Whichever direction you choose to go, the ability to transform depends on the availability of data and the ability to draw insights from it, within the organization. None of the innovations of 2030 will be possible without advanced analytics capabilities like artificial intelligence (AI). Indeed, advanced analytics are already at the core of some of today’s most innovative use cases. For example, a number of leading financial organizations use AI techniques to identify potentially fraudulent activity by individuals or even organized groups, and block or prevent attacks.5 Others have used machine learning and analytics to pinpoint opportunities to offer more targeted product and service recommendations to customers, increasing sales as a result.6
It’s important to determine where and how to invest in breakthrough technologies like these (and whatever comes next) in your own organization. A recent white paper from Intel outlines how you can go about building your own insights-driven business; key considerations are to think about the long-term from the beginning, and work closely with your IT department. Only by combining your deep understanding of your business, customers and industry with their technology know-how will you be able to come up with the most effective and transformational use of new technologies.
Make sure your innovations are business outcome-centric
While the latest trend might seem exciting, don’t assume that it’s exactly what your business needs right now. Consider your current strategy and think about where you can make improvements, using data and insights that will help you and your colleagues better meet your business goals. Find out about current areas of difficulty and what could be done to alleviate them. Of course, every investment should have a strong business case, but when you’re looking to introduce anything new this is even more important. By demonstrating the business value of your initiative early on, you’ll secure more support and investment over time. A good way to do this is to start with some ‘low-hanging fruit’ – areas in which analytics can reliably deliver quick results. For FIs, these are often around compliance, operational efficiency or customer engagement.
Think about the implications for people, processes and technology
Any technological innovation in the workplace will inevitably need some changes to be made to the way things are currently done. When planned carefully though, the positive transformation can easily outweigh any inconvenience or initial legwork. Whether you’re planning your first AI project or your fiftieth, be sure to consider how it will impact three key areas of the business, and that you’ve taken into account any adaptations that might be needed:
- People: Do you have the right skills in the right parts of the organization to enable you to properly use the insights or efficiencies you’re expecting to create? Who do you need to train? Where do you need to hire? Also, do you have strong support at the executive level? An advocate there can help unlock budget and drive cultural changes where needed from the top down.
- Process: How and where will your new insights impact business processes and workflows (for example, introducing AI to enhance your customer experience will mean digitizing back end processes)? What do you need to update or change to enable this? What impact will these changes have on any other areas like security, compliance or customer experience? Thinking through and properly preparing for these changes ahead of time will save you time and headaches later when you have done your pilot and come to implement your new technology across the business.
- Technology: Which investments will you need to make in order to equip your organization with the analytics capabilities you need to meet your goals? The type of analytics or AI you need will depend on what data you’re using and where it’s coming from, and on the use cases you’re aiming for. Your IT team can help you assess this.
Plan your roadmap
While it may be hard to predict completely what tomorrow’s technology landscape will look like, it’s safe to say that AI is here to stay. As the volume and variety of the data your organization holds continue to grow, and the capabilities of these technologies get stronger, you’ll be able to gain more insights, automate more processes, and be more competitive.
It’s worth thinking about what this journey might look like for you. The good news is that if you’re already doing any sort of BI, you’re already on the right path – it’s now a case of building on what you have in a way that fits with your business strategy. You can learn more about what type of analytics or AI technology are right for you in this planning guide7, or get practical tips on how to implement innovative cloud and analytics solutions in your financial services organization in this Intel white paper.8 9 10
Product and Performance Information
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
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