Banks Should Focus on Core Competencies, Not Tech, in Their Digital Transformation

(Guest blogger, Jerry Silva, is research vice president at IDC Financial Insights.)

银行业已经更多的潜力hange through technological innovation in the last three to five years than it has in the last three to five decades.

Spurred by regulatory demands, increased customer expectations, and competitive pressures, normally deliberate financial institutions are embracing technologies like cloud, artificial intelligence (AI), mobility, and even quantum computing in order to maintain their relationships with consumers and retain their position as trusted and convenient financial partners.

Tomorrow’s bank experience will look deceptively easier and more in line with the customer’s lifestyle—faster, more convenient and responsive, and customers may be a bit surprised when their bank is there at exactly the right moment when they’re needed. But behind the wall, the changes will be fundamental, essential, and truly transformative. As science fiction author Arthur C. Clarke once observed, “Any sufficiently advanced technology is indistinguishable from magic.” To the customer’s eyes, their bank’s improved responsiveness and convenience will belie the enormous work taking place today to make that future possible.

I believe that one of the key characteristics of transformation is represented by a philosophy I used to hear quite a bit in the 1990s: to stick to core competencies. Banks that seek to leverage technology more efficiently, but don’t strive to be technology firms themselves, will be best positioned to deliver the future of financial services.

I say this for a number of reasons. After speaking with technology executives at banks both large and small, it’s apparent that there is a real challenge in attracting and retaining the staffing required to develop data lakes, AI-enabled analytics, cloud environments, and next generation security solutions, just to name a few—but very critical—areas of transformation. Banks focused on becoming technology-first organizations run the risk of losing sight that they are, first and foremost, financial institutions.

The other reason I believe in the power of core competencies today is that many banks are overly focused on IT transformation instead of business innovation. For example, while open banking is growing in Europe because of regulatory guidance, it lags in the Americas, where the potential of innovative products and services based on customer journeys is lacking in attention.

Finally, I say this because I’m cheating: I already had the answers to the test, as it were. In IDC’s 2019 CloudPath survey of adoption of cloud technology by banks globally, the institutions told us that’s exactly what they’re thinking when three of their top five expectations of cloud are business-related. Figure 1 shows the top five expectations banks have in adopting public cloud.

While in the evaluation process, which of the following benefits did you expect to achieve from your organization’s public cloud purchasing, usage, and strategy?

This attitude toward a more business-led IT strategy is evident in other areas, like data and analytics, where investments in 2019 will top $25 billion at banks worldwide. Even mission-critical and data-heavy applications like finance and business planning are being deployed on cloud environments. In fact, the CloudPath survey shows that over 60 percent of the banks in the survey are already running finance applications on either private or public cloud. And a further 18 percent anticipate moving finance workloads to the cloud within 24 months.

I mention this area of the bank specifically as the goal of digital transformation will require close attention to business planning and the financial implications of change. Moving from on-premise data centers to cloud-based software-as-a-service changes the balance between capital and operational expenses but introduces third party relationships that must be managed. Open banking will enable innovation using non-bank partners to create profoundly different products and services, but the bank must examine those opportunities in the context of their markets and value.

The good news is that another set of technologies, artificial intelligence and machine learning (AI/ML), is aiding both the analysis of business data and the processes needed to execute on business strategy. And even better, AI/ML are inherently deployed as part of many cloud-based solutions. In fact, 85 percent of banks in the CloudPath survey report that they consider AI/ML as “important” or “very important” in choosing a cloud service provider.

Importantly, the combination of these three technologies: cloud, data/analytics, and AI/ML, will be key to the transformation of the banking industry and its future. And business planning, including finance, is an essential area that institutions should consider for early adoption of these tools.

Essential Guidance

mov的ement to off-premise deployment models for mission-critical workloads will continue to grow significantly, as evidenced by double-digit growth in cloud, data, analytics, and AI/ML spending by banks worldwide. Institutions that don’t yet have plans to take advantage of these transformative technologies will lag behind their competitors.

The return to business-led IT strategy is driving decisions on technology transformation, with a focus on improving performance, increasing efficiencies, and enabling business innovation. Digital transformation is often thought of as a technology initiative, where it is, in fact, a transformation of culture and vision, led necessarily by the bank’s business leaders.

IDC Financial Insights believes that cloud providers have an opportunity to support business transformation in banking worldwide by providing the tools, technologies, and deployment models that allow the bank’s leaders to focus on the business of banking and not on the technology.

I believe it’s time for banks to get back to the business of banking, and revolutionize the industry through the use of transformative technology and smarter deployment models.