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The banking and financial services (BFS) industry has typically been thought of as one of the most advanced users of data and analytics. BFS companies were early adopters of data-driven models to do risk modeling and portfolio management, and made the necessary large scale investments over the last twenty years in IT systems and quantitative skills to drive this adoption. So why is it that BFS executives feel like they’re either barely keeping up or completely inundated with data and analytics requirements today?
Our research suggests causes for this gap. More than 80% of 104 BFSI leaders from across business functions believe the value from investments in analytics is primarily in optimizing processes, and 63% of this group stresses that analytics is changing the way that services and processes are managed. But the perceived level of competence today is shocking—only 11% feel like they have the right skills to maximize opportunities with analytics, and a mere 4% have the right technology.
Source: 2014, HfS Research in Conjunction with KPMG, n=104 BFSI services executives
In the course of conducting research for the upcoming HfS BFS Analytics Services Blueprint, we are seeing several themes emerge for how banks are reinvigorating their analytics capabilities. The first is the naming of a Chief Data or Analytics Officer to create a centralized approach for managing analytics that address corporate priorities. This parallels with the development of more centralized competencies, which for some banks means specific centers of excellence for risk management or marketing, or a move toward shared services environments, while others are focusing on technology rationalization.
The main goal for these CDOs/CAOs and the resulting centralization efforts is to promote the sharing of data, analytical models, statistical techniques, broad technology platforms and tools and most importantly, talent across product, functional and regional silos. These initiatives will help prioritize and address the most critical business outcomes that will be impacted at the enterprise level, including maximizing profits (72% agree), managing risk (71% agree), and predicting market changes such as changes in demographic mix, price sensitivity and customer sentiment (67% agree).
The second theme is that talent and technology strategy for analytics is very much still evolving and we have yet to see a dominant operating model emerge, especially given the cost and talent shortage across key client markets today. The head of modeling and insights at a global bank’s card division shared his strategy: “Regulatory challenges and revenue pressures from fintech startups are making us look at analytics differently for segmentation and profitability analysis. Analytics is more the need of the hour than in the past; our functions are being forced [to seek us out].” However, these skills are not readily available, which leads to an increasing willingness of analytics leaders to seek third-party service providers using traditional staff augmentation models, as well as annuity-based ongoing decision support services.
As an example of how partnerships can quickly bring talent and technology to help address the gap, one interviewee said: “Our U.S. risk management function and analytics teams don’t differentiate between ‘us’ and ‘them’ [service provider]. They bring flexibility and access to phenomenal global talent at a great cost…I will absolutely plan my future in-house data and analytics team’s capabilities and career path collaboratively. [Service provider] is an integral piece in our analytics journey.”
Banks may not have all the analytical skills they need in-house, so they are increasingly looking to third-party service providers to become a part of an extended team. We believe this kind of collaborative and more integrated engagement, which brokers capability from across organizations, is the way forward to help banks move back to the vanguard of analytics deployment.