No Match Found
COVID-19 has severely disrupted the financial services industry, ending a decade-long positive credit cycle and all but guaranteeing that ultra-low interest rates are here for the foreseeable future. The pandemic also has exacerbated existing productivity challenges. Many firms have had increasingly unsustainable cost–income ratios—and if they don’t take action, they’ll face an existential threat.
But the pandemic has presented productivity opportunities, too. Some of them are highlighted by the closure of brick-and-mortar branches and offices due to health concerns, the relative ease with which customers have switched to digital channels, and the successful aspects of remote work.
In our first productivity report, published in 2019, we identified six areas that our survey showed were the focus of most institutions’ productivity efforts. Now, in our second survey, we realise that one of them, improving workforce digital IQ, is integral to and interwoven with all of the others.
Each of the pillars of productivity, as you will see in this report, involves some element of upskilling. Better understanding the workforce, for example, requires the deployment of new measurement and analytical tools. Embracing the platform economy to fully leverage gig work and innovation in crowdsourcing means organising and managing the workforce differently and developing and introducing products in a new way. Making sure your employees are equipped with new skills for a new world will unlock productivity gains across the board and is fundamental to becoming a world-beating institution.
As digitisation becomes ever more critical and technology solutions increasingly involve collaboration with third parties, firms need new capabilities. Most organisations have already digitised to some degree and are seeing productivity gains as a result, but there’s much more that can be done. For one thing, it’s not just technical skills that workers need. They also require training in new ‘soft’ skills, such as agile methods and advanced collaboration techniques.
Our updated survey shows that many firms are taking concerted action to implement this sort of training. They’re also putting in place the resources and technology infrastructure necessary for making productivity gains. In this report, we’ll examine the current state of the market, explore success stories, and lay out the next steps we think you should consider as you craft your productivity agenda for 2021 and beyond.
77% of all organisations track productivity compared to 96% in both China and India
On the surface, the survey results regarding understanding workforce productivity appear encouraging, with a majority of respondents reporting some level of productivity tracking. Yet when you dig deeper, little has changed in the past two years in terms of institutions’ understanding of the detailed tasks their workers actually do every day. Hourly time tracking or periodic time studies are still rare, and just 37% of firms who are not already applying these measures believe that such tracking will improve productivity, down from 63% in our previous survey.
As a result, managerial decisions continue to be made with very little specific data, often by looking at comparable wage rates in different locations and with a cursory knowledge of the activities associated with individual roles. Few institutions are looking comprehensively at the nature of work, the activities that different employees perform, and how individuals can improve their productivity through new ways of working and the development of digital skills. In our view, gaining a better understanding of the workforce represents a major cost reduction opportunity for the industry.
The COVID-19 pandemic and its aftermath have underscored the importance of analysing workforce productivity. Although studies have shown that remote workers are just as productive or more productive than office workers, dealing with a more dispersed workforce does add layers of complexity to the already difficult job managers face in evaluating employees, improving their performance and developing teams. From an employee perspective, the lack of visibility often means that training needs are overlooked, extra work isn’t appreciated, and it is more difficult to separate top performers from the middle of the pack.
$5bn+ organisations are more likely to track productivity at an hourly level (25%) than those under $5bn (11%)
Institutions need to develop a baseline understanding of the activities their people engage in each day, supported by quantitative data. It’s best to start by applying this approach to a small segment of the workforce where the potential gains are most obvious. For many institutions, this could be a problem unit, a certain category of workers (such as contractors) or an area undergoing change. A detailed time study will generate data needed to identify top performers and laggards, improve the organisation of work, and—critically—make it clear which specific actions would increase productivity and engagement.
Respondents in our most recent survey cite several obstacles to consistent and detailed productivity analyses, including a perception that requiring it would cost too much or take up too much of employees’ time. In addition to employer reasons for avoiding detailed tracking, employees might be resistant to workforce analytics for a variety of reasons. For instance, many workers have concerns that productivity tracking information will be used to accelerate their replacement by automation and AI. A 2019 survey shows that 27% of US workers polled fear their jobs will be replaced by technology within the next five years. This is particularly acute in the 18 to 24 age group, where 37% have this fear.
Workforce analytics do lay bare performance and productivity differences amongst employees and teams. However, that information can lead to positive results, including better recognition of high performers, identification of both leading-edge and deficient practices, and better balancing of workloads between teams and individuals. Data also can help managers better align daily work activities to skill levels and experience and help them determine the types of training needed. To ease privacy concerns, organisations can anonymise and aggregate data and ask employees to opt in to the programme.
Change is expensive. The average change budget at a financial services institution represents about 14% of annual operating costs, according to our most recent findings. Almost one-fourth of respondents are spending 21% to 30% of their operating costs on change programmes, and budgets can exceed that for organisations going through challenging periods. According to another survey PwC conducted in 2020, the top three organisational change priorities, ranked by importance, are client and customer satisfaction (cited by 90% of respondents), regulatory compliance (85%) and operational resilience (82%). Moreover, spending on change programmes has continued to increase since our previous survey, despite continued cost pressures and the impact of COVID-19, and is up 5% year-on-year.
Yet in our experience, increased change budgets are not leading to commensurate results, often because spending is not aligned with an institution’s strategic priorities. Worse, firms often have an inflated sense of their ability to implement change. The majority of our most recent survey respondents say they have a good or very good ability to manage and execute change programmes, but PwC analysis shows financial services lagging most other industries in this area. In a post–COVID-19 world, marked by a growing need to accelerate digitisation efforts, right-size businesses, and cut costs in a negative credit and economic environment, institutions need to improve their performance and generate maximum impact from ever-larger change budgets. Digital skills are a key means of achieving these goals.
As we noted in our 2019 report, productivity must remain at the core of the ROI equation for change initiatives. Quality information about time and expense budgets, business benefits, dependencies, deliverables and other metrics—all at the level of individual projects—enables leaders to prioritise and rationalise the change portfolio at any budget level. Even without perfect or comprehensive information, improved analytics can provide enough insight to significantly improve the ROI on change initiatives. For example, upon close examination, many ‘mandatory’ change projects include discretionary components that can be trimmed without compromising the primary objective of the effort. Likewise, deeper analysis often uncovers duplication of technology expenditures across different business units. In our experience, better use of data and analytics can help firms reduce the budget for a change portfolio by up to 20% without losing material benefits.
Talent is a central challenge in implementing change. Many top employees feel that giving up their day-to-day operational roles to help drive a one-time transformation effort is a high-risk, low-reward proposition, leading to long hours, high stress and an uncertain career path once the transformation is complete. Firms are responding to this challenge by offering specialised training, career mentoring and defined secondment programmes, often with a clear commitment to return to the business once the project ends. Consultants continue to be used extensively as a source for high quality and subject matter expertise, although firms could improve knowledge transfer once the consultants have executed their mandate. Beyond talent, perhaps the most significant challenge in implementing change is shaping the right portfolio of change activities at the outset and accurately measuring results over time.
Many of the most valuable companies in the world share one thing in common: They have embraced the platform economy as a business model. They operate with relatively few full-time employees and an increasing percentage of ‘gig economy’ talent that they can access on-demand, making their organisations extremely innovative, nimble and cost-efficient. Beyond cost efficiencies, these platforms make it possible to access the full spectrum of talent, from workers with undifferentiated skills to professionals with highly specialised expertise.
As we discussed in our previous report, the financial services sector can use a platform approach to access ‘new world/new skills’ talent and ideas. The sector has made some progress towards this goal. Among respondents to our most recent survey, 50% say they now use crowdsourcing, up from 21% in our 2019 report. Among those that have already implemented crowdsourcing, the vast majority say it’s generated high or very high value for the organisation.
Next, we expect many financial institutions to become platform companies themselves—facilitating transactions across a wider suite of products and services (including those from other participants on the platform). Mutual fund marketplaces and multi-provider lending and insurance sites are some examples. We believe this trend will continue, pushed forward by some of the forces we describe in our recent report The future of financial services: Securing your tomorrow, today. These include continued low interest rates and margins, the increasing cost of regulated (versus unregulated) capital, and the rise of nonbank lenders and investors in the market.
Leaders in the industry are looking seriously at their workforces to evaluate which roles need to be performed by permanent employees and which can be performed by gig economy workers, contractors or even crowdsourcing. We suggest that all firms follow suit. COVID-19 and remote working have opened the door to accessing talent outside of a firm’s physical location, including outside of the country. Talent platforms provide a clear means to access gig economy talent and related classification and compliance services, and they typically charge fees substantially below those of traditional contracting firms.
Despite increasingly available on-demand talent, most institutions still rely primarily on full-time and part-time employees. But many of our survey respondents say they expect to have more gig-based employees over the next three to five years. We believe this group of employees will likely perform 15% to 20% of the work of a typical institution within five years, driven by continuous cost pressure and the need to access digitally skilled talent.
Getting to this point, though, will require overcoming several obstacles. When it comes to crowdsourcing, the obstacles our survey respondents cite most commonly haven’t changed much since our 2019 report and include confidentiality concerns, a lack of knowledge, regulatory risk and overall risk avoidance. In addition, our experience shows that central reasons for not leveraging platforms more widely are a lack of institutional commitment and deeply ingrained procurement practices, particularly for talent. Supplier arrangements and contracts with larger institutions can literally take years to complete, and it’s almost impossible for new entrants to break down these barriers.
When deciding whether to crowdsource, organisations in China are more likely to cite a lack of knowledge/experience (70%) compared to organisations overall (43%)
Make crowdsourcing and the gig economy part of your productivity and workforce strategy at all levels, from the C-suite to the most junior hires. This push must come from the very top of the organisation to be successful, given general resistance to change. A growing number of partnerships between financial institutions and technology companies (such as the leading cloud providers) will further move financial services in this direction.
Understand which talent and solutions platforms are applicable to your business. This area is so new and growing so quickly that organisations need to establish a baseline and continually update it over time.
Identify the highest-volume or highest-impact work (perhaps starting with 15% to 20% of total work) that would be better served with a gig economy placement, and begin exploring talent platforms and building virtual benches of on-demand talent. Is there a particular transformation or one-time exercise where you can blend in more gig economy workers?
Somewhat surprisingly, our most recent survey shows that the use of agile ways of working actually declined over the past two years. The most common applications are still in information technology, finance and business development. Our work with institutions around the world has given us some insight into why this might be the case. First, some management teams might not be fully committed to the journey. Other management teams don’t always understand how the new approach will create value or improve performance, and they might be uncomfortable working in unconventional ways with greater transparency. In extreme cases, they might actively undermine confidence in agile ways of working by citing examples where peer organisations have failed.
64% of organisations are adopting agile ways of working, down from 77% in 2019
Agile is not a single, monolithic approach. Rather, it can be adapted to the unique business model, culture and ways of working at an organisation, and towards a specific set of objectives, from boosting productivity to increasing employee engagement and creating a better customer experience. Moreover, agile is best implemented in financial institutions when viewed from the enterprise lens, rather than being isolated within a single business or support area. But doing this will result in profound change in the way an institution is organised and operated, so organisations need to build up their capabilities in stages over time.
In addition to the mindset challenges we’ve described that might prevent managers from experimenting with agile ways of working, firms also often try to use standard, out-of-the-box solutions based on rigid frameworks rather than tailoring solutions to meet their specific needs. Some transformations might be too ambitious to succeed—or too cautious to generate meaningful results. Disruptive new technology or ways of working could push teams too far. And some agile initiatives fail due to a lack of momentum. Teams and employees need to see quick wins early on to be persuaded that a project has merit. Without those early successes, projects stall out, and scepticism grows.
‘Digital labour’ encompasses all of the tools and techniques used to replace human labour with technology. According to our most recent survey, artificial intelligence (AI) has passed robotic process automation (RPA) as the most widely used type of automation solution, and PwC’s experience on the ground supports this finding. AI is increasingly being used to drive exponential improvements in productivity and provide unique value to end customers. For example, AI solutions now enable the underwriting of large mortgage loans in minutes, allowing home buyers to walk through a property and make a fully backed offer on the spot—a dramatic improvement over the weeks-long process offered by traditional institutions. AI is also increasingly being used in conjunction with Internet of Things devices to track data as diverse as health factors, driving habits and investor sentiments.
As organisations incorporate AI into more and more areas of the business, regulators and other stakeholders are increasingly focused on topics such as transparency, control, fairness and privacy. The risk is that AI is creating new ‘black boxes,’ where humans are unable to understand the nature of the algorithms and their implications. Do credit-scoring algorithms have hidden biases that discriminate against certain borrowers? Are the algorithms that are monitoring transactions for money laundering able to detect the latest techniques used by drug traffickers and terrorists? Can my AI-based intrusion-detection software cope with the latest threats from hackers, organised crime and national governments? These questions have moved beyond risk and technology functions and into the C-suite, and we are only at the beginning.
The increasing use of the cloud is akin to providing rocket fuel to the use of AI in financial services. In fact, many of the first applications being developed or converted to both the private and public clouds are algorithmic in nature and require large amounts of data and computing power. AI is an area where the cloud providers themselves will lend not only their immense computing power but also considerable expertise.
As these new digital labour solutions become mainstream, you’ll need to apply the same type of rigorous management and control processes to AI and RPA that you have to more traditional automation efforts carried out by the information technology department. This also means that end-user upskilling efforts need to go far beyond simply teaching people to use a tool. The workforce will need a better understanding of control, change management and other elements of the systems development lifecycle. In addition, firms will need to emphasise rigorously testing AI solutions for biases and ensuring that data is collected and used responsibly—both during the development of these applications and after they are rolled out. This means thinking about how data is used, unconscious and conscious biases, data protection and other ethical matters.
In our experience, many clients still lack a rigorous method to determine where digital labour solutions would most benefit their end-to-end processes in terms of improving client satisfaction, reducing cycle time and lowering the number of full-time employees needed. Institutions continue to make educated guesses about where best to implement digital labour, generating improved—but still less than optimal—results. Our most recent survey shows that 30% of respondents cite poor implementation of tech (versus 71% in our 2019 report), and 36% note a lack of a coordinated strategy (versus 59% previously).
Organisations need to be careful that citizen-led automation efforts are both efficient and well-controlled. As the number and complexity of these efforts increase, some executives and control functions fear a repeat of the computing debacle of the early 2000s, when ad hoc automations (mostly Excel micros) led to a series of control failures and information misreporting, with sometimes serious financial and regulatory effects. Proactive institutions are implementing a robust control and change management infrastructure and system of governance to manage this risk. In addition, firms’ increased reliance on algorithms raises questions about transparency, control, fairness and privacy—and regulators and other stakeholders could increasingly scrutinise AI. The shift to cloud-based services, which can put AI applications directly into the hands of consumers in cost-effective ways will only fuel these concerns.
Across all categories of digital solutions, the key question to ask is whether your infrastructure, methodologies and control processes are fit for purpose. The first step is simply to understand the full extent of digital applications your organisation is using, along with the intended roadmap for use.
Consider the full end-to-end lifecycle, from business-case development to implementation to change management, and what additional or different techniques, methodologies, infrastructure and education you need to support digital initiatives. This is key to making sure that automation solutions not only provide short-term productivity benefits but are both sustainable and controllable.
As our survey results and experiences with the world’s leading financial institutions show, there are many ways to address the daunting productivity challenge, but they all share a common foundation. You need to improve the digital IQ of your workforces, along with relevant softer skills. These skills are even more critical in a post–COVID-19 environment. They are, in fact, the decisive factor in increasing productivity on a sustainable basis, which is proving to be one of the key factors in an organisation’s long-term success.
This skills challenge calls for a comprehensive talent strategy and approach, and the execution of specific upskilling efforts that can explicitly demonstrate the tie between investment and improved business outcomes. Without these quantitative results, along with greater employee engagement (which can also be measured), our experience shows that upskilling programmes quickly lose momentum and can ultimately fail. On the other hand, explicitly linking investments to outcomes and capturing benefits typically builds confidence that such efforts deliver real improvements in return on investment and other aspects of performance. This momentum can quickly spread throughout the enterprise.
We hope the many ideas and real-world examples shared here inspire you in your own productivity and upskilling journey.
PwC Research, PwC’s global centre of excellence for market research and insight, conducted this global survey of business executives. For further information on the research, please contact Rachel Surgenor
Global Asset & Wealth Management Leader, Partner, PwC Ireland (Republic of)
Tel: +353 (0) 1 792 8719
Global Insurance Leader, Partner, PwC United Kingdom
Global Banking and Capital Markets Leader, and US Finance Transformation Leader, PwC United States