As Covid-19 disruption reigns, embracing technology can no longer be an optional or a sequential process. Bhaskar Sahay discusses why banks need to embark on a continuous, accelerated transformation journey to overhaul their Finance functions.
Banks are facing an ever-increasing appetite to adopt innovative solutions, uncertainty in the marketplace, evolving regulatory requirements, increasing volumes of big data, and reduced capacity across the Finance function. There are a number of key themes that CFOs are focusing on to drive value, as elaborated upon in KPMG’s 2021 Banking Finance Function Benchmarking report .
Strategy and value management
Our recent CIOs survey shows that only 8% of CIOs see ‘Accounting and Finance’ as an investment priority. CFOs should aspire to lead forward-looking Finance functions with an increased focus on creating value for the business by enabling organizational strategies through planning analytics, cost management, and business partnering. Partially ignited by Covid-19, we have observed an increased focus on value creation activities notwithstanding the predominant focus on value protection activities. Undoubtedly, FTE (full-time equivalent) reduction initiatives are mainly targeting areas of value production. The ability to reverse the balance hinges on a higher level of data integration, enabled by high levels of data literacy within Finance function staff. Using finance analytics in mergers and acquisitions can help identify value creation opportunities through the following initiatives:
- Client and product analytics for the combined entity
- Identifying revenue synergies through white space analysis uncovering untapped business opportunities
- Identifying opportunities for cost synergies through effective headcount and product catalogue rationalization
- Developing a consolidated single view of customer relationship management (CRM) data
Banking customers are digitally literate and expect financial institutions will match their tech-savviness through adopting technologies such as low code digital transformation, Machine Learning, Artificial Intelligence (AI), and advanced analytics. KPMG recently bought together CFOs and finance executives to discuss how to leverage disruptive technologies to move their organizations forward. Themes included optimizing the back office through Robotic Process Automation (RPA), AI, and machine learning; capitalizing on data to gain valuable insight; blockchain integration as they foresee new financial operating models unfolding; and customer-centric strategic shifts creating business value and driving sustainable growth. In the absence of a cloud infrastructure, higher IT spending has often indicated an architecture that is not integrated enough and requires human intervention to perform data transformation activities such as reconciliations. Conversely, banks have displayed a higher degree of platform integration, predominantly using single vendor strategies that have resulted in a relatively higher level of IT costs, while also achieving total Cost of Finance (CoF) ratios below 2.25%. Combined with a cloud transition strategy, banks could see CoF ratios drop below 1.5% in the medium term.
Data strategy and governance
With the flux of data within the financial services industry, we have observed new revenue models and data monetization initiatives being developed through external partnerships with the Finance functions at the heart of these agendas. The need for a Finance function foundational data strategy is prominently supported by a culture change to drive and prioritize the deployment of analytics. CFOs and finance executives should consider a data-driven and location-agnostic function where a single data model feeding a series of cloud-enabled engines is utilized, assessing the impact of management decisions and actions in a near real-time fashion.
There is a growing trend for managed services and outsourcing if financial institutions are unable to scale their digital pool of resources. There has also been a rise in organizations offering third-party solutions for Finance functions such as performing end-to-end exceptions’ handling. We have observed a focus on flatter agile structures with outcome driven, digitally-enabled processes that optimize delivery mix and emphasize partnerships. Financial institutions with mature sourcing models have sizeable shared services functions that spread their time in a relatively balanced way between governance, value protection, and value creation activities. We expect these centers to almost halve in size in the future, and predominantly be involved in data quality management and engine configuration.
The modern workforce and ways of working
The dynamics of the business are changing, and new ways of working have evolved. There is a need for a flexible approach that focuses on talent management from within and the acquisition of talent from the sizeable, virtual talent pool. CFOs and Finance functions will become increasingly reliant on data scientists and design professionals who can maintain cloud enterprise architecture and engineer automated reporting and forecasting solutions to optimize end-to-end processes. We foresee the traditional teardrop shape being inverted over the long term; smaller regional banks with limited low-cost sourcing might be able to truncate timelines. At the same time, the skill-set mix will become more balanced and less accounting-oriented. We will see a reduction in junior grades at the back of enterprise-to-enterprise process automation and digital reporting. The focus will be on attracting, managing, and retaining talent through more flexible finance career paths. Spans of control are going to naturally narrow, which is the inverse of what was considered leading practice five years ago. CFOs and finance executives are facing a range of challenges: organization silos, manual data analysis, leadership adjustment, harnessing technology to improve business operations, reducing fraud and waste, an aging baby boomer workforce, achieving clean audit opinion, and budget constraints. Over the past few years, we have seen instances where organizations have started large-scale data lake programs without considering underlying business requirements and data architecture design. This is inadequate without establishing a robust strategic foundation. Anticipating the next normal requires an enterprise-wide design approach that considers all angles of the operating model before deploying use case-driven initiatives.