A new survey of finance professionals found many are unprepared to meet the demands for more insights and information, despite ongoing significant transformation efforts already in place. The national survey from Deloitte’s Centre for Controllership and IMA (The Institute of Management Accountants) discovered that 76% of finance professionals confirm their controllership functions have started their transformation journeys but nearly 95% report additional work is needed and admit progress is slow. A further 65% admit their business function isn’t prepared or only somewhat prepared to meet their future demands. 

The report “Stepping into the future of controllership: from accounting to finance”, explores the impacts of the pandemic on financial services and how finance professionals can use this period to drive innovation within controllership and generate more value for their businesses. 

According to Kyle Cheney, risk and financial advisory partner at Deloitte, a clear lesson from the pandemic is that driving digital functions within controllership is here to stay. Cheney explains that activities previously considered a part of the future of finance, such as data modelling and analytics, are now mainstream parts of the industry. Financial controllers are confident that they need to transform, but this doesn’t change the fact that there are still challenges to overcome on the shift toward an innovative, strategic and digital controllership future. Further data from survey respondents found a mix of responses between the existing and future conditions of controllership, including maturity gaps within vital controllership, enabling and domain areas. Enablers, like governance and compliance, were ranked as the farthest along the maturity continuum by 65% of finance professionals while nearly 50% reported data and analytics to still be in the early stages of maturity. 

Similarly, over 50% of respondents identified financial planning and analysis (FP&A) as a dominant area most in need of progress to meet the future demands of the controllership function. 

Over 60% of surveyed finance professionals agree that advanced maturity levels, or those considered to be integrated or optimised, will be required across enabling and domain areas to achieve the demands of controllership function in the next few years. The report also highlights actions that finance leaders believe will increase their ability to perform in a more innovative, challenging and increasingly digital era. 

Loreal Jile, IMA VP of research and thought leadership and the lead on this study, highlighted that transformation in controllership is more than adopting new technology. It also involves considering how finance teams use that technology to become more strategic partners to the business. Jile explains that the hope is controllers, CFOs, and other finance leaders can use the report as a roadmap to continue progress with their digital plans. The report can enable organisations to structure organisational silos to support intelligent, flexible and more resilient operations capable of managing future industry challenges.

Data-driven finance has become a key priority for finance and accounting teams worldwide. However, a new study suggests that only 23% feel they have the data and analytics potential required to generate real-time insights and deliver the strategies needed for their business.
The report, Behind Every Successful Enterprise, There is Data-Driven Finance explores over 200 finance and accounting leaders to determine their goals for the year ahead. Saurabh Gupta, the president of research and advisory of HFS Research, explains that the study finds that data-driven finance has become the main priority as businesses pursue further growth and profitability. The journey toward this includes many challenges, and most finance leaders feel they lack the tools, technology and talent to progress in this environment. For those that are growing fast and have reached the peak of economic performance, it is clear that focusing their investment in data-driven finance is paying off in creating more flexible operations and repositioning finance from a cost element to a more strategic focus.
Some of the key findings from the report include:

Data-driven finance is the future – Nearly 90% of finance leaders believe that data-driven finance is the future, and 87% agree that they must invest in AI analytics, cloud and digital talent to achieve their data-driven finance targets.

The second key finding from the report is that most finance teams stumble on data maturity. Only 23% of businesses already have mature, data-driven functions, while another 77% believe they are still working on creating a strategy for their financial data and analytics. On average, finance leaders think it will take two years to achieve their data-driven finance goals.

The third finding was that the primary drivers behind finance teams desire for data-driven finance are identifying growth opportunities to support business and become a more strategic advisor, as well as reducing operational costs and improving capital allocation.
Of the fast-developing businesses with higher growth rates, 36% have mature, data-driven finance functions, and more than 30% believe the main driver of their data strategies is the ability to be a strategic advisor for their business. The majority of fast-growing companies are actively developing core centres to improve the management of data and analytics. In contrast, only 23% of mid and slow-moving companies are developing these core areas.

With the current economic and geopolitical conditions, technological disruption and continuous changes in consumer behaviour, the finance function has become a critical area for intelligence and supporting corporate strategies. To capitalise on this intelligence, finance teams require sophisticated data and analytical tools that provide them with real-time insights and the ability to determine varying scenarios. Many emerging companies have managed this feat and applied sophisticated data-driven finance functions, but others still have a long way to go.

A few months ago, one of the most established banking businesses n the world – Lloyds Bank, confirmed it was joining the likes of Google and Uber by delivering a new role – Group Head of Data Ethics. The new position is mainly responsible for the moral and legal obligations for collecting, measuring and protecting significant volumes of data available at Lloyds Bank.

The job posting focused on promoting, embedding and commercialising data and analytics and the culture within the business to drive a data-enabled organisation. If data is the focus to drive business insights, AI is the accelerator that will power this process forward, generating new solutions quicker than previously possible. Conversely, using AI automation on scale without a data ethics representative in a highly regulated industry like finance is similar to beginning a journey without having a map, lacking direction or the ability to shift their course.

The pressure of audits, compliance checks, detailed processes and meeting standards in the financial sector all influence overall success, not just in terms of avoiding regulatory fines but reducing any possible legal implications due to data errors. The challenge comes when the rate of data creation exceeds the ability to process it efficiently.

To appreciate why data ethics is so important, we need to understand the role data has in the finance sector. Combining the complexity and volume of data created today, this surge of information rapidly outpaced the original systems and processes needed to analyse data efficiently. Traditionally, compliance and regulatory processes were managed manually via static spreadsheets. Today, these processes are being streamlined through advanced analytics, making the technologies more accessible and approachable. Analytical automation is a solution to refine large volumes of data effectively and convert it into clear, actionable insights.

Two main tools used within the finance industry are business process automation and automated insight generation from data. Process automation is the ability to take a selected activity, such as extracting data from its source and combining it with data from other areas, and automatically delivering reports. This process generates several benefits for compliance and regulatory requirements and significantly reduces report generation with nearly no errors. These results happen through repeatable, transparent and verified processes completed the same way every time.

The second tool is applying AI for decision-making. AI can determine patterns within data sets to identify fraud or money laundering of specific factors, impacting the ability of an applicant to repay their mortgage. The speed and accuracy benefits that come with automation mean this technology is quickly becoming a vital part of modern finance. The challenge is relatively simple – we alone cannot maintain pace with the continued flow of new data, nor the analytical systems required to make sense of it.

Alongside the rise of new information and an increased focus on compliance and efficiency comes an increased need for humans required to understand and manage that process from end to end. This process requires acquiring the necessary knowledge to deliver the datasets and the correct governance measures to facilitate and improve data quality. Under the GDPR, the requirement for explainability in these processes has become mandatory. Finance businesses, especially those larger established organisations, contain significant sets of valuable data with the incentive to utilise this information. However, the sensitive side of this financial information, creating data standards, and an ethics framework must be the focus before developing these areas. An ethics leader, someone responsible for maintaining human benefit and ethics lies at the core of AI innovation, is a critical part of delivering growth and generating the value expected from AI automation.
Instead of assuming AI will deliver the best insights, it’s critical to understand how and why the results are as they seem. Focusing on this ethical approach and ensuring wider adoption is vital to the role of an ethics leader.

While AI can perform many tasks without human involvement, it is critical that those creating, operating and making the decisions completely understand any potential errors before AI replicates them. With the ethical and governance-based foundation, finance teams will potentially automate bad decisions faster. Training, testing and continuous monitoring and vital to success. Training data applied to AI systems must not include bias to ensure no bias is replicated at a later stage.

While ethics professionals must manage the best possible practice, ethical AI requires a holistic approach to data literacy and ethics. These go together when creating and implementing assured AI, capable of supporting and complimenting human capabilities. A recent study by Alteryx discovered that 42% of UK employees responsible for data work saw data ethics as irrelevant to their role. The reality is, however, that data integrity and transparent decision-making are critical for any success in AI-focused insight generation.