How the Distinct Roles of Internal Audit and the Finance Function Drive Good Governance

How the Distinct Roles of Internal Audit and the Finance Function Drive Good Governance

Effective governance involves many individuals and departments throughout an organization, including the Board of Directors, executive management, finance, and internal audit, among others. Yet each of these groups has a different set of skills and responsibilities. To successfully identify and manage risk, they must come together to create and maintain a sound system of corporate governance.

The insights shared here by 11 governance experts offer important perspective as to how finance and internal audit collaborate to support corporate governance, despite their distinct and separate missions.

Interviewees provided perceptions and experiences and shared best practices, as well as challenges, that they have encountered on their quest to achieve effective governance. These contributors come from organizations around the world that differ in size, industry, and management configurations. Several experienced governance from within both the finance function and internal audit.

A few shared perceptions include:

  • The Board of Directors is responsible for setting the proper tone for the organization;
  • It is critical to purposefully develop a consistent culture throughout the organization, driven by the CEO and senior management; and
  • Communication and coordination across complementary functions is vital.

Keys To Achieving Good Governance

There are many different definitions of governance. According to The Institute of Internal Auditors (hereafter The IIA), governance is “the combination of processes and structures implemented by the board in order to inform, direct, manage and monitor the activities of the organization toward the achievement of its objectives.

The International Federation of Accountants (hereafter IFAC) uses a slightly different definition which focuses more on the creation of strategic objectives and stakeholder value, “Governance is to create and optimize sustainable organizational success and stakeholder value, balancing the interests of the various stakeholders. It comprises arrangements put in place to ensure that organizations define and achieve intended outcomes.

Both definitions suggest that good governance and the achievement of organizational success are not the responsibility of the Board alone, but rather the outcome of a mosaic of organizational policies, processes, and cross-functional interactions.

When asked to provide the key objectives of governance, interviewees shared a number of different perspectives. Most frequently, good governance was defined as representing the interests of stakeholders by setting appropriate objectives and driving a culture that supports them.

Three LoD

Click here to acces IFAC and IIA’s detailed article

The Future of Planning Budgeting and Forecasting

The world of planning, budgeting and forecasting is changing rapidly as new technologies emerge, but the actual pace of change within the finance departments of most organizations is rather more sluggish. The progress companies have made in the year since The Future of Planning, Budgeting and Forecasting 2016 has been incremental, with a little accuracy gained but very little change to the reliance on insight-limiting technologies like spreadsheets.

That said, CFOs and senior finance executives are beginning to recognize the factors that contribute to forecasting excellence, and there is a groundswell of support for change. They’ll even make time to do it, and we all know how precious a CFOs time can be, especially when basic improvements like automation and standardization haven’t yet been implemented.

The survey shows that most PBF functions are still using relatively basic tools, but it also highlights the positive difference more advanced technology like visualization techniques and charting can make to forecasting outcomes. For the early adopters of even more experimental technologies like machine learning and artificial intelligence, there is some benefit to being at the forefront of technological change. But the survey suggests that there is still some way to go before machines take over the planning, budgeting and forecasting function.

In the meantime, senior finance executives who are already delivering a respected, inclusive and strategic PBF service need to focus on becoming more insightful, which means using smart technologies in concert with non-financial data to deliver accurate, timely, long term forecasts that add real value to the business.

Making headway

CFOs are making incremental headway in improving their planning, budgeting and forecasting processes, reforecasting more frequently to improve accuracy. But spreadsheet use remains a substantial drag on process improvements, despite organizations increasingly looking towards new technologies to progress the PBF landscape.

That said, respondents seem open to change, recognizing the importance of financial planning and analysis as a separate discipline, which will help channel resources in that direction. At the moment, a slow and steady approach is enough to remain competitive, but as more companies make increasingly substantial changes to their PBF processes to generate better insight, those that fail to speed up will find they fall behind.

Leading the debate

FSN’s insights gleaned from across the finance function shed light on the changes happening within the planning, budgeting and forecasting function, and identify the processes that make a real difference to outcomes. Senior finance executives are taking heed of these insights and making changes within the finance function. The most important one is the increasing inclusion of non-financial data into forecasting and planning processes. The Future of The Finance Function 2016 identified this as a game-changer, for the finance function as a whole, and for PBF in particular. It is starting to happen now. Companies are looking towards data from functions outside of finance, like customer relationship management systems and other non-financial data sources.

Senior executives are also finally recognizing the importance of automation and standardization as the key to building a strong PBF foundation. Last year it languished near the bottom of CFO’s priority lists, but now it is at the top. With the right foundation, PBF can start to take advantage of the new technology that will improve forecasting outcomes, particularly in the cloud.

There is increasing maturity in the recognition of cloud solution benefits, beyond just cost, towards agility and scalability. With recognition comes implementation, and it is hoped that uptake of these technologies will follow with greater momentum.

Man vs machine

Cloud computing has enabled the growth of machine learning and artificial intelligence solutions, and we see these being embedded into our daily lives, in our cars, personal digital assistants and home appliances. In the workplace, machine learning tools are being used for

  • predictive maintenance,
  • fraud detection,
  • customer personalization
  • and automating finance processes.

In the planning, budgeting and forecasting function, machine learning tools can take data over time, apply parameters to the analysis, and then learn from the outcomes to improve forecasts.

On the face of it, machine learning appears to be a game changer, adding unbiased logic and immeasurable processing power to the forecasting process, but the survey doesn’t show a substantial improvement in forecasting outcomes for organizations that use experimental technologies like these. And the CFOs and senior finance executives who responded to the survey believe there are substantial limitations to the effective of machine forecasts. As the technology matures, and finance functions become more integrated, machine learning will proliferate, but right now it remains the domain of early adopters.

Analytic tools

Many of the cloud solutions for planning, budgeting and forecasting involve advanced analytic tools, from visualization techniques to machine learning. Yet the majority of respondents still use basic spreadsheets, pivot tables and business intelligence tools to mine their data for forecasting insight. But they need to be upgrading their toolbox.

The survey identifies users of cutting edge visualization tools as the most effective forecasters. They are more likely to utilize specialist PBF systems, and have an arsenal of PBF technology they have prioritized for implementation in the next three years to improve their forecasts.

Even experimental organizations that aren’t yet able to harness the full power of machine learning and AI, are still generating better forecasts than the analytic novices.

The survey results are clear, advanced analytics must become the new baseline technology, it is no longer enough on rely on simple spreadsheets and pivot tables when your competitors are several steps ahead.

Insight – the top trump

But technology can’t operate in isolation. Cutting edge tools alone won’t provide the in-depth insight that is needed to properly compete against nimble start-ups. CFOs must ensure their PBF processes are inclusive, drawing input from outside the financial bubble to build a rounded view of the organization. This will engender respect for the PBF outcomes and align them with the strategic direction of the business.

Most importantly though, organizations need to promote an insightful planning, budgeting and forecasting function, by using advanced analytic techniques and tools, coupled with a broad data pool, to reveal unexpected insights and pathways that lead to better business performance.

As FSN stated, today’s finance organizations are looking to:

  • provide in-depth insights;
  • anticipate change and;
  • verify business opportunities before they become apparent to competitors.

But AI and machine learning technologies are still too immature. And spreadsheet-based processes don’t have the necessary functions to fill these advanced needs. While some might argue that spreadsheet-based processes could work for small businesses, they become unmanageable as companies grow.

PBF

Click here to access Wolters Kluwers FSN detailed survey report

The IFRS 9 Impairment Model and its Interaction with the Basel Framework

In the wake of the 2008 financial crisis, the International Accounting Standards Board (IASB) in cooperation with the Financial Accounting Standards Board (FASB) launched a project to address the weaknesses of both International Accounting Standard (IAS) 39 and the US generally accepted accounting principles (GAAP), which had been the international standards for determining financial assets and liabilities accounting in financial statements since 2001.

By July 2014, the IASB finalized and published its new International Financial Reporting Standard (IFRS) 9 methodology, to be implemented by January 1, 2018 (with the standard available for early adoption). IFRS 9 will cover financial organizations across Europe, the Middle East, Asia, Africa, Oceana, and the Americas (excluding the US). For financial assets that fall within the scope of the IFRS 9 impairment approach, the impairment accounting expresses a financial asset’s expected credit loss as the projected present value of the estimated cash shortfalls over the expected life of the asset. Expected losses may be considered on either a 12-month or lifetime basis, depending on the level of credit risk associated with the asset, and should be reassessed at each reporting date. The projected value is then recognized in the profit and loss (P&L) statement.

Most banks subject to IFRS 9 are also subject to Basel III Accord capital requirements and, to calculate credit risk-weighted assets, use either standardized or internal ratings-based approaches. The new IFRS 9 provisions will impact the P&L that in turn needs to be reflected in the calculation for impairment provisions for regulatory capital. The infrastructure to calculate and report on expected loss drivers of capital adequacy is already in place. The data, models, and processes used today in the Basel framework can in some instances be used for IFRS 9 provision modeling, albeit with significant adjustments. Not surprisingly, a Moody’s Analytics survey conducted with 28 banks found that more than 40% of respondents planned to integrate IFRS 9 requirements into their Basel infrastructure.

Arguably the biggest change brought by IFRS 9 is incorporation of credit risk data into an accounting and therefore financial reporting process. Essentially, a new kind of interaction between finance and risk functions at the organization level is needed, and these functions will in turn impact data management processes. The implementation of the IFRS 9 impairment model challenges the way risk and finance data analytics are defined, used, and governed throughout an institution. IFRS 9 is not the only driver of this change.

Basel Committee recommendations, European Banking Authority (EBA) guidelines and consultation papers, and specific supervisory exercises, such as stress testing and Internal Capital Adequacy Assessment Process (ICAAP), are forcing firms to consider a more data-driven and forward-looking approach in risk management and financial reporting.

Accounting and Risk Management: An Organization and Cultural Perspective

The implementation of IFRS 9 processes that touch on both finance and risk functions creates the need to take into account differences in culture, as well as often different understandings of the concept of loss in the two functions.

  • The finance function is focused on product (i.e., internal reporting based on internal data) and is driven by accounting standards.
  • The risk function, however, is focused on the counterparty (i.e., probability of default) and is driven by a different set of regulations and guidelines.

This difference in focus leads the two functions to adopt these differing approaches when dealing with impairment:

  • The risk function uses a stochastic approach to model losses, and a database to store data and run the calculations.
  • Finance uses arithmetical operations to report the expected/ incurred losses on the P&L, and uses decentralized data to populate reporting templates.

In other words, finance is driven by economics, and risk by statistical analysis. Thus, the concept of loss differs between teams or groups: A finance team views it as part of a process and analyzes loss in isolation from other variables, while the risk team sees loss as absolute and objectively observable with an aggregated view.

IFRS 9 requires a cross-functional approach, highlighting the need to reconcile risk and finance methodologies.

The data from finance in combination with the credit risk models from risk should drive the process.

  • The risk function runs the impairment calculation, whilst providing objective, independent, and challenger views (risk has no P&L or bonus-driven incentive) to the business assumptions.
  • Finance supports the process by providing data and qualitative overlay.

Credit Risk Modeling and IFRS 9 Impairment Model

Considering concurrent requirements across a range of regulatory guidelines, such as stress testing, and reporting requirements, such as common reporting (COREP) and financial reporting (FINREP), the challenge around the IFRS 9 impairment model is two-fold:

  • Models: How to harness the current Basel-prescribed credit risk models to make them compliant with the IFRS 9 impairment model.
  • Data: How (and whether) the data captured for Basel capital calculation can be used to model expected credit losses under IFRS 9.

IFRS9 Basel3

Click here to access Moody’s detailed report