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Navigating the new world – Preparing for insurance accounting change (IFRS 17)

If implementation of the forthcoming insurance contracts standard is to reach the best possible outcome for your organization, we believe it needs to be seen as more than just a compliance exercise. This will entail

  • combining multiple strands into a common program,
  • identifying linkages
  • and addressing dependencies

across the business in a logical sequence and thinking strategically about possible effects on the organization and its stakeholders. A well-developed and ‘living’ plan assigns clear accountabilities and breaks down objectives into manageable tasks for delivery to realistic time-scales in order to establish an effective blue-print for success.

Our methodology groups activities into four manageable phases:

  1. assess the change
  2. design your response
  3. implement your design
  4. sustain your new practices, securely embedding them in business as usual.

Key success factors

Our experience shows us there are many factors that will contribute to successfully implementing insurance accounting change, including:

  1. Dedicated staff: In our experience the single biggest factor contributing to program success is the presence of full-time staff dedicated to the project, with a wide range of skills including data management, IT implementation and project management and who know your business.
  2. Spend sufficient time and energy on the initial impact phase: It is essential that an insurer plans for this critical phase and allows for sufficient time to perform a gap analysis on a line-by-line basis through the income statement and balance sheet and supports disclosures.
  3. Consider fundamental questions surrounding core business drivers: earnings trends, growth opportunities and target operating models. The earlier effects are identified, the more time an insurer will have to develop and implement a strategic response.
  4. Training staff: Many organizations underestimate the amount of personnel training required. Designing a comprehensive training strategy and program is highly complex and requires careful planning.
  5. Robust project planning: The plan must be achievable and continuously refined with formal tracking and monitoring.
  6. Clear communications: Communication needs to be both formal and informal and applied throughout the life of the program.
  7. Careful change management: IFRS conversion will lead to significant changes in how people do their jobs. Some of the biggest challenges have arisen when the cultural issues have not been acknowledged and addressed.
  8. More than just an accounting and actuarial project: Implementing the forthcoming insurance contracts project will undoubtedly be a multi-disciplinary effort.
    1. IT specialists consider the functionality of source systems and enterprise performance management (EPM) systems;
    2. Change management specialists focus on behavioral change and communication;
    3. specialists in commercial functions (tax, data management, executive incentives, etc.) bring a holistic approach to the program.

Robust project management helps to bring everything together coherently.

Assessing what the forthcoming standard will mean for you

Accounting, actuarial, tax and reporting

Q1. What are the key accounting, actuarial, tax and disclosure differences between our current generally accepted accounting principles (GAAP) and the new standards? What are the key decisions that need to be made by management regarding the alternative treatments that are available?

Data, systems and processes

Q2. What will the impact be for our data requirements, and on the systems and processes used for

  • data collection,
  • actuarial projections,
  • calculating and accruing interest on the contractual service margin
  • and consolidation and financial reporting systems?

Are there quick fixes that we can use? Can we leverage recent investments in infrastructure or will we need a major overhaul?

Q3. How will the group‘s close and other processes be impacted?

Business

Q4. What is the estimated directional impact on profit and equity and what are the key decisions and judgments that this will influence?

Q5. What are the key impacts for my business and how will these be influenced by the choices open to us? Who will need to understand results and metrics on the new basis?

People and change management

Q6. Who will be impacted by the conversion, what skills and resources are likely to be needed and what training needs can we identify?

Program management

Q7. What would a high-level conversion plan look like and what is its likely impact on resources?

IFRS17 3

Click here to access KPMG’s methodology paper

Accelerated evolution – M&A, transformation and innovation in the insurance industry

Strong appetite for deal activity

Today’s insurers know that maintaining the ‘status quo’ is not a recipe for sustainable growth. They feel the pressure of disruption in the market from

  • new competitors,
  • new technologies,
  • new customer demands
  • and new sources of capital.

They feel the pain of

  • continued low interest rates,
  • volatility in underwriting losses
  • and pressure on profitability,

as investment portfolio yields continue to decline.

Organic growth has been challenging across most of the mature insurance markets. Consider this: Since the start of this decade to 2016, global gross domestic product (GDP) increased by more than 20 percent. Yet the global premium market grew by just 9 percent over the same period. Insurers recognize that things must change if they want to maintain or grow their market share.

“In an era of anticipated disruption of legacy business and operating models, global insurance executives realize that their strategy cannot be about pursuing growth for growth’s sake. When it comes to growth strategy, more of the same is not necessarily the best answer. What may have been a core business in the past may not be in the future,” notes Ram Menon, KPMG’s Global Insurance Deal Advisory Leader.

Today’s insurance leaders are taking a more strategic view of the value of M&A. According to a recent global survey of 115 insurance CEOs conducted by KPMG International, more than 60 percent of insurers now see disruption as more of an opportunity for growth than a threat. And they are using their capital and their M&A capabilities to maximize those opportunities — often by strategically deploying capital towards emerging technology as a competitive advantage to

  • engage customers,
  • generate cash flows
  • and enhance enterprise value.

The good news is that — for the most part — capital and surplus levels are at record highs across life, non-life and reinsurance markets. And most insurers plan to tap into that capital to make deals. In fact, our survey suggests that close to three-quarters of insurers expect to conduct an acquisition and two-thirds expect to seek partnership opportunities over the next 3 years. Eighty-one percent say they will conduct up to three acquisitions or partnerships in the same period. More than 70 percent said they are hoping their deals will help transform their organization in some way. As a top priority,

  • 37 percent hope to transform their business models,
  • 24 percent want to transform their operating models,
  • and 10 percent are looking to acquire new innovation capabilities and emerging technologies

through their acquisitions.

“Insurers increasingly recognize their days of operating business-as-usual numbered. And it’s not small changes market going to be undoing — big ones,” says Thomas Gross with KPMG Germany. Auto insurers, for example, looking at rapid adoption of mobility models and wondering how they add value when car manufacturers or leasers own relationship customer.”

On their path to transformation, insurance companies expect to strategically deploy capital against a range of specific inorganic growth opportunities:

  • transforming their business models for sustainable growth;
  • modernizing their operating models for profitable growth;
  • enhancing customer engagement;
  • and gaining access to innovation and emerging technologies.

“The top factor that will drive insurance acquisitions will be the need for emerging technologies. Insurance companies are all looking at how to put their operations on digital platforms in order to save time and resources both for the company and the customers,” notes the Head of Finance at a China-based property and casualty (P&C) insurer. At the same time, a significant number of insurers also hope to rebalance their portfolio of businesses. Many plan to evaluate whether they should fix or exit businesses that are struggling to achieve returns in excess of their longterm capital rates. This should allow them to remain focused on transforming businesses they consider core for the future while freeing up additional capital for reinvestment into new lines of business and technology capabilities.

As the director of finance at a UK-based non-life insurer notes, “Units that are consistently performing poorly will be segregated to further analyze their positions and whether or not they still fit in the company’s planned structure. We discourage force-fitting any product or company unless it has great potential for generating revenue. If it does not, we look for suitable buyers for the business.”

Our data indicates, insurance executives expect to exit non-core businesses, enter new markets and gain access to new technology infrastructure and operating capabilities via M&A and partnerships, as a way to further diversify their global risks and earnings profile.

Looking beyond the borders

Our survey suggests that the majority of insurers will be involved in some sort of non-domestic deal: 68 percent say they expect to conduct a cross-border acquisition, partnership or divestiture over the next 3 years. Just 32 percent say their top priority will be on domestic activity.

“Over a period of 3 years, we expect to see a lot of M&A transactions overseas. We are looking to expand into regions that are new for us and with acquisitions, you can get going without having to set up a base from scratch or encounter a lot of unforeseen risks,” notes the senior VP for M&A at a global insurance brokerage firm. Perhaps not surprisingly, our data suggests that insurers expect to see the most activity in North America — the US in particular. Given that the US is still the largest insurance market in the world with around 30 percent of the global premium market share, many insurers see the US as a source of steady market growth and relative premium stability.

“The volume of M&A in North America will increase the most in the coming years. With the new tax reforms, insurance companies will pay lower taxes — these new regulations will provide insurers opportunities to grow. Companies from other markets will also want to take advantage of the lower tax rate and will look for ways to expand into the US market,” suggested the CFO at a Bermuda-based reinsurer. Changes to US tax laws will certainly create significant disruption and opportunity for insurers both onshore and offshore. “The reduction in the corporate tax rate to 21 percent makes US assets much more compelling,” notes Philip Jacobs, leader of the Insurance Tax practice with KPMG in the US. “The lower US tax rate has also eliminated some of the offshore tax advantage; the large Bermuda players may still be operating with relatively low effective rates, but the tax differential between operating in the US versus Bermuda has narrowed.”

Latin America, however, expects relatively lower levels of deal activity. “It’s a sellers’ market in Latin America,” notes David Bunce, Senior Client Partner with KPMG in Brazil. “Lots of international insurers want to get into certain Latin American markets, but nobody is really ready to sell.”

At the other end of the spectrum — and the other side of the world — Asia-Pacific is widely viewed as a region of massive growth potential and innovation. China has already become the world’s second largest insurance market (with around 10 percent of
global premium market share) and premiums have more than doubled since 2010. Singapore and Hong Kong have long been key centers of insurance innovation growth.

Asia-Pacific was identified as the geographic region where insurers would most likely seek partnership opportunities. “As insurers seek to expand outside of their traditional distribution networks in Asia, digital partnerships are emerging as a fairly quick way to tap into new customer segments without significant upfront capital investment,” adds Joan Wong with KPMG China. “A digital partnership could unlock significant new growth, which would tip the balance for those making a ‘go or grow’ decision about their businesses.”

The director of investment at a Korea-based international insurer agrees. “Asia has become one of the biggest markets for insurers, and the region’s growing population along with changes in capital regulations will give insurers the backing they need to grow. In China alone we have seen a major increase in the number of companies seeking out new ventures in the insurance sector.”

While the majority of our respondents say they are looking across their borders for growth, those in Asia-Pacific are much more likely to be focused on domestic acquisitions instead. “Most of the markets in Asia are still fairly domestically oriented and there is still significant fragmentation and inefficiency that could be eliminated,” adds Stephen Bates with KPMG in Singapore. “Given the growth potential across the region, it’s not surprising that Asian insurers are thinking about taking advantage of opportunities at home before investing further into foreign markets.”

Somewhat tellingly, insurers expect most of the divestiture activity to originate from Western Europe. As the head of finance and investments at a large French insurer argues, “The persistent compression in global interest rates continues to be a challenge for the insurance industry, and many companies in Europe are aiming to divest in part to cope with this. When you add in the factors of changing regulation and customer demographics, it means that insurance business models have evolved and companies are reshaping themselves accordingly.”

“Insurers in Europe are very interested in diversifying their risk and see adjacent markets as an opportunity to do just that,” notes Giuseppe Rossano Latorre, Head of Corporate Finance at KPMG in Italy. “There are a number of life insurers that are looking at the asset management business, for example, as a potential growth opportunity in the future.”

Our data indicates that in the Life sector, acquisitions will likely focus on finding lower-risk, higher-growth, higher-return assets, particularly around capital-light retirement, investment management and group benefits businesses. However, greater levels of activity should be expected in the Nonlife sector, driven by a growing appetite for more profitable specialty risks and commercial risks, with a preference for commercial risk in the small- and medium-sized enterprise (SME) sector.

What this survey makes clear is that global insurance companies recognize they now have a window of opportunity to strategically allocate their capital across the globe towards achieving and accelerating their transformation strategy.

MandA_Innovation

Click here to access KPMG’s detailed study

EIOPA: Potential macroprudential tools and measures to enhance the current insurance regulatory framework

The European Insurance and Occupational Pensions Authority (EIOPA) initiated in 2017 the publication of a series of papers on systemic risk and macroprudential policy in insurance. So far, most of the discussions concerning macroprudential policy have focused on the banking sector. The aim of EIOPA is to contribute to the debate, whilst taking into consideration the specific nature of the insurance business.

With this purpose, EIOPA has followed a step-by-step approach, seeking to address the following questions:

  • Does insurance create or amplify systemic risk?
  • If yes, what are the tools already existing in the current framework, and how do they contribute to mitigate the sources of systemic risk?
  • Are other tools needed and, if yes, which ones could be promoted?

While the two first questions were addressed in previous papers, the purpose of the present paper is to identify, classify and provide a preliminary assessment of potential additional tools and measures to enhance the current framework in the EU from a macroprudential perspective.

EIOPA carried out an analysis focusing on four categories of tools:

  1. Capital and reserving-based tools;
  2. Liquidity-based tools;
  3. Exposure-based tools; and
  4. Pre-emptive planning.

EIOPA also considers whether the tools should be used for enhanced reporting and monitoring or as intervention power. Following this preliminary analysis, EIOPA concludes the following (Table 1):

Table 1 Macro

It is important to stress that the paper essentially focuses on whether a specific instrument should or should not be further considered. This is an important aspect in light of future work in the context of the Solvency II review. As such, this work should be understood as a first step of the process and not as a formal proposal yet. Furthermore, EIOPA is aware that the implementation of tools also has important challenges. In this respect this report provides an overview of tools, main conclusions and observations, stressing also the main challenges.

Table 2 puts together the findings of all three papers published by EIOPA by linking

  1. sources of systemic risk and operational objectives (first paper),
  2. tools already available in the current framework (second paper)
  3. and other potential tools and measures to be further considered (current paper).

Table 2 Papers

The first paper, ‘Systemic risk and macroprudential policy in insurance’ aimed at identifying and analysing the sources of systemic risk in insurance from a conceptual point of view and at developing a macroprudential framework specifically designed for the insurance sector.

The second paper, ‘Solvency II tools with macroprudential impact’, identified, classified and provided a preliminary assessment of the tools or measures already existing within the Solvency II framework, which could mitigate any of the sources of systemic risk.

This third paper carries out an initial assessment of potential tools or measures to be included in a macroprudential framework designed for insurers, in order to mitigate the sources of systemic risk and contribute to the achievement of the operational objectives.

It covers six main issues:

  1. Identification of potential new instruments/measures. The tools will be grouped according to the following blocks:
    • Capital and reserving-based tools
    • Liquidity-based tools
    • Exposure-based tools
    • Pre-emptive planning
  2. Way in which the tools in each block contribute to achieving one or more of the operational objectives identified in previous papers.
  3. Interaction with Solvency II.
  4. Individual description of all the tools identified for each of the blocks. The following classification will be considered:
    • Enhanced reporting and monitoring tools and measures. They provide supervisors and other authorities with additional relevant information about potential risks and vulnerabilities that are or could be building up in the system. Authorities could then implement an array of measures to address them both at micro and macroprudential level (see annex for an inventory of powers potentially available to national supervisory authorities (NSAs)).
    • Intervention powers. These powers are currently not available as macroprudential tools. They are more intrusive and intervene more severely in the management of the companies. Examples could be additional buffers, limits or restrictions. They are only justified where the existing measures may not suffice to address the sources of systemic risk identified.
  5. Preliminary analysis per tool.
  6. Preliminary conclusion.

Four initial remarks should be made.

  1. First, although in several instances the measures and instruments are originally microprudential in nature, they could also be implemented as macroprudential instruments, if a systemically important institution or set of institutions or the whole market are targeted.
  2. Secondly, analysing potential changes on the long-term guarantees (LTG) measures and measures on equity risk that were introduced in the Solvency II directive, although out of the scope of this paper, could contribute to further enhance the framework from a macroprudential perspective. The focus of this paper is essentially on new tools, leaving aside the analysis of potential changes in the current LTG measures and measures on equity risk, which will be carried out in the context of the Solvency II review by 1 January 2021.
  3. Thirdly, when used as a macroprudential tool, the decision process may differ, given that there are different institutional models for the implementation of macroprudential policies across EU countries, in some cases involving different parties (e.g. ministries, supervisors, etc.). This paper seeks to adopt a neutral approach by referring to the concept of the ‘relevant authority in charge of the macroprudential authority’, which should encompass the different institutional models existing across jurisdictions.
  4. Fourthly, there seems to be no single solution when it comes to the level of application of each tool (single vs. group level).

Concerning the different proposed monitoring tools, in the follow-up work, the structure and content of the additional data requirements should be defined. This should then be followed by an assessment of the potential burden of collecting this information from undertakings.

It is important to stress that this paper essentially focuses on whether a specific instrument should or should not be further considered. This is an important aspect in light of future work in the context of the Solvency II review. As such, this work should be understood as a first step of the process and not as a formal proposal yet.

Figure ORSA

Click here to access EIOPA’s detailed discussion paper

Failures and near misses in insurance – Overview of the causes and early identification

General approach

The approach to dealing with failures of financial institutions has witnessed significant changes since the eruption of the financial crisis in 2008, both from the crisis prevention and the crisis management perspective. A changing perspective in the interpretation of the causes, early identification and corrective measures used in the context of (near) failures may create difficulties when trying to compare past failures with current ones, particularly with the advent of recovery and resolution frameworks in finance.

EIOPA has developed its own conceptual approach, which is followed throughout this report. It should be stressed that there is not a conceptual approach which is universally agreed. The aim of the present chapter is to explain the approach followed by EIOPA, in order to achieve a common understanding and support the classification of the different cases of insurance failures and near misses.

This chapter focuses on the following two issues:

  • The definition of the concepts of “failure” and “near miss”, which are essential to understanding the database construction process and the scope of the cases to be included.
  • The need to have a common understanding of the framework for crisis prevention and management, as well as the recovery and resolution tools to be used.

In terms of crisis prevention and management, the fundamental approach followed by EIOPA can be understood as part of a continuum of supervisory activities. Illustration 1 below summarizes the whole process: During business as usual, and in the normal stages of supervision, an initial problem can be identified, and insurers may seek to implement measures to overcome the problem. Supervisors would, in turn, normally intensify supervision and follow-up more closely on the developments of the insurer. Should the initial problem become a real financial threat (e.g. being in breach of, or about to breach, solvency capital requirements) the insurer enters into a new stage, which is linked to an increased risk of failure, i.e. a near miss situation. In this context, the insurer should trigger certain recovery actions to restore its financial position, while supervisors can intervene more intrusively. In general, there should be a reasonable prospect of recovery if effective and credible measures are implemented. Nevertheless, if the situation of distress is extremely severe and the measures taken do not yield the expected results, the insurer enters into resolution.

Eventually, the insurer (or parts of it) is (are) wound-up and exits the market.

EIOPA - Resolution

Near miss

In the context of this report, a near miss is defined as a case where an insurer faces specific financial difficulties (for example, when the solvency requirements are breached or likely to be breached) and the supervisor feels it necessary to intervene or to place the insurer under some form of special measures.

The elements to identify a near miss are the following:

  • The insurer is still in operation under its original form;
  • Nevertheless it is subject to a severe financial distress to an extent that the supervisory authority deems it necessary to intervene; and
  • In the absence of this intervention, the insurer will not survive in its current form and may eventually go into resolution or be wound-up.

Underlying is the idea of success of the measures taken. As such, it should not involve public money or policyholders’ loss.

In other words, a near miss presupposes that the supervisory intervention, either directly (e.g. replacing the management) or indirectly (e.g. request for an increase in capital), contributed in a clear way to overcome the insurer’s financial distress and bring it back to a “business-as-usual” environment. Shareholders generally keep their rights and could potentially oppose any of the measures undertaken.

On a day-to-day basis, insurers and NSAs might have to take different actions that require a certain degree of coordination. A “near miss” in the sense described in this report should be distinguished from these type of situations. Near misses only refer to cases where severe problems were detected or reported and supervisory measures were necessary to ensure the viability of the insurer.

Near misses actually constitute an area of particular interest for this report. In effect, their correct reporting and analysis would allow valuable lessons to be learned from successfully managed distress situations – prospective failure of an insurer and supervisory actions that permitted recovery.

Insurance failure

A failure, for the purposes of the present database, exists from the moment when an insurer is no longer viable or likely to be no longer viable, and has no reasonable prospect of becoming so.

The processes of winding-up/liquidation, which are usually initiated after insolvency, either on a balance sheet basis (the insurer’s liabilities are greater than its assets) or cash-flow basis (the insurer is unable to pay its debts as they fall due), are also encompassed within the definition of failure for the purposes of the database. Failure is thus triggered by “non-viability”.

The failed insurer ceases to operate in its current form. Shareholders generally lose some or all of their rights and cannot oppose to the measures taken by the authority in charge of resolution, which has formally taken over the reins from the supervisory authority.

For classification purposes, any case is considered as a failure (regardless of the final result of the intervention) when:

  • Private external support (e.g. by means of an insurance guarantee system (IGS)) has been received.
  • Public funds by taxpayers were needed for policyholders’ protection or financial stability reasons.
  • Policyholders have suffered any type of loss, be it in financial terms or in a deterioration of their insurance coverage.

The following are examples of resolution tools that may be used by authorities in a case of failure:

  • Sale of all or part of the insurers’ business to a private purchaser. A particular case is the transfer of an insurers’ portfolio, moving all or part of its business to another insurer without the consent of each and every policyholder.
  • Discontinue the writing of new business and continue administering the existing contractual policy obligations for inforce business (run-off).
  • Set-up a bridge institution as a temporary public entity to which all or part of the business of the insurer is transferred in order to preserve its critical functions.
  • Separate toxic assets from good assets establishing an asset management vehicle (i.e. a “bad insurer” similar to the concept used in banking) wholly owned by one or more public authorities for managing and running-down those assets in an orderly manner.
  • Restructure, limit or write down liabilities (including insurance and reinsurance liabilities) and allocate losses following the hierarchy of claims.

This also includes the bail-in of liabilities when they are by converted into equity.

  • Closure and orderly liquidation of the whole or part of a failing insurer.
  • Withdrawal of authorisation.

Lastly, it should be mentioned that the flow of events shown in Illustration 1 does not necessarily take place in a sequential way. For example, there could be cases in which an insurer goes directly into resolution. Thus, what is relevant for the classification of a particular case is whether the insurer recovers (which would then be considered as a near miss or as a case resolution/return to market if some kind of resolution action/tool is used) or has to be fully resolved and/or liquidated.

EIOPA - Sharma Risks

Click here to access EIOPA’s detailed report

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 Innovation Game – How Data is Driving Digital Transformation

Technology waits for no one. And those who strike first will have an advantage. The steady decline in business profitability across multiple industries threatens to erode future investment, innovation and shareholder value. Fortunately, the emergence of artificial intelligence (AI) can help kick-start profitability. Accenture research shows that AI has the potential to boost rates of profitability by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies by 2035.

Driven by these economic forces, the age of digital transformation is in full swing. Today we can’t be “digital to the core” if we don’t leverage all new data sources – unstructured, dark data and thirty party sources. Similarly, we have to take advantage of the convergence of AI and analytics to uncover previously hidden insights. But, with the increasing use of AI, we also have to be responsible and take into account the social implications.

Finding answers to the biggest questions starts with data, and ensuring you are capitalizing on the vast data sources available within your own business. Thanks to the power of AI/machine learning and advanced algorithms, we have moved from the era of big data to the era of ALL data, and that is helping clients create a more holistic view of their customer and more operational efficiencies.

Embracing the convergence of AI and analytics is crucial to success in our digital transformation. Together,

  • AI-powered analytics unlock tremendous value from data that was previously hidden or unreachable,
  • changing the way we interact with people and technology,
  • improving the way we make decisions, and giving way to new agility and opportunities.

While businesses are still in the infancy of tapping into the vast potential of these combined technologies, now is the time to accelerate. But to thrive, we need to be pragmatic in finding the right skills and partners to guide our strategy.

Finally, whenever we envision the possibilities of AI, we should consider the responsibility that comes with it. Trust in the digital era or “responsible AI” cannot be overlooked. Explainable AI and AI transparency are critical, particularly in such areas as

  • financial services,
  • healthcare,
  • and life sciences.

The new imperative of our digital transformation is to balance intelligent technology and human ingenuity to innovate every facet of business and become a smarter enterprise.

The exponential growth of data underlying the strategic imperative of enterprise digital transformation has created new business opportunities along with tremendous challenges. Today, we see organizations of all shapes and sizes embarking on digital transformation. As uncovered in Corinium Digital’s research, the primary drivers of digital transformation are those businesses focused on addressing increasing customer expectations and implementing efficient internal processes.

Data is at the heart of this transformation and provides the fuel to generate meaningful insights. We have reached the tipping point where all businesses recognize they cannot compete in a digital age using analog-era legacy solutions and architectures. The winners in the next phase of business will be those enterprises that obtain a clear handle on the foundations of modern data management, specifically the nexus of

  • data quality,
  • cloud,
  • and artificial intelligence (AI).

While most enterprises have invested in on-premises data warehouses as the backbone of their analytic data management practices, many are shifting their new workloads to the cloud. The proliferation of new data types and sources is accelerating the development of data lakes with aspirations of gaining integrated analytics that can accelerate new business opportunities. We found in the research that over 60% of global enterprises are now investing in a hybrid, multi-cloud strategy with both data from cloud environments such as Microsoft Azure along with existing on-premises infrastructures. Hence, this hybrid, multicloud strategy will need to correlate with their investments in data analytics, and it will become imperative to manage data seamlessly across all platforms. At Paxata, our mission is to give everyone the power to intelligently profile and transform data into consumable information at the speed of thought. To empower everyone, not just technical users, to prepare their data and make it ready for analytics and decision making.

The first step in making this transition is to eliminate the bottlenecks of traditional IT-led data management practices through AI-powered automation.

Second, you need to apply modern data preparation and data quality principles and technology platforms to support both analytical and operational use cases.

Thirdly, you need a technology infrastructure that embraces the hybrid, multi-cloud world. Paxata sits right at the center stage of this new shift, helping enterprises profile and transform complex data types in highvariety, high-volume environments. As such, we’re excited about partnering with Accenture and Microsoft to accelerate businesses with our ability to deliver modern analytical and operational platforms to address today’s digital transformation requirements.

Artificial intelligence is causing two major revolutions simultaneously among developers and enterprises. These revolutions will drive the technology decisions for the next decade. Developers are massively embracing AI. As a platform company, Microsoft is focused on enabling developers to make the shift to the next app development pattern, driven by the intelligent cloud and intelligent edge.

AI is the runtime that will power the apps of the future. At the same time, enterprises are eager to adopt and integrate AI. Cloud and AI are the most requested topics in Microsoft Executive Briefing Centers. AI is changing how companies serve their customers, run their operations, and innovate.

Ultimately, every business process in every industry will be redefined in profound ways. If it used to be true that “software was eating the world,” it is now true to say that “AI is eating software”. A new competitive differentiator is emerging: how well an enterprise exploits AI to reinvent and accelerate its processes, value chain and business models. Enterprises need a strategic partner who can help them transform their organization with AI. Microsoft is emerging as a solid AI leader as it is in a unique position to address both revolutions. Our strength and differentiation lie in the combination of multiple assets:

  • Azure AI services that bring AI to every developer. Over one million developers are accessing our pre-built and customizable AI services. We have the most comprehensive solution for building bots, combined with a powerful platform for Custom AI development with Azure Machine Learning that spans the entire AI development lifecycle, and a market leading portfolio of pre-built cognitive services that can be readily attached to applications.
  • A unique cloud infrastructure including CPU, GPU, and soon FPGA, makes Azure the most reliable, scalable and fastest cloud to run AI workloads.
  • Unparalleled tools. Visual Studio, used by over 6 million developers, is the most preferred tool in the world for application development. Visual Studio and Visual Studio Code are powerful “front doors” through which to attract developers seeking to add AI to their applications.
  • Ability to add AI to the edge. We enable developers, through our tools and services, to develop an AI model and deploy that model anywhere. Through our support for ONNX – the open source representation for AI models in partnership with Facebook, Amazon, IBM and others – as well as for generic containers, we allow developers to run their models on the IoT edge and leverage the entire IoT solution from Azure.

But the competition to win enterprises is not only played in the platform battlefield, enterprises are demanding solutions. Microsoft AI solutions provide turnkey implementations for customers who want to transform their core processes with AI. Our unique combination of IP and consulting services address common scenarios such as business agents, sales intelligence or marketing intelligence. As our solutions are built on top of our compelling AI platform, unlike ourcompetitors, our customers are not locked in to any one consulting provider, they remain in full control of their data and can extend the scenarios or target new scenarios themselves or through our rich partner ecosystem.

AI Analytics

Click here to access Corinium’s White Paper

RPA – A programmatic approach to intelligent automation to scale growth, manage risk, and drive enterprise value

Business leaders and chief information officers around the world are jumping on the robotic process automation (RPA) pilot bandwagon to start their companies on the automation journey. Some RPA pilots are evaluating software designed to stitch together known technology concepts—such as screen scraping and macrobased automation—through user-friendly tools to take process automation to the next level. Other pilots are venturing into the use of machine learning and cognitive automation to unleash new business insights.

These pilots—or proof-of-concept programs—help leaders set a foundation for their understanding of RPA, while at the same time introducing new ideas for how automation can help scale operations or define new business strategies. And now the pilot was successful, and leaders are seeing the possibilities. So what happens next?

When performing RPA pilots many companies get stuck in basic automation and stop there. Other companies have basic and cognitive automation pilots going on simultaneously.

Aligning the goals of basic RPA with cognitive computing and artificial intelligence can seem improbable. But are the objectives really that different? Leaders want to use all levels of automation to

  • drive business growth,
  • manage risk,
  • and increase value.

The trick is having a strategy for getting from pilot to program, and putting in place a comprehensive structure looking beyond the RPA pilots to intelligent automation (IA) as an across-the-board investment. This ensures IA ventures become more than speculation and remain significant to the business.

  • But how can leaders ensure that IA is more than a one-time cost play?
  • How are future automation opportunities identified and evaluated for both risk and benefit?
  • How is “electronic employee” service performance monitored?
  • How do leaders ensure the optimal mix of basic, enhanced, and cognitive automation?
  • How is business continuity maintained if the IA solution fails?
  • How is
    • system security,
    • change management,
    • system processing,
    • and authentication control
  • maintained as automation risk becomes more complex?
  • How will IA be used to transform the business?

Leaders know technology is changing rapidly, and IA is a moving target. Implementing a “bullet-proof” value-based program is critical to managing the automation revolution and ensuring it delivers positive business impacts over time. Robust program management balances risk and reward with structures driving sustainable IA value. An IA program model delivers these ideals.

An Intelligent Automation program can help enhance and expedite the implementation of IA throughout an organization. Here are four critical characteristics for success:

  1. It is strategically positioned – Positioning IA on par with other business strategies as integral to enterprise objectives is the best place to start. Similar to outsourcing (OS), these dependent IA vendor relationships are treated as strategic. Global processowners (GPO) use IA to transform end-to-end services. Global teams engage in IA opportunity evaluation to ensure bad processes are not automated.
  2. It uses a “center of excellence” service model – Establishing a center of excellence (CoE) demonstrates a commitment to IA success. Focus drives effectiveness, and CoEs drive transparency to IA results. CoEs have varied formats (virtual, centralized, regional, etc.) and engage cross-functional teams. CoE governance guides IA strategy and validates results. Clarifying decision rights balances governance and operations accountabilities. Incorporating IA support roles (e.g., HR, IT Security, Internal Audit, risk) in decision-making ensures change integration is well managed.
  3. It has a robust delivery framework – Integrating technologies, toolkits, and tactics into IA program execution safeguards sustainability. Including relevant designers, IT professionals, and operations teams in testing makes sure solutions work. Socializing and managing life cycle compliance (e.g., intake, approvals, testing) ensures team interaction is clear. Program management, repository, and workflow tools makes oversight effective.
  4. It incorporates a proactive risk management structure – Making IT risk and security control oversight a part of IA development ensures solutions are sound. Like any technology integration, change control is critical to implementation success. An IT security risk and control framework provides this support. Risk mitigation strategies linking security reviews to IA validation ensures business goals and technology risks are appropriately considered.

RPA

Click here to access KPMG’s detailed RPA report