Data Search and Discovery in Insurance – An Overview of AI Capabilities

Historically, the insurance industry has collected vast amounts of data relevant to their customers, claims, and so on. This can be unstructured data in the form of PDFs, text documents, images, and videos, or structured data that has been organized for big data analytics.

As with other industries, the existence of such a trove of data in the insurance industry led many of the larger firms to adopt big data analytics and techniques to find patterns in the data that might reveal insights that drive business value.

Any such big data applications may require several steps of data management, including collection, cleansing, consolidation, and storage. Insurance firms that have worked with some form of big data analytics in the past might have access to structured data which can be ingested by AI algorithms with little additional effort on the part of data scientists.

The insurance industry might be ripe for AI applications due to the availability of vast amounts of historical data records and the existence of large global companies with the resources to implement complex AI projects. The data being collected by these companies comes from several channels and in different formats, and AI search and discovery projects in the space require several initial steps to organize and manage data.

Radim Rehurek, who earned his PhD in Computer Science from the Masaryk University Brno and founded RARE Technologies, points out:

« A majority of the data that insurance firms collect is likely unstructured to some degree. This poses several challenges to insurance companies in terms of collecting and structuring data, which is key to the successful implementation of AI systems. »

Giacomo Domeniconi, a post-doctoral researcher at IBM Watson TJ Research Center and Adjunct Professor for the course “High-Performance Machine Learning” at New York University, mentions structuring the data as the largest challenge for businesses:

“Businesses need to structure their information and create labeled datasets, which can be used to train the AI system. Yet creating this labeled dataset might be very challenging apply AI and in most cases would involve manually labeling a part of the data using the expertise of a specialist in the domain.”

Businesses face many challenges in terms of collecting and structuring their data, which is key to the successful implementation of AI systems. An AI application is only as good as the data it consumes.

Natural language processing (NLP) and machine learning models often need to be trained on large volumes of data. Data scientists tweak these models to improve their accuracy.

This is a process that might last several months from start to finish, even in cases where the model is being taught relatively rudimentary tasks, such as identifying semantic trends in an insurance company’s internal documentation.

Most AI systems necessarily require the data to be input into an AI system in a structured format. Businesses would need to collect, clean, and organize their data to meet these requirements.

Although creating NLP and machine learning models to solve real-world business problems is by itself a challenging task, this process cannot be started without a plan for organizing and structuring enough data for these models to operate at reasonable accuracy levels.

Large insurance firms might need to think about how their data at different physical locations across the world might be affected by local data regulations or differences in data storage legacy systems at each location. Even with all the data being made accessible, businesses would find that data might still need to be scrubbed to remove any incorrect, incomplete, improperly formatted, duplicate, or outlying data. Businesses would also find that in some cases regulations might mandate the signing of data sharing agreements between the involved parties or data might need to be moved to locations where it can be analyzed. Since the data is highly voluminous, moving the data accurately can prove to be a challenge by itself.

InsIA

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The State of Connected Planning

We identify four major planning trends revealed in the data.

  • Trend #1: Aggressively growing companies plan more, plan better, and prioritize planning throughout the organization.

  • Trend #2: Successful companies use enterprise-scale planning solutions.

  • Trend #3: The right decisions combine people, processes, and technology.

  • Trend #4: Advanced analytics yield the insights for competitive advantage.

TREND 01 : Aggressively growing companies prioritize planning throughout the organization

Why do aggressively growing companies value planning so highly? To sustain an aggressive rate of growth, companies need to do two things:

  • Stay aggressively attuned to changes in the market, so they can accurately anticipate future trend
  • Keep employees across the company aligned on business objectives

This is why aggressively growing companies see planning as critical to realizing business goals.

Putting plans into action

Aggressively growing companies don’t see planning as an abstract idea. They also plan more often and more efficiently than other companies. Compared to their counterparts, aggressively growing companies plan with far greater frequency and are much quicker to incorporate market data into their plans

This emphasis on

  • efficiency,
  • speed,
  • and agility

produces real results. Compared to other companies, aggressively growing companies put more of their plans into action. Nearly half of aggressively growing companies turn more than three-quarters of their plans into reality.

For companies that experience a significant gap between planning and execution, here are three ways to begin to close it:

  1. Increase the frequency of your planning. By planning more often, you give yourself more flexibility, can incorporate market data more quickly, and have more time to change plans. A less frequent planning cadence, in contrast, leaves your organization working to incorporate plans that may lag months behind the market.
  2. Plan across the enterprise. Execution can go awry when plans made in one area of the business don’t take into account activities in another area. This disconnect can produce unreachable goals throughout the business, which can dramatically reduce the percentage of a plan that gets executed. To avoid this, create a culture of planning across the enterprise, ensuring that plans include relevant data from all business units.
  3. Leverage the best technology. As the statistic above shows, the companies who best execute on their plans are those who leverage cloud-based enterprise technology. This ensures that companies can plan with all relevant data and incorporate all necessary stakeholders. By doing this, companies can set their plans up for execution as they are made.

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TREND 02 : Successful companies use enterprise-scale planning solutions

Although the idea that planning assists all aspects of a business may seem like common sense, the survey data suggests that taking this assumption seriously can truly help companies come out ahead.

Executives across industries and geographies all agreed that planning benefits every single business outcome, including

  • enhancing revenues,
  • managing costs,
  • optimizing resources,
  • aligning priorities across the organization,
  • making strategies actionable,
  • anticipating market opportunities,
  • and responding to market changes.

In fact, 92 percent of businesses believe that better planning technology would provide better business outcomes for their company. Yet planning by itself is not always a panacea.

Planning does not always equal GOOD planning. What prepares a company for the future isn’t the simple act of planning. It’s the less-simple act of planning well. In business planning, band-aids aren’t solutions

What counts as good planning? As businesses know, planning is a complicated exercise,
involving multiple processes, many different people, and data from across the organization. Doing planning right, therefore, requires adopting a wide-angle view. It requires planners to be able to see past their individual functions and understand how changes in one part of the organization affect the organization as a whole.

The survey results suggest that the best way to give planners this enterprise-level perspective is to use the right technology. Companies whose technology can incorporate data from the entire enterprise are more successful. Companies whose planning technology cannot link multiple areas of the organization, or remove multiple obstacles to planning, in contrast, plan less successfully.

Here are three areas of consideration that can help you begin your Connected Planning journey.

  1. Get the right tools. Uncertainty and volatility continue to grow, and spreadsheets and point solutions lack the agility to pivot or accommodate the volumes of data needed to spot risks and opportunities. Consider tools such as cloud-based, collaborative Connected Planning platforms that use in-memory technology and execute real-time modeling with large volumes of data. Not only can teams work together but plans become more easily embraced and achievable.
  2. Operate from a single platform with reliable data. Traditionally, companies have used individual applications to plan for each business function. These solutions are usually disconnected from one another, which makes data unreliable and cross-functional collaboration nearly impossible. A shared platform that brings together plans with access to shared data reduces or altogether eliminates process inefficiencies and common errors that can lead to bad decision-making.
  3. Transform planning into a continuous, connected process. Sales, supply chain, marketing, and finance fulfill different purposes within the business, but they are inextricably linked and rely on each other for success. The ability to connect different business units through shared technology, data, and processes is at the core of a continuous and connected business planning process.

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TREND 03 The right decisions combine people, processes, and technology

As businesses examine different ways to drive faster, more effective decision-making, planning plays a critical role in meeting this goal. Ninety-nine percent of businesses say that planning is important to managing costs. According to 97 percent of all survey respondents,

  • enhancing revenues,
  • optimizing resource allocation,
  • and converting strategies into actions

are all business objectives for which planning is extremely crucial. Eighty-two percent of executives consider planning to be “critically important” for enhancing revenues.

For planning to be successful across an organization, it need to extend beyond one or two siloed business units. The survey makes this clear: 96 percent of businesses state that
planning is important for aligning priorities across the organization. Yet even though companies recognize planning as a critical business activity, major inefficiencies exist: 97 percent of respondents say that their planning can be improved.

The more planners, the merrier the planning

When describing what they could improve in their planning, four components were all named essential by a majority of respondents.

  • Having the right processes
  • Involving the right people
  • Having the right data
  • Having the right technology

To support strong and effective change management initiatives, successful businesses can build a Center of Excellence (COE). A COE is an internal knowledge-sharing community that brings domain expertise in creating, maturing, and sustaining high-performing business disciplines. It is comprised of an in-house team of subject matter experts who train and share best practices throughout the organization.

By designing a Center of Excellence framework, businesses can get more control over their planning processes with quality, speed, and value, especially as they continue to expand Connected Planning technology into more complex use cases across the company.

Here are six primary benefits that a COE can provide:

  1. Maintaining quality and control of the planning platform as use case expands.
  2. Establishing consistency to ensure reliability within best practices and business data.
  3. Fostering a knowledge-sharing environment to cultivate and develop internal expertise.
  4. Enabling up- and downstream visibility within a single, shared tool.
  5. Driving efficiency in developing, releasing, and maintaining planning models.
  6. Upholding centralized governance and communicating progress, updates, and value to executive sponsors.

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TREND 04 Advanced analytics yield the insights for competitive advantage

Disruption is no longer disruptive for businesses—it’s an expectation. Wide-spread globalization, fluid economies, emerging technologies, and fluctuating consumer demands make unexpected events and evolving business models the normal course of business today.

This emphasizes the critical need for a more proactive, agile, and responsive state of planning. As the data shows, companies that have implemented a more nimble approach to planning are more successful.

Planners don’t have to look far to find better insights. Companies who plan monthly or more are more likely to quickly incorporate new market data into their plans—updating forecasts and plans, assessing the impacts of changes, and keeping an altogether closer eye on ongoing business performance and targets.

However, not all companies are able to plan so continuously: Almost half of respondents indicate that it takes them weeks or longer to update plans with market changes. For businesses that operate in rapidly changing and competitive markets, this lag in planning can be a significant disadvantage.

Advancements in technology can alleviate this challenge. Ninety-two percent of businesses state that improved planning technology would provide better business outcomes for their company. The C-Suite, in particular, is even more optimistic about the adoption of improved technology: More than half of executives say that adopting better planning technology would result in “dramatically better” business performance.

Planning goes digital

Rather than planners hunting for data that simply validates a gut-feeling approach to planning, the survey results indicate that data now sits behind the wheel—informing, developing, improving, and measuring plans.

Organizations, as well as a majority of executives, describe digital transformation as a top priority. Over half of all organizations and 61 percent of executives say that digital transformation amplifies the importance of planning. As businesses move into the future, the increasing use of advanced analytics, which includes predictive analytics and spans to machine learning and artificial intelligence, will determine which businesses come out ahead.

Roadblocks to data-driven planning

Increasing uncertainty and market volatility make it imperative that businesses operate with agile planning that can be adjusted quickly and effectively. However, as planning response times inch closer to real time, nearly a third of organizations continue to cite two main roadblocks to implementing a more data-driven approach:

  • inaccurate planning data and
  • insufficient technology

Inaccurate data plagues businesses in all industries. Sixty-three percent of organizations that use departmental or point solutions, for example, and 59 percent of businesses that use on-premises solutions identify “having the right data” as a key area for improvement in planning. The use of point solutions, in particular, can keep data siloed. When data is stored in disparate technology across the organization, planners end up spending more time consolidating systems and information, which can compromise data integrity.

It’s perhaps these reasons that lead 46 percent of the organizations using point and on-premises solutions to say that better technologies are necessary to accommodate current market conditions. In addition, 43 percent of executives say that a move to cloud-based technology would benefit existing planning.

In both cases, data-driven planning remains difficult, as businesses not employing cloud-based, enterprise technology struggle with poor data accuracy. By moving to cloud-based technology, businesses can automate and streamline tedious processes, which

  • reduces human error,
  • improves productivity,
  • and provides stakeholders with increased visibility into performance.

State-of-planning research reveals that organizations identify multiple business planning
obstacles as equally problematic, indicating a need for increased analytics in solutions that can eliminate multiple challenges at once. Nearly half of all respondents shared a high desire for a collaborative platform that can be used by all functions and departments.

Highly analytical capabilities in planning solutions further support the evolving needs of
today’s businesses. In sales forecasting, machine learning methodologies can quickly analyze past pipeline data to make accurate forecast recommendations. When working in financial planning, machine learning can help businesses analyze weather, social media, and historical sales data to quickly discern their impact on sales.

Here are some additional benefits that machine learning methodologies in a collaborative planning platform can offer businesses:

  1. Manage change to existing plans and respond to periods of uncertainty with accurate demand forecasting and demand sensing
  2. Develop enlightened operations, real-time forecasting, and smart sourcing and resourcing plans
  3. Operations that maintain higher productivity and more control with lower maintenance costs
  4. Targeted customer experience programs that increase loyalty and improve customer engagement
  5. Products and services that are offered at the right price with effective trade promotions, resulting in higher conversions

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EIOPA reviews the use of Big Data Analytics in motor and health insurance

Data processing has historically been at the very core of the business of insurance undertakings, which is rooted strongly in data-led statistical analysis. Data has always been collected and processed to

  • inform underwriting decisions,
  • price policies,
  • settle claims
  • and prevent fraud.

There has long been a pursuit of more granular data-sets and predictive models, such that the relevance of Big Data Analytics (BDA) for the sector is no surprise.

In view of this, and as a follow-up of the Joint Committee of the European Supervisory Authorities (ESAs) cross-sectorial report on the use of Big Data by financial institutions,1 the European Insurance and Occupational Pensions Authority (EIOPA) decided to launch a thematic review on the use of BDA specifically by insurance firms. The aim is to gather further empirical evidence on the benefits and risks arising from BDA. To keep the exercise proportionate, the focus was limited to motor and health insurance lines of business. The thematic review was officially launched during the summer of 2018.

A total of 222 insurance undertakings and intermediaries from 28 jurisdictions have participated in the thematic review. The input collected from insurance undertakings represents approximately 60% of the total gross written premiums (GWP) of the motor and health insurance lines of business in the respective national markets, and it includes input from both incumbents and start-ups. In addition, EIOPA has collected input from its Members and Observers, i.e. national competent authorities (NCAs) from the European Economic Area, and from two consumers associations.

The thematic review has revealed a strong trend towards increasingly data-driven business models throughout the insurance value chain in motor and health insurance:

  • Traditional data sources such as demographic data or exposure data are increasingly combined (not replaced) with new sources like online media data or telematics data, providing greater granularity and frequency of information about consumer’s characteristics, behaviour and lifestyles. This enables the development of increasingly tailored products and services and more accurate risk assessments.

EIOPA BDA 1

  • The use of data outsourced from third-party data vendors and their corresponding algorithms used to calculate credit scores, driving scores, claims scores, etc. is relatively extended and this information can be used in technical models.

EIOPA BDA 2

  • BDA enables the development of new rating factors, leading to smaller risk pools and a larger number of them. Most rating factors have a causal link while others are perceived as being a proxy for other risk factors or wealth / price elasticity of demand.
  • BDA tools such as such as artificial intelligence (AI) or machine learning (ML) are already actively used by 31% of firms, and another 24% are at a proof of concept stage. Models based on these tools are often cor-relational and not causative, and they are primarily used on pricing and underwriting and claims management.

EIOPA BDA 3

  • Cloud computing services, which reportedly represent a key enabler of agility and data analytics, are already used by 33% of insurance firms, with a further 32% saying they will be moving to the cloud over the next 3 years. Data security and consumer protection are key concerns of this outsourcing activity.
  • Up take of usage-based insurance products will gradually continue in the following years, influenced by developments such as increasingly connected cars, health wearable devices or the introduction of 5G mobile technology. Roboadvisors and specially chatbots are also gaining momentum within consumer product and service journeys.

EIOPA BDA 4

EIOPA BDA 5

  • There is no evidence as yet that an increasing granularity of risk assessments is causing exclusion issues for high-risk consumers, although firms expect the impact of BDA to increase in the years to come.

In view of the evidence gathered from the different stake-holders, EIOPA considers that there are many opportunities arising from BDA, both for the insurance industry as well as for consumers. However, and although insurance firms generally already have in place or are developing sound data governance arrangements, there are also risks arising from BDA that need to be further addressed in practice. Some of these risks are not new, but their significance is amplified in the context of BDA. This is particularly the case regarding ethical issues with the fairness of the use of BDA, as well as regarding the

  • accuracy,
  • transparency,
  • auditability,
  • and explainability

of certain BDA tools such as AI and ML.

Going forward, in 2019 EIOPA’s InsurTech Task Force will conduct further work in these two key areas in collaboration with the industry, academia, consumer associations and other relevant stakeholders. The work being developed by the Joint Committee of the ESAs on AI as well as in other international fora will also be taken into account. EIOPA will also explore third-party data vendor issues, including transparency in the use of rating factors in the context of the EU-US insurance dialogue. Furthermore, EIOPA will develop guidelines on the use of cloud computing by insurance firms and will start a new workstream assessing new business models and ecosystems arising from InsurTech. EIOPA will also continue its on-going work in the area of cyber insurance and cyber security risks.

Click here to access EIOPA’s detailed Big Data Report

Is Your Company Ready for Artificial Intelligence?

Overview

Companies are rushing to invest in and pursue initiatives that use artificial intelligence (AI). Some hope to find opportunity to transform their business processes and gain competitive advantage and others are concerned about falling behind the technology curve. But the reality is that many AI initiatives don’t work as planned, largely because companies are not ready for AI.

However, it is possible to leverage AI to create real business value. The key to AI success is ensuring the organization is ready by having the basics in place, particularly structured analytics and automation. Other elements of AI readiness include

  • executive engagement and support,
  • data excellence,
  • organizational capabilities,
  • and completion of AI pilots.

Key Takeaways

There is tremendous AI hype and investment. Artificial intelligence is software that can make decisions without explicit instructions for each scenario, including an ability to learn and improve over time. The term “machine learning” is often used interchangeably with AI, but machine learning is just one approach to AI, though it is currently the approach generating the most attention. Today in most business situations where AI is relevant, machine learning is likely to be employed.

The hype around AI is tremendous and has accelerated in the last few years. It is rare to read a business-related article these days that doesn’t mention AI.

The AI hype is being accompanied by massive investments from corporations (like Amazon, Google, and Uber), as well as from venture capital firms.

Because organizations often pursue AI without fully understanding it or having the basics in place, many AI initiatives fail. The AI fervor is causing companies to hurriedly pursue AI. There is a rush to capitalize on AI, but significant frustration when it comes to actually delivering AI success. AI initiatives are often pursued for the wrong reasons and many AI initiatives experience pitfalls. Some key pitfalls are:

  • Expensive partnerships between large companies and startups without results.
  • Impenetrable black box systems.
  • Open source toolkits without programmers to code.

The root cause for these failures often boils down to companies confusing three different topics:

  • automation,
  • structured analytics,
  • and artificial intelligence.

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Despite the challenges, some organizations are experiencing success with AI. While the hype around AI is overblown, there are organizations having success by leveraging AI to create business value, particularly when AI is used for customer support and in the back office.

The key to AI success is first having the basics in place. In assessing AI successes and failures, the presenters drew three conclusions:

  1. There is a huge benefit from first getting the basics right: automation and structured analytics are prerequisites to AI.
  2. The benefits from AI are greater once these basics have been done right.
  3. Organizations are capable of working with AI at scale only when the basics have been done at scale.

GETTING THE BASICS RIGHT

The most important basics for AI are automation and structured analytics.

  • Automation: In most businesses there are many examples of data processes that can be automated. In many of these examples, there is no point having advanced AI if the basics are not yet automated.
  • Structured analytics means applying standard statistical techniques to well-structured data. In most companies there is huge value in getting automation and structured analytics right before getting to more complicated AI.

Examples of how businesses use structured analytics and automation include:

  • Competitor price checking. A retailer created real-time pricing intelligence by automatically scraping prices from competitors’ websites.
  • Small business cash flow lending product. Recognizing the need for small business customers to acquire loans in days, not weeks, a bank created an online lending product built on structured analytics.

BENEFITS WHEN THE BASICS ARE IN PLACE

Once the basics of structured analytics and automation are in place, organizations see more value from AI—when AI is used in specific situations.

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Examples of how adding AI on top of the basics helps improve business results are:

  • New product assortment decisions. Adding AI on top of structured analytics allowed a retailer to predict the performance of new products for which there was no historic data. With this information, the retailer was able to decide whether or not to add the product to the stores.
  • Promotions forecasting. A retailer was able to improve forecasting of promotional sales using AI. Within two months of implementation, machine learning was better than the old forecasts plus the corrections made by the human forecasting team.
  • Customer churn predictions. A telephone company used AI and structured analytics to identify how to keep at-risk customers from leaving.
  • Defect detection. An aerospace manufacturer used AI to supplement human inspection and improve defect detection.

AI AT SCALE AFTER THE BASICS ARE AT SCALE

Once an organization proves it can work with automation and structured analytics at scale, it is ready for AI at scale. Readiness for AI at scale goes beyond completing a few AI pilots in defined but isolated areas of capability; the basics need to be in use across the business.

Before undertaking AI, organizations need to assess their AI readiness. To be successful, organizations need to be ready for AI. Readiness consists of multiple elements, including

  • executive engagement and support,
  • data excellence,
  • organizational capabilities,
  • and an analytical orientation.

Organizations often struggle with data excellence and organizational capabilities.

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Click here to access HBR and SAS article collection

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.

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Click here to access Wolters Kluwers FSN detailed survey report