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Moving from best to better and better – Business practice redesign is an untapped opportunity

Under mounting performance pressure, many corporate leaders are looking to business process reengineering to improve performance, and in many ways that makes sense after all, processes give shape to an organization and are often useful for coordinating routine flows across large organizations. The routine work of a company should be done as efficiently as possible, which increasingly means incorporating automation.

But organizations may be missing a much greater opportunity to improve performance.

Here’s the thing: Much of the work of many organizations today—at least the work that typically offers the potential for differentiation—is no longer routine or even predictable. When conditions and requirements shift constantly, processes fail. While process optimization can still certainly help

  • reduce costs
  • and streamline operations,

leaders should consider a different kind of organizational rethinking for significant performance improvement. And in an environment of accelerating technological advances and rapid and unpredictable change, constant performance improvement is a must. Competition can come from anywhere—doing well relative to the competitors on your radar isn’t enough. Many barriers to competition are falling, and many boundaries, between industries and between markets, are blurring.

  • Consumers have more access to information and alternatives than ever, along with a coincident increase in expectations.
  • Workers have more access to information and alternatives—and increased expectations.

At the same time, many employees, in all kinds of environments, face increasing pressure to reach higher levels of individual performance. The useful life of many skills is in decline, creating a constant pressure to learn fast and reskill.

Many companies have struggled to effectively respond to these pressures since long before the Internet of Things and cognitive technologies added new layers of complexity. The average return on assets for US companies has declined for the past several decades, and companies find themselves displaced from market leadership positions more often than they used to. While the price-performance improvement in the digital infrastructure has increased exponentially, most companies are still capturing only a small fraction of the value that ought to be available through the technologies built on this infrastructure. Existing approaches to performance improvement appear to be falling short.

It begs the question: In a world of digital transformation and constant change, what does performance improvement mean? Many companies suffer from at least one of three broad problems that can misdirect their focus:

  1. Thinking of performance improvement too modestly. Leaders often think of performance advances as discrete, one-time jumps from A to B, or even a series of jumps to C and D. The initiatives that typically generate these bumps are similarly construed as pre-defined, one-time changes rather than as unbounded efforts that have the potential to generate more and more improvement. As we discuss in more detail, not only do most companies need to continually improve their performance— those that don’t start accelerating may fall further and further behind and become increasingly marginalized. Accelerating improvement, then, should be a goal of operations, not just one-off initiatives.
  2. Thinking of performance improvement too narrowly, focused only on costs. Process dominated much of performance improvement efforts for the past several decades, focusing largely on the denominator of the financial ratio of revenues to costs. But costs can be cut only so far, and technology-based process efficiencies can be quickly competed away, especially at a time when the changing environment and shifting customer expectations are making many standardized processes quickly obsolete. Further reductions can become harder to achieve and have less impact. The relevant performance might be more about an organization’s ability to create significant new value. Workers across an organization regularly encounter new needs, new tools for meeting needs, and opportunities to identify new ways of delivering more value and impact in multiple dimensions, including helping other parts of the organization generate more value. The potential for value creation isn’t confined to certain roles or functions, and is bounded primarily by an organization’s ability to create new knowledge and creatively address new problems. Focusing on new value creation may be the key to getting on a trajectory of accelerating performance improvement. Doing so would require an organization to move beyond efficiency and standardization and begin focusing on cultivating the behaviors—such as experimentation and reflection to make sense of what has been learned—associated with new value creation.
  3. Thinking of performance improvement at the wrong level. Most organizations manage performance where they measure it—which is to say where they have data: broadly, for the department and organization, and narrowly, for the individual. Both levels can miss where work, especially value-creating work, increasingly gets done: in groups. As a result, organizations can miss the opportunity to shape how work actually gets done. Focusing on performance where it matters most to the organization’s work might be a key to having a significant impact on the performance that matters.

The imperative to act seems simple: Today’s environment seems to offer no reprieve, no stabilization that gives us a chance to catch our breath and say, “OK, now we’ve got it figured out.” The methods and processes that led organizations to great success in the past seem to no longer be working. For sustained performance improvement, companies may need to change their focus and look in new directions.

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Click here to access Deloitte’s detailed study

By investing heavily in start-ups and technology, (re)insurance companies appear to have assumed a semblance of control over the InsurTech revolution

Who Benefits from Modularization?

With technology moving forward at an unprecedented pace, incumbents are increasingly electing to outsource functions to highly specialized new entrants, renting evolving modules of technology that can be tailored to suit their individual needs. Though this approach may be more cost effective, it further fuels the question of whether incumbents will allow value in the industry to shift towards new entrants. In time, market participants will come to understand which module in the chain generates the most value. It is plausible that automation in distribution will shift value towards efficiency of internal processes that support cutting-edge modeling and underwriting engines.

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The State of InsurTech

InsurTech funding volume increased 36% year-over-year in 2017, demonstrating that technology driven innovation remains a core focus area for (re)insurance companies and investors heading into 2018. However, perhaps contrary to many of the opinions championed in editorial and press coverage of the InsurTech sector, further analysis of the growing number of start-ups successfully attracting capital from (re)insurers and financial investors reveals that the majority of InsurTech ventures are not focused on exiling incumbents by disrupting the pressured insurance value chain. According to research from McKinsey & Company,

  • 61% of InsurTech companies aim to enable the value chain,
  • 30% are attempting to disintermediate incumbents from customers
  • 9% are targeting full scale value chain disruption.

Has the hype surrounding InsurTech fostered unjustified fear from overly defensive incumbents?

We have taken this analysis a step further by tracking funding volume from strategic (re)insurers versus financial investors for InsurTechs focused on enabling the value chain relative to their counterparts attempting to disintermediate customers from incumbents or disrupt the value chain altogether and found that 65% of strategic (re)insurer InsurTech investments have been concentrated in companies enabling the value chain, with only 35% of incumbent investments going to start-ups with more disruptive business models. What does it mean? While recognizing the subjective nature of surmising an early stage company’s ultimate industry application at maturity from its initial focus, we attribute this phenomenon to the tendency of incumbents to, consciously or subconsciously, encourage development of less perceptibly threatening innovation while avoiding more radical, potentially intimidating technologies and applications.

Recognizing that this behavior may allow incumbents to preserve a palatable status quo, it should be considered in the context in which individual investments are evaluated – on the basis of expected benefits relative to potential risk. We have listed several benefits that InsurTechs offer to incumbents :

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Segmenting the InsurTech Universe

As InsurTech start-ups continue to emerge across the various components of the insurance value chain and business lines, incumbents and investors are evaluating opportunities to deploy these applications in the insurance industry today and in the future. To simplify the process of identifying useful and potentially transformational technologies and applications, we have endeavored to segment the increasingly broad universe of InsurTech companies by their core function into four categories:

  1. Product & Distribution
  2. Business Process Enhancement
  3. Data & Analytics
  4. Claims Management

This exercise is complicated by the tendency of companies to operate across multiple functions, so significant professional judgment was used in determining the assignment for each company. A summary of the criteria used to determine placement is listed below. On the following pages, we have included market maps to provide a high level perspective of the number of players in each category, as well as a competitive assessment of each subsector and our expectations for each market going forward. Selected companies in each category, ranked by the amount of funding they have raised to date, are listed, followed by more detailed overviews and Q&A with selected representative companies from each subsector.

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Click here to access WTW’s detailed birefing

2018 AI predictions – 8 insights to shape your business strategy

  1. AI will impact employers before it impacts employment
  2. AI will come down to earth—and get to work
  3. AI will help answer the big question about data
  4. Functional specialists, not techies, will decide the AI talent race
  5. Cyberattacks will be more powerful because of AI—but so
    will cyberdefense
  6. Opening AI’s black box will become a priority
  7. Nations will spar over AI
  8. Pressure for responsible AI won’t be on tech companies alone

Key implications

1) AI will impact employers before it impacts employment

As signs grow this year that the great AI jobs disruption will be a false alarm, people are likely to more readily accept AI in the workplace and society. We may hear less about robots taking our jobs, and more about robots making our jobs (and lives) easier. That in turn may lead to a faster uptake of AI than some organizations are expecting.

2) AI will come down to earth—and get to work

Leaders don’t need to adopt AI for AI’s sake. Instead, when they look for the best solution to a business need, AI will increasingly play a role. Does the organization want to automate billing, general accounting and budgeting, and many compliance functions? How about automating parts of procurement, logistics, and customer care? AI will likely be a part of the solution, whether or not users even perceive it.

3) AI will help answer the big question about data

Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They’ll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries. There’s no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

4) Functional specialists, not techies, will decide the AI talent race

Enterprises that intend to take full advantage of AI shouldn’t just bid for the most brilliant computer scientists. If they want to get AI up and running quickly, they should move to provide functional specialists with AI literacy. Larger organizations should prioritize by determining where AI is likely to disrupt operations first and start upskilling there.

5) Cyberattacks will be more powerful because of AI—but so will cyberdefense

In other parts of the enterprise, many organizations may choose to go slow on AI, but in cybersecurity there’s no holding back: Attackers will use AI, so defenders will have to use it too. If an organization’s IT department or cybersecurity provider isn’t already using AI, it has to start thinking immediately about AI’s short- and long-term security applications. Sample use cases include distributed denial of service (DDOS) pattern recognition, prioritization of log alerts for escalation and investigation, and risk-based authentication. Since even AI-wary organizations will have to use AI for cybersecurity, cyberdefense will be many enterprises’ first experience with AI. We see this fostering familiarity with AI and willingness to use it elsewhere. A further spur to AI acceptance will come from its hunger for data: The greater AI’s presence and access to data throughout an organization, the better it can defend against cyberthreats. Some organizations are already building out on-premise and cloud-based “threat lakes,” that will enable AI capabilities.

6) Opening AI’s black box will become a priority

We expect organizations to face growing pressure from end users and regulators to deploy AI that is explainable, transparent, and provable. That may require vendors to share some secrets. It may also require users of deep learning and other advanced AI to deploy new techniques that can explain previously incomprehensible AI. Most AI can be made explainable—but at a cost. As with any other process, if every step must be documented and explained, the process becomes slower and may be more expensive. But opening black boxes will reduce certain risks and help establish stakeholder trust.

7) Nations will spar over AI

If China starts to produce leading AI developments, the West may respond. Whether it’s a “Sputnik moment” or a more gradual realization that they’re losing their lead, policymakers may feel pressure to change regulations and provide funding for AI. More countries should issue AI strategies, with implications for companies. It wouldn’t surprise us to see Europe, which is already moving to protect individuals’ data through its General Data Protection Regulation (GDPR), issue policies to foster AI in the region.

8) Pressure for responsible AI won’t be on tech companies alone

As organizations face pressure to design, build, and deploy AI systems that deserve trust and inspire it, many will establish teams and processes to look for bias in data and models and closely monitor ways malicious actors could “trick” algorithms. Governance boards for AI may also be appropriate for many enterprises.

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Click here to access PWC’s detailed predictions report

 

EIOPA Risk Dashboard January 2018

Risks originating from the macroeconomic environment remained stable and high. Improvements have been observed across most indicators, but were not sufficient to change the overall risk picture. The improving prospects for economic growth still contrast with the persistence of structural imbalances, such as fiscal deficit. The accommodative stance of monetary policy has been reduced only very gradually, with low interest rates continuing to put a strain on the insurance sector.

Credit risks remained constant at a medium level whereas observed spreads continued to decline. The average rating of investments has seen some marginal improvements. Concerns on the pricing of the risk premia remain.

Market risks remained stable at a medium level despite a reduction of the volatility on prices was observed. Only price to book value of European stocks moved in the direction of risk increase.

Liquidity and funding risks were constant at a medium level in 2017 Q3 and remained a minor issue for insurers. Catastrophe bond issuance significantly decreased when compared to the record high registered during the previous quarter. The low volume of issued bonds made the indicator less relevant.

Profitability and solvency risks remained stable at a medium level. A deterioration of the net combined ratio was observed in the tail (90 percentile) of the distribution mainly populated by reinsurers in this quarter. SCR ratios have improved across all types of insurers mainly due to an increase of the Eligible Own Funds. This has been especially marked for life solo companies.

Interlinkages & imbalances: Risks in this category remained constant at a medium level. Investment exposures to banks and other insurers increased slightly from the previous quarter.

Insurance risks increased when compared to 2017 Q2 and are now at a medium level. This was essentially driven by the significant increase in the catastrophe loss ratio resulting from the impact of the catastrophic events observed in Q3 mainly on reinsurers’ technical results. This is also reflected in the loss ratio. Other indicators in this risk category still point to a stable risk exposure.

Market perceptions remained constant, with the improvement in external rating outlooks outweighing the observed increase in price to earnings ratios. Insurance stocks slightly outperformed the market, especially for life insurance, and CDS spreads reduced.

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Click here to access EIOPA’s Risk Dashboard January 2018

The Global Risks Report 2018

Last year’s Global Risks Report was published at a time of heightened global uncertainty and strengthening popular discontent with the existing political and economic order. The report called for “fundamental reforms to market capitalism” and a rebuilding of solidarity within and between countries.

One year on, a global economic recovery is under way, offering new opportunities for progress that should not be squandered: the urgency of facing up to systemic challenges has, if anything, intensified amid proliferating indications of uncertainty, instability and fragility. Humanity has become remarkably adept at understanding how to mitigate conventional risks that can be relatively easily isolated and managed with standard riskmanagement approaches. But we are much less competent when it comes to dealing with complex risks in the interconnected systems that underpin our world, such as organizations, economies, societies and the environment. There are signs of strain in many of these systems: our accelerating pace of change is testing the absorptive capacities of institutions, communities and individuals. When risk cascades through a complex system, the danger is not of incremental damage but of “runaway collapse” or an abrupt transition to a new, suboptimal status quo.

In our annual Global Risks Perception Survey, environmental risks have grown in prominence in recent years. This trend has continued this year, with all five risks in the environmental category being ranked higher than average for both likelihood and impact over a 10-year horizon. This follows a year characterized by high-impact hurricanes, extreme temperatures and the first rise in CO2 emissions for four years. We have been pushing our planet to the brink and the damage is becoming increasingly clear. Biodiversity is being lost at mass-extinction rates, agricultural systems are under strain and pollution of the air and sea has become an increasingly pressing threat to human health. A trend towards nation-state unilateralism may make it more difficult to sustain the long-term, multilateral responses that are required to counter global warming and the degradation of the global environment.

Cybersecurity risks are also growing, both in their prevalence and in their disruptive potential. Attacks against businesses have almost doubled in five years, and incidents that would once have been considered extraordinary are becoming more and more commonplace. The financial impact of cybersecurity breaches is rising, and some of the largest costs in 2017 related to ransomware attacks, which accounted for 64% of all malicious emails. Notable examples included the WannaCry attack—which affected 300,000 computers across 150 countries—and NotPetya, which caused quarterly losses of US$300 million for a number of affected businesses. Another growing trend is the use of cyberattacks to target critical infrastructure and strategic industrial sectors, raising fears that, in a worst-case scenario, attackers could trigger a breakdown in the systems that keep societies functioning.

Headline economic indicators suggest the world is finally getting back on track after the global crisis that erupted 10 years ago, but this upbeat picture masks continuing underlying concerns. The global economy faces a mix of long-standing vulnerabilities and newer threats that have emerged or evolved in the years since the crisis. The familiar risks include potentially unsustainable asset prices, with the world now eight years into a bull run; elevated indebtedness, particularly in China; and continuing strains in the global financial system. Among the newer challenges are limited policy firepower in the event of a new crisis; disruptions caused by intensifying patterns of automation and digitalization; and a build-up of mercantilist and protectionist pressures against a backdrop of rising nationalist and populist politics.

The world has moved into a new and unsettling geopolitical phase. Multilateral rules-based approaches have been fraying. Re-establishing the state as the primary locus of power and legitimacy has become an increasingly attractive strategy for many countries, but one that leaves many smaller states squeezed as the geopolitical sands shift. There is currently no sign that norms and institutions exist towards which the world’s major powers might converge. This creates new risks and uncertainties: rising military tensions, economic and commercial disruptions, and destabilizing feedback loops between changing global conditions and countries’ domestic political conditions. International relations now play out in increasingly diverse ways. Beyond conventional military buildups, these include new cyber sources of hard and soft power, reconfigured trade and investment links, proxy conflicts, changing alliance dynamics, and potential flashpoints related to the global commons. Assessing and mitigating risks across all these theatres of potential conflict will require careful horizon scanning and crisis anticipation by both state and nonstate actors.

This year’s Global Risks Report introduces three new series:

  1. Future Shocks,
  2. Hindsight,
  3. Risk Reassessment.

Our aim is to broaden the report’s analytical reach: each of these elements provides a new lens through which to view the increasingly complex world of global risks.

Future Shocks is a warning against complacency and a reminder that risks can crystallize with disorientating speed. In a world of complex and interconnected systems, feedback loops, threshold effects and cascading disruptions can lead to sudden and dramatic breakdowns. We present 10 such potential breakdowns—from democratic collapses to spiralling cyber conflicts—not as predictions, but as food for thought: what are the shocks that could fundamentally upend your world?

In Hindsight we look back at risks we have analysed in previous editions of the Global Risks Report, tracing the evolution of the risks themselves and the global responses to them. Revisiting our past reports in this way allows us to gauge risk-mitigation efforts and highlight lingering risks that might warrant increased attention. This year we focus on antimicrobial resistance, youth unemployment, and “digital wildfires”, which is how we referred in 2013 to phenomena that bear a close resemblance to what is now known as “fake news”.

In Risk Reassessment, selected risk experts share their insights about the implications for decisionmakers in businesses, governments and civil society of developments in our understanding of risk. In this year’s report, Roland Kupers writes about fostering resilience in complex systems, while Michele Wucker calls for organizations to pay more attention to cognitive bias in their risk management processes.

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Click here to access WEF – Marsh’s detailed Global Risk Report 2018

Technology Driven Value Generation in Insurance

The evolution of financial technology (FinTech) is reshaping the broader financial services industry. Technology is now disrupting the traditionally more conservative insurance industry, as the rise of InsurTech revolutionises how we think about insurance distribution.

Moreover, insurance companies are improving their operating models, upgrading their propositions, and developing innovative new products to reshape the insurance industry as a whole.

Five key technologies are driving the change today:

  1. Cloud computing
  2. The Internet of Things (including telematics)
  3. Big data
  4. Artificial intelligence
  5. Blockchain

This report examines these technologies’ potential to create value in the insurance industry. It also examines how technology providers could create new income streams and take advantage of economies of scale by offering their technological backbones to participants in the insurance industry and beyond.

Cloud computing refers to storing, managing, and processing data via a network of remote servers, instead of locally on a server or personal computer. Key enablers of cloud computing include the availability of high-capacity networks and service-oriented architecture. The three core characteristics of a cloud service are:

  • Virtualisation: The service is based on hardware that has been virtualised
  • Scalability: The service can scale on demand, with additional capacity brought online within minutes
  • Demand-driven: The client pays for the services as and when they are needed

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Telematics is the most common form of the broader Internet of Things (IoT). The IoT refers to the combination of physical devices, vehicles, buildings and other items embedded with electronics, software, sensors, actuators, and network connectivity that enable these physical objects to collect and exchange data.

The IoT has evolved from the convergence of

  • wireless technologies,
  • micro-electromechanical systems,
  • and the Internet.

This convergence has helped remove the walls between operational technology and information technology, allowing unstructured, machine-generated data to be analysed for insights that will drive improvements.

IoT

Big data refers to data sets that are so large or complex that traditional data processing application software is insufficient to deal with them. A definition refers to the “five V” key challenges for big data in insurance:

  • Volume: As sensors cost less, the amount of information gathered will soon be measured
    in exabytes
  • Velocity: The speed at which data is collected, analysed, and presented to users
  • Variety: Data can take many forms, such as structured, unstructured, text or multimedia. It can come from internal and external systems and sources, including a variety
    of devices
  • Value: Information provided by data about aspects of the insurance business, such as customers and risks
  • Veracity: Insurance companies ensure the accuracy of their plethora of data

Modern analytical methods are required to process these sets of information. The term “big data has evolved to describe the quantity of information analysed to create better outcomes, business improvements, and opportunities that leverage all available data. As a result, big data is not limited to the challenges thrown up by the five Vs. Today there are two key aspects to big data:

  1. Data: This is more-widely available than ever because of the use of apps, social media, and the Internet of Things
  2. Analytics: Advanced analytic tools mean there are fewer restrictions to working with big data

BigData

The understanding of Artificial Intelligence AI has evolved over time. In the beginning, AI was perceived as machines mimicking the cognitive functions that humans associate with other human minds, such as learning and problem solving. Today, we rather refer to the ability of machines to mimic human activity in a broad range of circumstances. In a nutshell, artificial intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider smart or human.

Therefore, AI combines the reasoning already provided by big data capabilities such as machine learning with two additional capabilities:

  1. Imitation of human cognitive functions beyond simple reasoning, such as natural language processing and emotion sensing
  2. Orchestration of these cognitive components with data and reasoning

A third layer is pre-packaging generic orchestration capabilities for specific applications. The most prominent such application today are bots. At a minimum, bots orchestrate natural language processing, linguistic technology, and machine learning to create systems which mimic interactions with human beings in certain domains. This is done in such a way that the customer does not realise that the counterpart is not human.

Blockchain is a distributed ledger technology used to store static records and dynamic transaction data distributed across a network of synchronised, replicated databases. It establishes trust between parties without the use of a central intermediary, removing frictional costs and inefficiency.

From a technical perspective, blockchain is a distributed database that maintains a continuously growing list of ordered records called blocks. Each block contains a timestamp and a link to a previous block. Blockchains have been designed to make it inherently difficult to modify their data: Once recorded, the data in a block cannot be altered retroactively. In addition to recording transactions, blockchains can also contain a coded set of instructions that will self-execute under a pre-specified set of conditions. These automated workflows, known as smart contracts, create trust between a set of parties, as they rely on pre-agreed data sources and and require not third-party to execute them.

Blockchain technology in its purest form has four key characteristics:

  1. Decentralisation: No single individual participant can control the ledger. The ledger
    lives on all computers in the network
  2. Transparency: Information can be viewed by all participants on the network, not just
    those involved in the transaction
  3. Immutability: Modifying a past record would require simultaneously modifying every
    other block in the chain, making the ledger virtually incorruptible
  4. Singularity: The blockchain provides a single version of a state of affairs, which is
    updated simultaneously across the network

Blockchain

Oliver Wyman, ZhongAn Insurance and ZhongAn Technology – a wholly owned subsidiary of ZhongAn insurance and China’s first online-only insurer – are jointly publishing this report to analyse the insurance technology market and answer the following questions:

  • Which technologies are shaping the future of the insurance industry? (Chapter 2)
  • What are the applications of these technologies in the insurance industry? (Chapter 3)
  • What is the potential value these applications could generate? (Chapter 3)
  • How can an insurer with strong technology capabilities monetise its technologies?
    (Chapter 4)
  • Who is benefiting from the value generated by these applications? (Chapter 5)

 

Click here to access Oliver Wyman’s detailed report

Insurance Data Integrated Platform

The insurance industry today is poised for a paradigm shift in the way that technology is deployed to provide products and services to customers. This has primarily been driven by changing business needs and the innovations brought about by myriad insuretech firms, leading to an inevitable shift towards adopting the new digital innovations.

Analysts have forecast significant investments geared towards the digitalization of the industry and expect such investments to continue pouring in for several years. It is also expected that an increasing number of new insurance companies will be driven by technology companies to bring better products, services, and customer service in the insurance industry.

A forward-looking plan of action, sufficient operational flexibility, an effective implementation strategy, and a willingness to adopt digital disruptions in every aspect of their organization – those insurers that have all of the above can position themselves to leverage the impending digital disruptions to propel their organization to the very forefront of the industry.

DEALING WITH THE DIGITALIZATION OF THE INSURANCE INDUSTRY

These adopters of digital technology will have a clear upper hand against their competition. Suitably equipped to cut costs and design more attractive offerings, the digital insurance carriers are sure to acquire a whole new set of customers, thus increasing market share. Those who fail to quickly adopt the new technologies, on the other hand, will struggle to maintain their competitive positions in the midst of a customer-centric, price-sensitive market.

Data has always been at the center of the insurance industry, and despite the changes that are to come, data will continue to be the focal point of the industry. In fact, it’s set to play a bigger role to play than ever before.

The continued criticality of data in the insurance landscape is ensured by carriers’ need for information-driven strategies in the digitalized business scenario. They’ll have to leverage data as an asset, enabling automated decision-making in critical business processes, in order to thrive. This, in turn, is why a digital business technology platform – one that incorporates information management and analytical capabilities – will become a necessity in the future.

Without a system in place to support the analytics and reporting needs of the business, decision-makers may be left with no choice but to rely on conventional time-consuming manual processes those are more qualitative rather than quantitative in nature. This is bound to cause serious repercussions for the organization, ultimately resulting in missed opportunities and loss of competitiveness.

According to a Gartner study, the two following technology platforms are essential for any digital business:

  1. Data and analytics platform – This platform should consist of data management programs and analytics applications to enable data-driven decision making
  2. Ecosystems platform – This platform’s role should be to support the creation of and connection to external ecosystems, marketplaces, and communities

MFX

 

Click here to access MFX’s detailed White Paper