Les besoins verticaux définissent la marche à suivre pour les transformations de produits numériques et les stratégies

Les initiatives de transformation numérique se déroulent différemment selon les secteurs verticaux et les entreprises, en fonction des besoins métiers en jeu. Lorsque les entreprises subissent des transformations numériques, elles se concentrent souvent sur

  • les processus informatiques,
  • les ventes et le marketing

avant le développement des produits. Cependant, ce rapport expliquera aux DSI et aux directeurs de la technologie comment les entreprises de différents secteurs verticaux utilisent l’organisation produits comme catalyseur de leur transformation numérique, et comment cette décision améliore leurs relations avec les clients.

Principales conclusions

Les sociétés de produits physiques se concentrent sur l’IoT

Pour les organisations produits physiques, l’étape évidente vers une entreprise numérique consiste souvent à connecter des produits et des actifs. Il s’agit d’une tâche complexe qui nécessite

  • une infrastructure technologique intégrée,
  • une grande compétence dans la connectivité et l’Internet des objets (IoT),
  • ainsi qu’une logique claire sur la façon dont les produits connectés répondront aux besoins de leurs clients.

Les sociétés de services construisent des plates-formes numériques orientées client

Les entreprises du secteur des services basculeront vers le commerce numérique grâce à des plateformes numériques axées sur la clientèle. Ces projets doivent être

  • faciles à utiliser,
  • évolutifs
  • et intégrés aux partenaires de l’écosystème

afin de créer de la valeur pour les clients.

Diapositive1

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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

cloud

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 Global Trends in 2017

A brief summary of the key regional trends :

  • Analytics, Customer Centricity and Digital Innovation achieve similar scores across all our regions.
    • Customer-Centricity trails marginally in North America.
    • Noteworthy is the perfect score of 60 attained for Digital Innovation in Asia-Pacific, which indicates that this was the number-one priority here in all four measures underlying the priority score (money, time, staffing and training).
  • Underwriting and Risk Management both score considerably higher in North America than they do elsewhere – as we saw in the first priorities table, Underwriting is 3rd in the list of priorities in North America, despite not getting above 7th place in any other regions, and its Risk Management score is more than 80% higher than the runner-up’s (Europe).
  • There is a step-up in focus on Claims in Europe and North America compared to Asia-Pacific.
  • With Distribution Diversification, we have the exact inverse scenario, with Asia-Pacific leading the pack, possibly a reflection of the emerging markets within it necessitating high-scale low-cost distribution, which traditional models cannot provide.
  • Fraud is also a marginally higher priority in Asia-Pacific.
  • Europe and Asia-Pacific lead North America with their focus on Internet of Things.
  • Cybersecurity and Mobile achieve similar (lowish) scores for all regions; Product Development is relatively high across the board.
  • Regulation is the biggest deal in Europe, where respondents quoted in particular Solvency II and the Insurance Distribution Directive (IDD) as being causes for concern.

InsuranceNexus

Click here to access Insurance Nexus detailed survey analysis