Tag Archives: deep learning

AI in a Post-Pandemic Future

The COVID-19 pandemic put businesses under extreme pressure and has led to a massively accelerated digitalization of the workplace. The silver lining is the opportunity to develop more efficient, digital operating models by reinventing work and leveraging the power of artificial intelligence and automation.

Artificial intelligence and why it matters

Hype has for some time surrounded AI, but promises first made more than 60 years ago are now finally being delivered. What has been the game changer responsible for putting AI back on the map and on the verge of changing, well, just about everything? The answer is deep learning, an old idea that found an opportunity to mature in the late 1990s and early 2000s. 

Based on learning tasks using artificial neural networks inspired by the biological nervous system, deep learning technology is highly advanced and requires vast volumes of data and computing power only recently made possible. By 2030, AI is estimated to contribute as much as $15 trillion to the world economy, making it the biggest commercial opportunity in today’s fast-changing economy. Indeed, the new realities of the post-COVID-19 world require the accelerated adoption of AI to deliver the efficiencies and augmentations of a highly digitized workplace.

Figure 1: AI’s projected impact on global GDP

For more than 250 years, the fundamental drivers of economic growth have been technological innovations, the most important being general-purpose technologies such as electricity and the steam engine. Now it is AI that stands out as the transformational technology of our digital age, which, as with previous GPTs (general purpose technologies), is expected to trigger waves of complementary innovations and opportunities.

What tangible opportunities does AI offer businesses right now? We are currently witnessing the first wave, usually as a result of companies automating tasks and processes, reducing costs and creating more efficiencies. The work dividends from this first wave are mostly positive. Low-level, tedious, hazardous and boring tasks are taken over by machines, freeing time for the humans to do the higher-level, more productive tasks. 

Significant shifts in computing power and availability of large-scale data advance the development of AI applications that continue to rapidly grow in complexity and autonomy. AI’s autonomous nature and the way it is trained on data – essentially learning from the mistakes made in the past – make the technology both an opportunity and a risk.

See also: 4 Post-COVID-19 Trends for Insurers

AI at work

As organizations deploy technologies that automate work or introduce machine intelligence in the organization, the limiting factor in translating these innovations into real business benefits will be talent. Beyond the designers, developers and data scientists that everyone is battling for today, companies will need to explore what new roles are likely to emerge in digital disruptors.

As with many professions, underwriters have been doing a job one way for decades and now are expected to do things differently. The role is primed for transformation as AI is poised to reconfigure and augment insurance underwriting. Fueled by an explosion of data, low-cost data storage and open source technology, AI has the potential to help underwriters analyze an incredible amount of information, find red flags and help make more accurate decisions. 

While there is no expectation for human underwriters to be replaced, as their judgment will still be needed for complex cases, future underwriters will be expected to work alongside AI systems to ensure all risks are accurately measured and priced. As underwriters increasingly interact with automated AI systems, there will be a need for new skill sets to develop, with some old skills potentially becoming obsolete.

Meanwhile, demand for these new skills far outstrips supply at present, which indicates that the main roadblock to insurers capturing the full value of this new technology is not the science, but the human change management factor. It is a tall order, but starting by having the right people with the right skills in the right roles will far outweigh picking the right technology, algorithm or latest start-up to work with.

More digital, more human

One of the major transformations of the digital age is to see more companies adopting a flat working structure, where career paths are less clear and the turnaround of young talent greater. In this new environment, a next-generation operating model that supports the opportunity to learn skills, to have thought leaders provide mentoring and to involve new staff in meaningful projects will be critical to attract and retain the best digital talent. 

By moving beyond a one-size-fits-all approach to human resources and talent management, digital workforce platforms can help create the conditions in which employees feel energized by their work, valued by their organization and happy in their environment.

Google and Apple are examples of early adopters of digital workforce platforms that built ecosystems allowing them to innovate, take advantage of new technologies to cut costs, improve quality, build value and respond quickly to the fast-changing and rising digital expectations of consumers. How can this model be replicated across other industries?

The answer may depend on the ability of corporate leaders to restabilize the workforce — and to reconceive organizational structures — by using the very same digital technologies that have destabilized it in the first place. The incoming AI revolution should reinforce, not weaken, the uniquely human characteristics that define how we work, particularly in the way that we collaborate, communicate and develop relationships. To fully exploit emerging digital capabilities, most organizations will continue to depend on people, with human skills actually becoming more critical in the digital world, not less. 

See also: Stop Being Scared of Artificial Intelligence

As tasks are automated, they tend to become commoditized; a “cutting edge” technology such as smartphone submission of insurance claims quickly becomes almost ubiquitous. In many contexts, therefore, competitive advantage is likely to depend even more on human capacity, on providing thoughtful advice to an investor saving for retirement or calm guidance to an insurance customer after an accident.

AI is likely to be one of the biggest game changers in insurance history, offering a wide range of opportunities from faster and more efficient claims management to a greater variety of on-demand insurance services. As organizations transform to thrive in a digital environment, their success will be affected by how well they integrate their workforce into the transformation journey and manage the tension between the constant drive to innovate and improve and the new governance, compliance and regulatory risks created by new AI technologies. Digital transformation requires the overhaul of culture beyond technology updates or process redesign to reap the anticipated benefits.

How to Use AI in Customer Service

How to manage the increase in incoming unstructured information is a key challenge in the insurance industry—we explore how Accenture’s Machine Learning Text Analyzer can achieve this using historical data.

How do you approach customer service and policy administration within your organization? In this blog post, I’ll demonstrate how artificial intelligence (AI) and a raised AIQ can help you get the most out of your data. (For the other articles in this series, click here.) To do this, I’ll discuss how insurers can use machine learning to analyze texts.

How can insurers use AI in customer service and policy administration?

The customer service and policy administration workforce can make their lives easier by using AI to:

  • Understand and act on external emails and requests.
  • Automate call center and webchat services—helping companies get on with more intricate work.
  • Enable self-service queries on policy issuance, endorsements, cancellations and renewals—using virtual assistants, for example.
  • Process unstructured data, which means fewer mistakes and better customer service.

How does AI improve customer services and policy administration?

AI enables more efficient administration processes. Insurance executives plan to invest in seven AI-related technologies in the next three years. They are: 

  • Machine learning; 
  • Deep learning; 
  • Natural language processing; 
  • Video analytics; 
  • Embedded AI solutions; 
  • Robotic process automation; 
  • Computer vision. 

See also: Policy Administration: Ripe for Modernizing  

In addition to increasing the efficiency of administration processes and enhancing analytical insights, AI technologies also benefit customer services through:

As I will show in the use case below, the customer service and policy administration workforce can use machine learning to process information faster and with greater accuracy.

Use case: Machine Learning Text Analyzer (MALTA)

Insurers today must figure out how to manage the exponential increase in incoming unstructured data. Eighty percent of data generated is unstructured, and the volume continues to grow exponentially. Forty percent of business executives complain that they have too much unstructured text data and don’t know how to interpret it.

Insurers face three main challenges:

1. Too much unstructured information

  • A large amount of information comes in through a variety of channels;
  • Incoming data is structured as well as unstructured;
  • Much of the workforce is occupied with processing unstructured information;
  • A large amount of unstructured information exists within the organization.

2. Too many communication channels

Customers use a large variety of channels to communicate with their insurance company, such as e-mail, contact forms, the service desk (e.g. ticketing), letters and applications.

3. The information is not linked to business processes

  • Workers lose a lot of time when they have to identify received information and allocate requests to the right channels;
  • They also lose time owing to inefficient processes caused by breaks in the system;
  • This prolongs the response time to clients;
  • Humans are prone to errors, which creep in at all points.

Solution: Machine Learning Text Analyzer (MALTA)

Now, insurers can automate the analysis and classification of incoming text by applying machine learning and using historical data.

How does MALTA work in customer service and policy administration?

MALTA can analyze any incoming documents, for example when customers send their policy documents via email.

These documents can be analyzed and classified using natural language processing methods and machine learning algorithms. MALTA is also trained with historical data, which enables it to classify, understand and extract information.

In the next step, MALTA links your customer’s policy document to business processes, prompting different functions to take action. Depending on the business and architecture set-up, MALTA or the output of the API triggers a process chain, a robot or an agent so that the necessary processing steps can be executed.

See also: In Age of Disruption, What Is Insurance?  

Benefits of MALTA

MALTA is flexible, customizable, independent, multilingual, state-of-the-art and end-to-end; using Accenture’s machine learning text analyzer, insurers can:

  • Increase classification accuracy and efficiency, and reduce errors.
  • Create individual learning models based on training data.
  • Deploy the solution on-premise, not only in the cloud.
  • Automate repetitive tasks, allowing employees to focus on more complex work.
  • Categorize new requests immediately and send them to the relevant departments.
  • Use state-of-the-art models and tools.
  • Work on a platform-independent web service.
  • Carry out classification outside regular business hours.
  • Cleanse data and extract and evaluate features.
  • Link robotics and process automation tools to classification.
  • Set up and train employees with minimal effort.

In addition to customer services and policy administration, insurers can use MALTA across other parts of the enterprise, for example:

Are you ready to power up your business with AI? Download the report on How to boost your AIQ for more insight.

10 Trends on Big Data, Advanced Analytics

Recently, I was invited to present on the impact of big data and advanced analytics on the insurance industry at the NCSL Legislative Summit. This talk couldn’t have been timelier, as the insurance sector now tops the list of most disrupted sectors. Some of the culprits and causes for this top spot are related to the speed of technological change, changing customer behavior, increased investments in the insurtech sector and new market entrants, such as homeowners and renters insurance startup Lemonade. A significant driver of this disruption is technological change – especially in big data and advanced analytics.

See also: Why to Refocus on Data and Analytics  

Here are 10 key trends that are affecting big data and advanced analytics – most of which have a hand in disrupting the insurance industry:

  1. Size and scope – Big data is getting bigger and faster. With connected cars, homes and buildings, and machines, the amount of data is increasing exponentially. Investments in IoT and Industrial IoT, 5G and other related areas will only increase the speed and amount of data. With this increased volume and velocity, we will not be able to generate meaningful insights from all of this data without advanced analytics and artificial intelligence.
  2. Big data technology – Big data technology is moving from Hadoop to streaming architectures to hybrid “translytical” databases. While concepts like “data lakes” and NoSQL databases mature, new technologies like Apache Spark, Tez, Storm, BigTop and REEF, among others, are creating a constant flow of new tools, which adds to a sense of “big data in flux.”
  3. Democratization – The democratization of data, business intelligence and data science is accelerating. Essentially, this means that anybody in a given organization with the right permissions can use any dataset, slice and dice the data, run analysis and create reports with very little help from IT or data scientists. This creates expectations for timely delivery, and business analysts can no longer hide behind IT timelines and potential delays.
  4. Open source movement – The open source revolution in data, code and citizen data scientist is accelerating access to data and generation of insights. Open source tools are maturing and finding their way into commercial vendor solutions, and the pace of open source tool creation is continuing unabated; the Apache Software Foundation lists more than 350 current open source initiatives. This steady stream requires data engineers and data scientists to constantly evaluate tools and discover new ways of data engineering and data science.
  5. Ubiquitous intelligence – Advanced analytics – especially various types of artificial intelligence areas (reference to my AI report post) – is evolving and becoming ubiquitous intelligence. AI can now interact with us through natural language, speak to us, hear us, see the world and even feel objects. As a result, it will start seamlessly weaving itself into many of our day-to-day activities, such as using a search engine or sorting our email, recommending things to buy based on our preferences and needs, seeing the world and guiding us through our interaction with other people and things without our even being aware of its doing so. This will further heighten our sense of disruption and constant change.
  6. Deep learning – Deep learning, a subset of the machine learning family (which itself is just one area of AI), has been improving in speed, scale, accuracy, sophistication and the scope of problems it addresses. Unlike previous techniques, which were specific to the different type of data (e.g., text, audio, image), deep learning techniques have been applied across all different types of data. This has contributed to reduced development time and greater sharing and broadened the scope of innovation and disruption.
  7. MLaaS – Machine learning, cloud computing and open source movement are converging to create Machine Learning as a Service (MLaaS). This not only decreases the overall variable costs of using AI but also provides large volumes of data that the machine learning systems can further exploit to improve their accuracy, resulting in a virtuous cycle.
  8. Funding – Big data funding peaked in 2015. However, funding for artificial intelligence, especially machine learning and deep learning, has continued to attract increasingly significant investments. In the first half of this year, more than $3.6 billion has been invested in AI and machine learning. This increased funding has attracted great talent to explore difficult areas of AI that will be disruptors of the future economy.
  9. Center of Excellence: As organizations continue to obtain good ROI from their initial pilots and proof-of-concepts in analytics, automation and AI efforts, they are increasingly looking toward setting up centers of excellence where they can train, nurture and grow the talent. The exact role of the center changes based on the overall organizational culture and how the rest of their business operates – centralized, federated or decentralized.
  10. Competitive landscape – The big data landscape continues to grow, and the AI landscape is expanding rapidly. Deep learning companies are growing the fastest across multiple sectors. Competition among startups – as well as incumbents that want to stay ahead of potential disruption – is creating a vibrant ecosystem of partnerships and mergers and acquisitions that further the disruptive cycle.

See also: Analytics and Survival in the Data Age  

Are there other trends you would add to the list? Share them here!