Advanced Analytics for Insurers:How to build and exploit enterprise-grade machine learning capabilities

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Insurers have long been leading proponents of using data for pricing and risk management, and have therefore invested significantly in technologies and people to support these functions. However, the prolonged focus on these traditional capabilities has resulted in a partial failure to exploit the maturity of advanced analytics. Other industries far outpace insurance in terms of their analytics capabilities, and are re-engineering their end-to-end value chains around machine learning.

Insurers gather enormous quantities of data. Some of this data feeds existing Management Information (MI) and Business Intelligence (BI) applications, but the majority of the data remains unused and unexplored as it may be unstructured, excluded from the traditional MI infrastructure, or its value simply not recognised. Maturing technologies such as cloud-based NoSQL databases, open-source R language, data ingestion and visualisation layers are transforming the pace at which

data can deliver tangible and actionable value using analytics. Forward-looking and challenger insurers are already transforming their businesses on a foundation of analytics, and they aim to significantly outperform their more traditional competitors as a result.

It is becoming indisputable, based on events in other industries, that companies which do not use advanced analytics to power their businesses will be outcompeted. Insurers are too early in the advanced analytics adoption curve to observe significant differences in business performance between insurance analytics leaders and laggards, but there is no doubt that this is coming. Given this imperative for making advanced analytics a core business competency, insurers should seek to maximise the benefits of this technology by ensuring they have built the requisite Data, Technology and Teams.

Introduction

The various segments of the insurance industry – Life & Pensions, General Insurance, Commercial & Specialty, and Reinsurance – each have very distinct characteristics in terms of their product lines and target clients. Nonetheless, what they do all have in common is that they are data-driven businesses.

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The fundamental issue is that data is typically organised, managed and governed for MI before being adapted for analytics. This is the cause of increasing frustration for data scientists within organisations, who spend most of their time re-engineering data for analytics purposes and then trying to retrofit the outputs into existing systems. Insurers need to rethink how they structure their data pipeline for MI and analytics, taking the following into account:

1. Machines learn from data in a different way to humansHuman-led analysis typically starts with a series of hypotheses which are then tested on a conformed, clean set of data. Machine learning does not work this way. In order to build data for advanced analytics, three tenets should be adhered to:

Gather ALL of the dataThis point has certainly been laboured by Big Data advocates and it is still salient – because machine learning can infer from 1000s of data items, the more data thrown at it the better. Although insurers have had some success combining internal data from across the business, there are still opportunities to gather more transactional and unstructured data from areas such as claims and digital channels. Insurance significantly lags some industries in the use of external data, including freely available web data, open datasets published by governments, or data created by the very entities being insured.

Keep it as raw as possibleThere are numerous ways raw data can be aggregated and summarised for the purpose of ‘feature engineering’. One employee’s approach to doing this will often not fully satisfy another colleague’s needs. By keeping data in its raw format, new features can be continually added to the organisation’s ‘insight account’ over time. Furthermore, new machine learning algorithms (such as deep learning) create new features automatically, and have frequently been proven to be better at it than humans.

Experimental data is kingData produced from a controlled experiment (e.g. an A/B test of a quote page, or a random allocation of claims to teams) can prove causality, whereas the insurer’s traditional use of observational data can only show correlation. Proving how one action causes another is how businesses make better decisions. New machine learning algorithms can identify causality, thereby engineering the shift from predictive to prescriptive analytics (see sidebar).

Data Built for Analytics

It is time for analytics to become a first-class citizen when it comes to data. Insurers have been using data since the birth of the industry, but this has hindered organisations attempting to move up the maturity curve and use more advanced analytics.

What is prescriptive analytics?Prescriptive analytics identify the action or set of actions which are most likely to optimise a result. By contrast, predictive models will evaluate the probability of an event occurring. For example, a predictive model for customer retention will evaluate the probability of renewal whereas a prescriptive model will identify the next best actions (such as changing price, out-bounding the customer, or offering an incentive) which will improve the probability of renewal. Machine learning can enhance prescriptive models and estimate the change in KPIs such as Customer Lifetime Value or customer satisfaction scores. These models are more closely aligned to what insurers are trying to achieve, i.e. improvements in value through making the right customer decisions.

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Machine Learning Model Development

Historical Today

Time Series Data Store

Compute Scale Up/Down

VIRTUAL ANALYTICAL RECORDSNow

Visualisation Analytics App API Store

Ad Hoc Data External Data Observational Data Experimental Data

Internal External

New ModelsNew Features

Building a data platform for advanced analytics

2. Grow the organisation’s insight accountThe majority of corporate insights reside within desktop computers and laptops rather than data farms. As data science traditionally took place using desktop software such as SAS and R, most insurers have fragmented sandpits spread across each functional area, along with repeated analytical dataset creation and lost knowledge due to employee churn. The alternative is not to try and centralise these data sets into monolithic analytical records. Instead, virtual analytical records should be maintained that allow data scientists to recreate data sets across any time period and for any customer cohort.

By designing and governing how these virtual data models are updated over time, including opportunities for new features to be rated and promoted, insurers can start creating a wealth of knowledge that the entire organisation will benefit from.

3. Reduce the friction from insight to actionThere is nothing more frustrating for a data scientist than creating a model that cannot be deployed. Many organisations are not set up to deliver analytics to where the business decisions are made. Such cases require a laborious translation of the data scientist’s desktop model (with its numerous hand-coded features)

into enterprise-ready Extract, Transform and Load (ETL) code ready for scoring in a decision system. Luckily there is another way. By creating a centralised virtual analytical record of the initial predictive analytical model (built on historical data), it can be used in near-real time by any decision system through an API call. This removes the need to translate model outputs, reduces errors at run-time, gets benefits flowing quicker and makes everyone’s lives simpler.

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1. Every analytics business is a cloud businessJust as most organisations no longer generate their own electricity, it is unlikely that organisations in the near future will continue to rely solely on their own compute infrastructure for all their computing needs. Advanced analytics thrives on variable bursts of storage and compute. Many algorithms fall into a category called ‘embarrassingly parallel’ – these are algorithms that are so easy to run in parallel that people are embarrassed they did not do it sooner. This means that giving one data scientist tens of compute units means they get answers out quicker. Giving them bursts of thousands of compute units means they get even better answers, and in a shorter time. One trade-off for this improved performance can be less-efficient memory usage, and therefore storage needs to be equally scalable.

Cloud services provide this burst capacity, but it is imperative that the rest of the analytics technology stacks can also support this burst capacity. Analytics technology should be bought ‘by the drink’, with commercials built around usage by node or user.

2. Analytics needs the right toolsData scientists need to experiment, using toolsets that enable rapid data manipulation, feature engineering and analysis. Data engineers need robust ETL toolsets that will not fall over every night. It is important to consider a balance of both of these toolsets, and to build processes that deploy insights and models from one environment to the other (again ensuring they work from the same virtual analytical records).

For machine learning, the statistical programming language R is king. It is the most popular language across most universities, so the next generation of data scientists already know it. As an open-source framework, it receives the vast majority of new algorithms developed by the research community, helping users stay at the cutting-edge. Additionally, whilst many algorithms are available in other tools, R has a growing repository of packages that help open the black box and allow glimpses inside models (see sidebar).

3. Design analytics to enable rapid insight to actionThere are three primary value drivers to consider when making technology choices:

Deliver packaged insightsEnable teams across the organisation to develop insights by giving them restricted access to the virtual analytical records through a modern visualisation tool.

Empower the end-user Most employees within an insurance company (from claims handlers to call centre agents) are potential end-users of the outputs from these machine learning algorithms. In order to facilitate this, analytics apps should translate these outputs into straightforward data-led workflows. Metrics captured by these workflows can also provide dynamic feedback of actions performed and levels of adoption.

Ensure models deliver near-real time responsesIncreasingly, analytics outputs automate decision making in real time. The prime example of this is providing quotes through insurance aggregators. Near-real time responses to high-volume requests can be delivered through APIs calling the analytics models on a cloud-based analytics technology platform.

Technology Built for Analytics

Advanced analytics requires different technology stacks from those used to deliver MI and BI. When describing the analytical technology landscape of most insurers, it is hard to escape using the phrase ‘one expensive mess’. This is due to technology being originally built to support MI reporting and a slower cycle of analysis. Points to consider when working out how to build and deploy technology for analytics include the following:

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Peering inside the black boxMachine learning algorithms are typically described as ‘black boxes’ in that the business cannot see or understand their internal workings. Technological advances have now made it possible to ‘peer inside the black box’ and see what the machine learning is doing. This gives insurers the best of both worlds – the power of machine learning algorithms and the insight into how they work.

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In such cases, enthusiasm gradually turns to frustration, and then to cynicism. By the time analytics teams finally demonstrate progress to stakeholders, they learn that priorities have moved on, core objectives have been misinterpreted from the outset, and that there is no hope of implementing any of the generated insights. It does not have to be this way.

Typically the root cause of these problems is a failure to create the right team and working practices from the outset. All too often, businesses attempt to leverage the management structure created for MI / BI and apply it to the delivery of advanced analytics. MI teams create highly regimented outputs that must be repeated consistently in order to tell the business how it is performing – typically ‘lag indicators’. Conversely, analytics teams need to be highly creative, seeking out new value that has not previously been considered anywhere within the organisation.

The best-performing analytics delivery teams adopt and adapt many of the key principles of agile-lean delivery. This means focusing on value, championing speed, and taking an organisational approach that ensures a transparent product is created within weeks of starting projects. Creating value from embracing analytics throughout the enterprise is now a matter of life and death for all insurance companies. The most critical element of this is delivering on the analytics promise and moving from data to business value, and this can only be achieved by having the right team. The following factors are key to delivering on the analytics promise:

1. The right individuals, with the right skills, doing the right jobsAgile delivery methodologies advocate small, autonomous teams who self-organise and deliver value at speed. In keeping with this approach, analytics teams need the following members who are recruited as the best people for the job and are determined to do things in a new way:

Data scientists Able to apply the latest advanced analytical techniques and capabilities such as sound statistical theory, machine learning, natural language processing and text mining to turn vastly complex, traditional and non-traditional data sets into simple and actionable insight.

Visualisation engineers With the ability to demonstrate the findings in an engaging and stakeholder-ready format, this team is critical in maintaining the engagement of the business.

Data engineer The member of the team who can bring the benefits of the advanced analytics IT stacks to the front-line analytics team, who creates the right data ingestions, helps to build analytical records with all the data that will be needed, and has a deep understanding of the systems that create the source data.

Business interlock Akin to the classic agile scrum master, and with very similar responsibilities: a servant-leader who maintains and coaches on agile practices and ceremonies and demonstrates progress with daily burndown charts. The analytics specific element involves empowering the agile analytics team and the wider enterprise data teams (including business insight and data governance teams) to ensure they are not being disrupted by legacy working practices.

Product owner A critical role, the product owner decides on the analytics priorities, has a deep understanding of the drivers of business value and the organisation’s strategic priorities, and also represents the end-users of the analytics outputs regarding how they will successfully work with those outputs.

2. Deliver value at paceOne of the reasons that agile working practices work so well for analytics projects is their relentless focus on velocity and time-boxed delivery of a potentially shippable product:

• Sprint practices should be adopted. Shippable product means analytical insights that are ready to be implemented into a live environment via scientific experiment. It does not mean a presentation of progress and insights only.

Teams Built for Analytics

Analytics teams need to be structured to deliver value at pace. Many analytics projects seem destined to fail from the outset: projects are unable to meet expectations of value or achieve the desired scalability throughout the business; there is disagreement regarding what should be delivered, an inability to implement findings and, critically, actionable insights take far too long to arrive.

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• Newly created agile analytics delivery teams need to plan very clearly and ensure that they are starting small and working on priorities that can be delivered within a two to four-week sprint window.

• This makes the ability to deliver the insight / experiment into the live environment a critical pre-requisite. If this is not already in place (and it usually is not), then it should almost always be the focus of the first one to three sprints.

3. The right working practices • Co-location is key: the data

scientists, visualisation engineers and data engineer must be in the same room at the same time throughout, in order to minimise wasted time and allow immediate interventions when team members hear misunderstandings emerging.

• Highly organised lock-in sessions allow for time-boxed periods where product owners can review working code, see progress that has been made, encourage the team, and make immediate decisions regarding the insights being created.

• Transparent working practices, including standard agile Kanban boards and burndown charts, ensure that the team and all interested stakeholders are immediately aware of progress against the sprint objectives.

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Business Accumen

Communication and Collaboration

Advanced Analytics

Creativity

Data Visualisation

Software Development

Data Integration

Systems Administration

Agile Analytics

BusinessInterlock

DataScientist

DataEngineer

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VisualisationEngineer

Assembling the right talent to deliver analytics through Agile methodologies

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Conclusion

Insurers can gain a significant competitive advantage in the market and out-perform their peers by re-engineering their end-to-end value chain using machine learning. Despite widespread recognition of this fact, most insurers are held back by a legacy setup and a mistaken belief that advanced analytics can be executed using traditional approaches and existing data. By building new, analytics-specific capabilities across Data, Technology and Teams, insurers can deliver near-real time, actionable insights to their decision makers throughout the business, thereby unlocking the true potential of their data.

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Contact the authorsMax RichterManaging Director, UK Insurance Analytics Leadmax.richter@accenture.com

Matthew O’KaneSenior Manager, UK FS Analytics Leadmatthew.okane@accenture.com

Peter TemperleySenior Manager, UK Insurancepeter.j.temperley@accenture.com

Benjamin TyteSenior Manager, UK Insurancebenjamin.tyte@accenture.com

About AccentureAccenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 375,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.