EXPOSURE
The RMS Magazine with a unique perspective and clear mission “... to provide insight and analysis to help insurance and risk professionals innovate, adapt, and deliver.”
Insurance: The next 10 years
Mohsen Rahnama, Cihan Biyikoglu and Moe Khosravy of RMS look to 2029, consider the…
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A New Way of Learning
EXPOSURE delves into the algorithmic depths of machine learning to better understand the data potential that it offers the insurance industry. Machine learning is similar to how you teach a child to differentiate between similar animals,” explains Peter Hahn, head of predictive analytics at Zurich North America. “Instead of telling them the specific differences, we show them numerous different pictures of the animals, which are clearly tagged, again and again. Over time, they intuitively form a pattern recognition that allows them to tell a tiger from, say, a leopard. You can’t predefine a set of rules to categorize every animal, but through pattern recognition you learn what the differences are.” In fact, pattern recognition is already part of how underwriters assess a risk, he continues. “Let’s say an underwriter is evaluating a company’s commercial auto exposures. Their decision-making process will obviously involve traditional, codified analytical processes, but it will also include sophisticated pattern recognition based on their experiences of similar companies operating in similar fields with similar constraints. They essentially know what this type of risk ‘looks like’ intuitively.” Tapping the Stream At its core, machine learning is then a mechanism to help us make better sense of data, and to learn from that data on an ongoing basis. Given the data-intrinsic nature of the industry, the potential it affords to support insurance endeavors is considerable. “If you look at models, data is the fuel that powers them all,” says Christos Mitas, vice president of model development at RMS. “We are now operating in a world where that data is expanding exponentially, and machine learning is one tool that will help us to harness that.” One area in which Mitas and his team have been looking at machine learning is in the field of cyber risk modeling. “Where it can play an important role here is in helping us tackle the complexity of this risk. Being able to collect and digest more effectively the immense volumes of data which have been harvested from numerous online sources and datasets will yield a significant advantage.” “MACHINE LEARNING CAN HELP US GREATLY EXPAND THE NUMBER OF EXPLANATORY VARIABLES WE MIGHT INCLUDE TO ADDRESS A PARTICULAR QUESTION” CHRISTOS MITAS RMS He also sees it having a positive impact from an image processing perspective. “With developments in machine learning, for example, we might be able to introduce new data sources into our processing capabilities and make it a faster and more automated data management process to access images in the aftermath of a disaster. Further, we might be able to apply machine learning algorithms to analyze building damage post event to support speedier loss assessment processes.” “Advances in natural language processing could also help tremendously in claims processing and exposure management,” he adds, “where you have to consume reams of reports, images and facts rather than structured data. That is where algorithms can really deliver a different scale of potential solutions.” At the underwriting coalface, Hahn believes a clear area where machine learning can be leveraged is in the assessment and quantification of risks. “In this process, we are looking at thousands of data elements to see which of these will give us a read on the risk quality of the potential insured. Analyzing that data based on manual processes, given the breadth and volume, is extremely difficult.” Looking Behind the Numbers Mitas is, however, highly conscious of the need to establish how machine learning fits into the existing insurance eco-system before trying to move too far ahead. “The technology is part of our evolution and offers us a new tool to support our endeavors. However, where our process as risk modelers starts is with a fundamental understanding of the scientific principles which underpin what we do.” Making the Investment Source: The Future of General Insurance Report based on research conducted by Marketforce Business Media and the UK’s Chartered Insurance Institute in August and September 2016 involving 843 senior figures from across the UK insurance sector “It is true that machine learning can help us greatly expand the number of explanatory variables we might include to address a particular question, for example – but that does not necessarily mean that the answer will more easily emerge. What is more important is to fully grasp the dynamics of the process that led to the generation of the data in the first place.” He continues: “If you look at how a model is constructed, for example, you will have multiple different model components all coupled together in a highly nonlinear, complex system. Unless you understand these underlying structures and how they interconnect, it can be extremely difficult to derive real insight from just observing the resulting data.” “WE NEED TO ENSURE THAT WE CAN EXPLAIN THE RATIONALE BEHIND THE CONCLUSIONS” PETER HAHN ZURICH NORTH AMERICA Hahn also highlights the potential ‘black box’ issue that can surround the use of machine learning. “End users of analytics want to know what drove the output,” he explains, “and when dealing with algorithms that is not always easy. If, for example, we apply specific machine learning techniques to a particular risk and conclude that it is a poor risk, any experienced underwriter is immediately going to ask how you came to that conclusion. You can’t simply say you are confident in your algorithms.” “We need to ensure that we can explain the rationale behind the conclusions that we reach,” he continues. “That can be an ongoing challenge with some machine learning techniques.” There is no doubt that machine learning has a part to play in the ongoing evolution of the insurance industry. But as with any evolving technology, how it will be used, where and how extensively will be influenced by a multitude of factors. “Machine learning has a very broad scope of potential,” concludes Hahn, “but of course we will only see this develop over time as people become more comfortable with the techniques and become better at applying the technology to different parts of their business.”
The Day a Botnet Took Down the Internet
The Dyn distributed denial of service (DDoS) attack in October 2016 highlighted security flaws inherent in the Internet of Things (IoT). EXPOSURE asks what this means for businesses and insurers as the world becomes increasingly connected. A decade ago, Internet connections were largely limited to desktop computers, laptops, tablets, and smart phones. Since then there has been an explosion of devices with IP addresses, including baby monitors, connected home appliances, motor vehicles, security cameras, webcams, ‘Fitbits’ and other wearables. Gartner predicts there will be 20.8 billion things connected to the Internet by 2020. In a hyper-connected world, governments, corporates, insurers and banks need to better understand the potential for systemic and catastrophic risk arising from a cyber attack seeking to exploit IoT vulnerabilities. With few actual examples of how such attacks could play out, realistic disaster scenarios and cyber modeling are essential tools by which (re)insurers can manage their aggregate exposures and stress test their portfolios. “IF MALICIOUS ACTORS WANTED TO, THEY WOULD ATTACK CORE SERVICES ON THE INTERNET AND I THINK WE’D BE SEEING A NEAR GLOBAL OUTAGE” KEN MUNRO PEN TEST PARTNERS Many IoT devices currently on the market were not designed with strict IT security in mind. Ethical hackers have demonstrated how everything from cars to children’s toys can be compromised. These connected devices are often an organization’s weakest link. The cyber criminals responsible for the 2013 Target data breach are understood to have gained access to the retailer’s systems and the credit card details of over 40 million customers via the organization’s heating, ventilation and air conditioning (HVAC) system. The assault on DNS hosting firm Dyn in October 2016, which brought down multiple websites including Twitter, Netflix, Amazon, Spotify, Reddit, and CNN in Europe and the U.S., was another wake-up call. The DDoS attack was perpetrated using the Mirai virus to compromise IoT systems. Like a parasite, the malware gained control of an estimated 100,000 devices, using them to bombard and overwhelm Dyn’s infrastructure. This is just the tip of the iceberg, according to Ken Munro, partner, Pen Test Partners. “My first thought [following the Dyn attack] was ‘you ain’t seen nothing yet’. That particular incident was probably using the top end of a terabyte of data per second, and that’s nothing. We’ve already seen a botnet that is several orders of magnitude larger than that. If malicious actors wanted to, they would attack core services on the Internet and I think we’d be seeing a near global outage.” In the rush to bring new IoT devices to market, IT security has been somewhat of an afterthought, thinks Munro. The situation is starting to change, though, with consumer watchdogs in Norway, the Netherlands and the U.S. taking action. However, there is a significant legacy problem to overcome and it will be several years before current security weaknesses are tackled in a meaningful way. “I’ve still got our first baby monitor from 10 years ago,” he points out. “The Mirai botnet should have been impossible, but it wasn’t because a whole bunch of security camera manufacturers did a really cheap job. IT security wasn’t on their radar. They were thinking about keeping people’s homes secure without even considering that the device itself might actually be the problem.” In attempting to understand the future impact of such attacks, it is important to gain a better understanding of motivation. For cyber criminals, DDoS attacks using IoT botnets could be linked to extortion attempts or to diverting the attention of IT professionals away from other activities. For state-sponsored actors, the purpose could be more sinister, with the intent to cause widespread disruption, and potentially physical damage and bodily harm. Insurers Stress-Test “Silent” Cyber It is the latter scenario that is of growing concern to risk and insurance managers. Lloyd’s, for instance, has asked syndicates to create at least three internal “plausible but extreme” cyber attack scenarios as stress-tests for cyber catastrophe losses. It has asked them to calculate their total gross aggregate exposure to each scenario across all classes, including “silent” cyber. AIG is also considering how a major cyber attack could impact its book of business. “We are looking at it, not only from our own ERM perspective, but also to understand what probable maximum losses there could be as we start to introduce other products and are able to attach cyber to traditional property and casualty policies,” explains Mark Camillo, head of cyber at AIG. “We look at different types of scenarios and how they would impact a book.” AIG and a number of Lloyd’s insurers have expanded their cyber offerings to include cover for non-damage business interruption and physical damage and bodily harm arising from a cyber incident. Some carriers – including FM Global – are explicitly including cyber in their traditional suite of products. Others have yet to include explicit wording on how traditional products would respond to a cyber incident. “WE HAVE RELEASED A NUMBER OF CYBER-PHYSICAL ATTACK SCENARIOS THAT CAUSE LOSSES TO TRADITIONAL PROPERTY INSURANCE” ANDREW COBURN RMS “I don’t know if the market will move towards exclusions or including affirmative cyber coverage within property and casualty to give insureds a choice as to how they want to purchase it,” states Camillo. “What will change is that there is going to have to be some sort of due diligence to ensure cyber exposures are coded properly and carriers are taking that into consideration in capital requirements for these types of attacks.” In addition to markets such as Lloyd’s, there is growing scrutiny from insurance industry regulators, including the Prudential Regulation Authority in the U.K., on how a major cyber event could impact the insurance industry and its capital buffers. They are putting pressure on those carriers that are currently silent on how their traditional products would respond, to make it clear whether cyber-triggered events would be covered under conventional policies. “The reinsurance market is certainly concerned about, and constantly looking at the potential for, catastrophic events that could happen across a portfolio,” says William Henriques, senior managing director and co-head of the Cyber Practice Group at Aon Benfield. “That has not stopped them from writing cyber reinsurance and there’s enough capacity out there. But as the market grows and gets to US$10 billion, and reinsurers keep supporting that growth, they are going to be watching that accumulation and potential for catastrophic risk and managing that.” Catastrophic Cyber Scenarios In December 2015 and again in December 2016, parts of Ukraine’s power grid were taken down. WIRED magazine noted that many parts of the U.S. grid were less secure than Ukraine’s and would take longer to reboot. It was eerily similar to a fictitious scenario published by Cambridge University’s Centre for Risk Studies in partnership with Lloyd’s in 2015. ‘Business Blackout’ considered the impact of a cyber attack on the US power grid, estimating total economic impact from the 1-in-200 scenario would be US$243 billion, rising to US$1 trillion in its most extreme form. It is not beyond the realms of possibility for a Mirai-style virus targeting smart thermostats to be used to achieve such a blackout, thinks Pen Test Partners’ Ken Munro. “You could simultaneously turn them all on and off at the same time and create huge power spikes on the electricity grid. If you turn it on and off and on again quickly, you’ll knock out the grid – then we would see some really serious consequences.” Smart thermostats could be compromised in other ways, for instance by targeting food and pharmaceutical facilities with the aim to spoil goods. There is a commonly held belief that the industrial and supervisory control and data acquisition systems (ICS/SCADA) used by energy and utility companies are immune to cyber attacks because they are disconnected from the Internet, a protective measure known as “air gapping”. Smart thermostats and other connected devices could render that defense obsolete. In its Cyber Accumulation Management System (CAMS v2.0), RMS considered how silent cyber exposures could impact accumulation risk in the event of major cyber attacks on operations technology, using the Ukrainian power grid attack as an example. “We’ve released a number of cyber-physical attack scenarios that cause losses to traditional property insurance,” explains Andrew Coburn, senior vice president at RMS and a founder and member of the executive team of the Cambridge Centre for Risk Studies. “We’re working with our clients on trying to figure out what level of stress test should be running,” he explains. “The CAMS system we’ve released is about running large numbers of scenarios and we have extended that to look at silent cover, things in conventional insurance policies that could potentially be triggered by a cyber attack, such as fires and explosions.” Multiple lines of business could be impacted by a cyber event thinks Coburn, including nearly all property classes, including aviation and aerospace. “We have included some scenarios for marine and cargo insurance, offshore energy lines of business, industrial property, large numbers of general liability and professional lines, and, quite importantly, financial institutions professional indemnity, D&O and specialty lines.” “The IoT is a key element of the systemic potential of cyber attacks,” he says. “Most of the systemic risk is about looking at your tail risk. Insurers need to look at how much capital they need to support each line of business, how much reinsurance they need to buy and how they structure their risk capital.” RMS CAMS v2.0 Scenarios Cyber-Induced Fires in Commercial Office Buildings Hackers exploit vulnerabilities in the smart battery management system of a common brand of laptop, sending their lithium-ion batteries into thermal runaway state. The attack is coordinated to occur on one night. A small proportion of infected laptops that are left on charge overnight overheat and catch fire, and some unattended fires in commercial office buildings spread to cause major losses. Insurers face claims for a large numbers of fires in their commercial property and homeowners’ portfolios. Cyber-Enabled Marine Cargo Theft From Port Cyber criminals gain access to a port management system in use at several major ports. They identify high value cargo shipments and systematically switch and steal containers passing through the ports over many months. When the process of theft is finally discovered, the hackers scramble the data in the system, disabling the ports from operating for several days. Insurers face claims for cargo loss and business interruption in their marine lines. ICS-Triggered Fires in Industrial Processing Plants External saboteurs gain access to the process control network of large processing plants, and spoof the thermostats of the industrial control systems (ICS), causing heat-sensitive processes to overheat and ignite flammable materials in storage facilities. Insurers face sizeable claims for fire and explosions in a number of major industrial facilities in their large accounts and facultative portfolio. PCS-Triggered Explosions on Oil Rigs A disgruntled employee gains access to a Network Operations Centre (NOC) controlling a field of oil rigs, and manipulates several of the Platform Control Systems (PCS) to cause structural misalignment of well heads, damage to several rigs, oil and gas release, and fires. At least one platform has a catastrophic explosion. Insurers face significant claims to multiple production facilities in their offshore energy book. Regional Power Outage From Cyber Attack on U.S. Power Generation A well-resourced cyber team infiltrates malware into the control systems of U.S. power generating companies that creates desynchronization in certain types of generators. Sufficient generators are damaged to cause a cascading regional power outage that is complex to repair. Restoration of power to 90 percent of customers takes two weeks. Insurers face claims in many lines of business, including large commercial accounts, energy, homeowners and speciality lines. The scenario is published as a Lloyd’s Emerging Risk Report ‘Business Blackout’ by Cambridge Centre for Risk Studies and was released in RMS CAMS v1.1. Regional Power Outage From Cyber Attack on UK Power Distribution A nation-state plants ‘Trojan Horse’ rogue hardware in electricity distribution substations, which are activated remotely to curtail power distribution and cause rolling blackouts intermittently over a multi-week campaign. Insurers face claims in many lines of business, including large commercial accounts, energy, homeowners and specialty lines. The scenario is published as ‘Integrated Infrastructure’ by Cambridge Centre for Risk Studies, and was released in RMS CAMS v1.1.
An Unparalleled View of Earthquake Risk
As RMS launches Version 17 of its North America Earthquake Models, EXPOSURE looks at the developments leading to the update and how distilling immense stores of high-resolution seismic data into the industry’s most comprehensive earthquake models will empower firms to make better business decisions. The launch of RMS’ latest North America Earthquake Models marks a major step forward in the industry’s ability to accurately analyze and assess the impacts of these catastrophic events, enabling firms to write risk with greater confidence due to the underpinning of its rigorous science and engineering. The value of the models to firms seeking new ways to differentiate and diversify their portfolios as well as price risk more accurately, comes from a host of data and scientific updates. These include the incorporation of seismic source data from the U.S. Geological Survey (USGS) 2014 National Seismic Hazard Mapping Project. First groundwater map for Liquefaction “Our goal was to provide clients with a seamless view of seismic hazards across the U.S., Canada and Mexico that encapsulates the latest data and scientific thinking— and we’ve achieved that and more,” explains Renee Lee, head of earthquake model and data product management at RMS. “There have been multiple developments – research and event-driven – which have significantly enhanced understanding of earthquake hazards. It was therefore critical to factor these into our models to give our clients better precision and improved confidence in their pricing and underwriting decisions, and to meet the regulatory requirements that models must reflect the latest scientific understanding of seismic hazard.” Founded on Collaboration Since the last RMS model update in 2009, the industry has witnessed the two largest seismic-related loss events in history – the New Zealand Canterbury Earthquake Sequence (2010-2011) and the Tohoku Earthquake (2011). “We worked very closely with the local markets in each of these affected regions,” adds Lee, “collaborating with engineers and the scientific community, as well as sifting through billions of dollars of claims data, in an effort not only to understand the seismic behavior of these events, but also their direct impact on the industry itself.” A key learning from this work was the impact of catastrophic liquefaction. “We analyzed billions of dollars of claims data and reports to understand this phenomenon both in terms of the extent and severity of liquefaction and the different modes of failure caused to buildings,” says Justin Moresco, senior model product manager at RMS. “That insight enabled us to develop a high-resolution approach to model liquefaction that we have been able to introduce into our new North America Earthquake Models.” An important observation from the Canterbury Earthquake Sequence was the severity of liquefaction which varied over short distances. Two buildings, nearly side-by-side in some cases, experienced significantly different levels of hazard because of shifting geotechnical features. “Our more developed approach to modeling liquefaction captures this variation, but it’s just one of the areas where the new models can differentiate risk at a higher resolution,” said Moresco. The updated models also do a better job of capturing where soft soils are located, which is essential for predicting the hot spots of amplified earthquake shaking.” “There is no doubt that RMS embeds more scientific data into its models than any other commercial risk modeler,” Lee continues. “Throughout this development process, for example, we met regularly with USGS developers, having active discussions about the scientific decisions being made. In fact, our model development lead is on the agency’s National Seismic Hazard and Risk Assessment Steering Committee, while two members of our team are authors associated with the NGA-West 2 ground motion prediction equations.” The North America Earthquake Models in Numbers 360,000 Number of fault sources included in the UCERF3, the USGS California seismic source model >3,800 Number of unique U.S. vulnerability functions in RMS’ 2017 North America Earthquake Models for building shake coverage, with the ability to further differentiate risk based on 21 secondary building characteristics >30 Size of team at RMS that worked on updating the latest model Distilling the Data While data is the foundation of all models, the challenge is to distil it down to its most business-critical form to give it value to clients. “We are dealing with data sets spanning millions of events,” explains Lee, “for example, UCERF3 — the USGS California seismic source model — alone incorporates more than 360,000 fault sources. So, you have to condense that immense amount of data in such a way that it remains robust but our clients can run it within ‘business hours’.” Since the release of the USGS data in 2014, RMS has had over 30 scientists and engineers working on how to take data generated by a super computer once every five to six years and apply it to a model that enables clients to use it dynamically to support their risk assessment in a systematic way. “You need to grasp the complexities within the USGS model and how the data has evolved,” says Mohsen Rahnama, chief risk modeling officer and general manager of the RMS models and data business. “In the previous California seismic source model, for example, the USGS used 480 logic tree branches, while this time they use 1,440 logic trees. You can’t simply implement the data – you have to understand it. How do these faults interact? How does it impact ground motion attenuation? How can I model the risk systematically?” As part of this process, RMS maintained regular contact with USGS, keeping them informed of how they were implementing the data and what distillation had taken place to help validate their approach. Building Confidence Demonstrating its commitment to transparency, RMS also provides clients with access to its scientists and engineers to help them drill down in the changes into the model. Further, it is publishing comprehensive documentation on the methodologies and validation processes that underpin the new version. Expanding the Functionality Upgraded soil amplification methodology that empowers (re)insurers to enter a new era of high-resolution geotechnical hazard modeling, including the development of a Vs30 (average shear wave velocity in the top 30 meters at site) data layer spanning North America Advanced ground motion models leveraging thousands of historical earthquake recordings to accurately predict the attenuation of shaking from source to site New functionality enabling high and low representations of vulnerability and ground motion 3,800+ unique U.S. vulnerability functions for building shake coverage. Ability to further differentiate risk based on 21 secondary building characteristics Latest modeling for very tall buildings (>40 stories) enables more accurate underwriting of high-value assets New probabilistic liquefaction model leveraging data from the 2010-2011 Canterbury Earthquake Sequence in New Zealand Ability to evaluate secondary perils: tsunami, fire following earthquake and earthquake sprinkler leakage New risk calculation functionality based on an event set includes induced seismicity Updated basin model for Seattle, Mississippi Embayment, Mexico City and Los Angeles. Added a new basin model for Vancouver Latest historical earthquake catalog from the Geological Survey of Canada integrated, plus latest research data on the Mexico Subduction Zone Seismic source data from the U.S. Geological Survey (USGS) 2014 National Seismic Hazard Mapping Project incorporated, which includes the third Uniform California Earthquake Rupture Forecast (UCERF3) Updated Alaska and Hawaii hazard model, which was not updated by USGS
The Analytics-Driven Organization
Over the past 15 years, revolutionary technological advances and an explosion of new digital data sources have expanded and reinvented the core disciplines of insurers. Today’s advanced analytics for insurance push far beyond the boundaries of traditional actuarial science. The opportunity for the industry to gain transformational agility in analytics is within reach. EXPOSURE examines what can be learnt from other sectors to create more analytics-driven organizations and avoid ‘DRIP’. Many (re)insurers seeking a competitive edge look to big data and analytics (BD&A) to help address a myriad of challenges such as the soft market, increasing regulatory pressures, and ongoing premium pressures. And yet amidst the buzz of BD&A, we see a lack of big data strategy specifically for evolving pricing, underwriting and risk selection, areas which provide huge potential gains for firms. IMAGINE THIS LEVEL OF ANALYTICAL CAPABILITY PROVIDED IN REAL-TIME AT THE POINT OF UNDERWRITING; A UTOPIA MANY IN THE INDUSTRY ARE SEEKING While there are many revolutionary technological advances to capture and store big data, organizations are suffering from ‘DRIP’– they are data rich but information poor. This is due to the focus being on data capture, management, and structures, at the expense of creating usable insights that can be fed to the people at the point of impact – delivering the right information to the right person at the right time Other highly regulated industries have found ways to start addressing this, providing us with sound lessons on how to introduce more agility into our own industry using repeatable, scalable analytics. Learning From Other Industries When you look across organizations or industries that have got the BD&A recipe correct, three clear criteria are evident, giving good guidance for insurance executives building their own analytics-driven organizations: Delivering Analytics to the Point of Impact In the healthcare industry, the concept of the back-office analyst is not that common. The analyst is a frontline worker – the doctor, the nurse practitioner, the social worker, so solutions for healthcare are designed accordingly. Let’s look within our own industry at the complex role of the portfolio manager. This person is responsible for large, diverse sets of portfolios of risk that span multiple regions, perils and lines of business. And the role relies heavily on having visibility across their entire book of business. A WILLIS TOWERS WATSON SURVEY REVEALS THAT LESS THAN 45 PER CENT OF U.S. PROPERTY AND CASUALTY INSURANCE EXECUTIVES ARE USING BIG DATA FOR EVOLVING PRICING, UNDERWRITING AND RISK SELECTION. THIS NUMBER IS EXPECTED TO JUMP TO 80 PERCENT IN TWO YEARS’ TIME Success comes from insights that give them a clear line of sight into the threats and opportunities of their portfolios – without having to rely on a team of technical analysts to get the information. They not only need the metrics and analytics at their disposal to make informed decisions, they also need to be able to interrogate and dive into the data, understand its underlying composition, and run scenarios so they can choose what is the right investment choice. If for every analysis, they needed a back-office analyst or IT supporter to get a data dump and then spend time configuring it for use, their business agility would be compromised. To truly become an analytics-driven organization, firms need to ensure the analytics solutions they implement provide the actual decision-maker with all the necessary insights to make informed decisions in a timely manner. Ensuring Usability Usability is not just about the user interface. Big data can be paralyzing. Having access to actionable insights in a format that provides context and underlying assumptions is important. Often, not only does the frontline worker need to manage multiple analytics solutions to get at insights, but even the user persona for these systems is not well defined. At this stage, the analytics must be highly workflow-driven with due consideration given to the veracity of the data to reduce uncertainty. Consider the analytics tools used by doctors when diagnosing a patient’s condition. They input standard information – age, sex, weight, height, ethnicity, address – and the patient’s symptoms, and are provided not with a defined prognosis but a set of potential diagnoses accompanied by a probability score and the sources. Imagine this level of analytical capability provided in real-time at the point of underwriting; a Utopia many in the industry are seeking that has only truly been achieved by a few of the leading insurers. In this scenario, underwriters would receive a submission and understand exactly the composition of business they were taking on. They could quickly understand the hazards that could affect their exposures, the impact of taking on the business on their capacity – regardless of whether it was a probabilistically–modeled property portfolio, or a marine book that was monitored in a deterministic way. They could also view multiple submissions and compare them, not only based on how much premium could be bought in by each, but also on how taking on a piece of business could diversify the group-level portfolio. The underwriter not only has access to the right set of analytics, they also have a clear understanding of other options and underlying assumptions. Integration Into the Common Workflow To achieve data nirvana, BD&A output needs to integrate naturally into daily business-as-usual operations. When analytics are embedded directly into the daily workflow, there is a far higher success rate of it being put to effective use. A good illustration is customer service technology. Historically, customer service agents had to access multiple systems to get information about a caller. Now all their systems are directly integrated into the customer service software – whether it is a customer rating and guidance on how best to handle the customer, or a ranking of latest offers they might have a strong affinity for. SKILLED UNDERWRITERS WANT ACCESS TO ANALYTICS THAT ALLOW THEM TO DERIVE INSIGHTS TO BE PART OF THE DAILY WORKFLOW FOR EVERY RISK THEY WRITE It is the same principle in insurance. It is important to ensure that whatever system your underwriter, portfolio manager, or risk analyst is using, is built and designed with an open architecture. This means it is designed to easily accept inputs from your legacy systems or your specific intellectual property-intensive processes. Underwriting is an art. And while there are many risks and lines of business that can be automated, in specialty insurance there is a still a need for human-led decision-making. Specialty underwriters combine the deep knowledge of the risks they write, historical loss data, and their own underwriting experience. Having good access to analytics is key to them, and they need it at their fingertips – with little reliance on technical analysts. Skilled underwriters want access to analytics that allow them to derive insights to be part of the daily workflow for every risk they write. Waiting for quarterly board reports to be produced, which tell them how much capacity they have left, or having to wait for another group to run the reports they need, means it is not a business-as-usual process. How will insurers use big data? Survey of property and casualty insurance executives (Source: Willis Towers Watson)
