On AI for Insurance and Risk Management. Interview with Sastry Durvasula
“AI in complex global industries is in a league of its own, with many opportunities, many risks and many rewards! We definitely see AI having a major impact on the entire risk and insurance industry value chain from improving customer experience to changing core insurance processes to creating next-gen risk products.” –Sastry Durvasula
I have interviewed Sastry Durvasula, Chief Digital Officer and Chief Data & Analytics Officer at Marsh, Inc.
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Q1: You are Marsh’s Chief Digital Officer and Chief Data & Analytics Officer. What are your main priorities?
Sastry Durvasula: My primary focus is leading Marsh’s global digital, data and analytics strategy and transformation, while building new digital-native businesses and growth opportunities. This includes development of next-gen digital platforms and products; data science and modelling; client-facing technology; and digital experiences for clients, carrier partners and colleagues. We also launched Marsh Digital Labs to incubate emerging tech, InsurTech partnerships, and forge industry alliances. Another key aspect of the role is to drive digital culture transformation across the company.
Q2: Can you talk briefly about Marsh Digital Labs?
Sastry Durvasula: We established Marsh Digital Labs as an incubator for developing innovative insurance products, running select tech experiments and supporting strategic engagements with clients, insurance carriers and InsurTechs. The Labs has an innovation funnel process whereby we select and move ideas from concepts to actual market pilots before handing off to the product teams for full-scale development. This allows us to be agile, fail fast and demonstrate product viability, which is critical in today’s fast-changing tech landscape. Our most recent pilot was RiskExchange, a blockchain for trade credit insurance, which was actually the winning idea from our global colleague hackathon called #marshathon.
We’re currently focused on three emerging tech areas – AI/ML, Blockchain and IoT – and exploring a number of new insurance products and distribution channels in the small commercial and consumer sector, as well as in the sharing economy, cyber, autonomous vehicles, and worker safety areas. But ongoing R&D is a core component of the Labs, too, and we collaborate with a number of industry, academia and open-source initiatives. And we need to cut through all the hype and focus on use cases that create true business impact. For example, the Labs has a dedicated unit right now working on using AI and IoT to develop next-gen risk model capabilities that leverage new streams of real-time data, cloud-based platforms, and machine learning algorithms.
Q3: Can you talk a little bit about your overall data infrastructure and the new data streams you are exploring?
Sastry Durvasula: Yes, absolutely. We implemented the Marsh big data ecosystem leveraging multi-cloud platform and capabilities, advanced analytics and visualization tools, and API-based integrations. It has been built to support data in any format, source or velocity with dynamic scalability on processing and storage. Data privacy and governance are safeguarded with metadata and controls built-in.
Keep in mind that traditional risk management and insurance placement is mostly done using static exposure data that gets updated typically only during policy renewal. We are actively working on changing the game by bringing in a wide variety of newer data streams, including IoT data and other external sources, in order to quantify and manage risks better.
For example, in the marine and shipping industry this includes behavioral data such as vessel statistics, movements, machinery and weather information, combined with historical claims data. We can get a more accurate picture of risk and can price more accurately. To assist with these metrics, we recently launched a partnership with InsurTech firm Concirrus that specializes in marine analytics. Similarly, in property risk we are looking at factors such as building integrity as measured by vibrations or earthquake potential, damage from water leakage as measured by sensors or actuators, and so on. In telematics, we can use real-time GPS and speed data, as well as driving behavioral data like braking, acceleration and so on.
We are also researching the overall risk profile of smaller enterprise clients by leveraging third-party external sources such as news, social, government and other regulatory or compliance filings. So, there is a wide variety of data and data types that we deal with or are actively exploring.
Q4: What is exciting about AI in the insurance and risk management space?
Sastry Durvasula: AI in complex global industries is in a league of its own, with many opportunities, many risks and many rewards. We definitely see AI having a major impact on the entire risk and insurance industry value chain from improving customer experience to changing core insurance processes to creating next-gen risk products.
Underwriting based on AI models working on dynamic data streams will result in usage-based and on-demand insurance offerings. We will also see systems that allow straight-through quoting, placement and binding of selected risks powered by AI. For insurance brokers and carriers, this will allow more intelligent risk selection methods.
Claims is another area where AI will have a major impact including automated claims management, claims fraud detection, and intelligent automation of the overall process.
Accelerating use of AI in many industries will have an impact on risk liability models. For example, as AI-powered autonomous vehicles become mainstream, liability shifts from personal auto coverage to a commercial product liability held by the manufacturer. So the insurance industry a decade from now may look quite different from today.
We have also been working on conversational AI and chatbots to support various client-facing and colleague-facing initiatives. AI will play a big role in intelligent automation – insurance is an industry with vast numbers of documents and is very manual and process-oriented. By providing AI-powered human-augmentation functions that improve and enhance the manual processes, we will see efficiencies in the overall industry.
Q5: What are some of the emerging risk and insurance products that Marsh is working on?
Sastry Durvasula: We have several new products targeting either different risks or different market segments. We recently launched Blue[i] next-gen analytics and AI suite, powered by Marsh’s big data ecosystem. Many of Marsh’s big enterprise customers retain significant risk in their portfolio. In fact, in many cases, the premium paid for risk transfer to the insurance markets is only a certain percentage of the Total Cost of Risk (TCOR) to that company. Worker’s compensation is one of the biggest risks in the US, costing employers nearly $100B annually. Our Blue[i] ML models powered by behavioral data and real-time insights help with the prediction of and reduction in claims, as well as reduction in insurance premiums.
Cyber Risk is definitely one of the fastest growing risk categories in the world. We launched market-leading solutions to understand, quantify and manage an enterprise’s cyber risk. These include several proprietary ways of quantifying cyber exposure, cyber business interruption and data breach impacts. These techniques will get more sophisticated as we build out our AI capabilities and increase our data sources.
Pandemic risk is another emerging risk category that we are building out solutions for. In partnership with a Silicon Valley based startup called Metabiota and re-insurer MunichRe, we have created an integrated pandemic risk quantification and insurance solution targeted at key industries in the travel, aviation, hospitality and educational sectors.
In addition to emerging risk products, we have also been innovating on digital solutions in the small commercial and consumer space. We launched Bluestream, a cloud-based digital broker platform for affinity clients, providing them with a new, streamlined way to offer insurance products and services to their customers, contractors, and employees.
Q6: Can you elaborate on how AI and IoT enable real-time risk management?
Sastry Durvasula: AI and new IoT data streams are making real-time risk management a possibility because enterprises have an up-to-the minute view of changing risk exposures and can effectively take actions to mitigate them. It changes how risks are calculated – from traditional actuarial models based on historic events to AI-powered analytics that support dynamic views of risk-triggering mitigating actions.
For example, in the marine use case, cargo insurance policies can be repriced in real-time based on the operator behavior, value of cargo, sea and weather conditions, and many other dynamic variables. In addition to repriced risk, the operator can also be ‘nudged’ to take less riskier actions in exchange for reduced insurance pricing.
We are also actively leveraging wearables to drive reduction in workers compensation claims based on repetitive motion as well as to improve worker safety. By using data from wearables such as smart belts that measure an employee’s sitting, standing, bending, twisting, walking and other repetitive motion actions, dashboards are created to collect and show individual and aggregate movement and locations. Our models recommend ways to improve the client’s safety as well as ergonomic plans to reduce injury and claims likelihood.
Q7: There is a lot of concern around possible malicious use of AI as the technology progresses. Can you talk about some of the risks posed by AI?
Sastry Durvasula: Definitely, this is an important area for us going into the future. AI models are not perfect at all – in fact, far from it. AI models trained on data sets containing unintentional human biases will reflect that same prejudice in their predictions. We are also starting to see more and more cases where opaque AI models resulted in inscrutable errors that were only uncovered after lengthy lawsuits. As more and more complex AI algos and models make their way to the enterprise, it has become very urgent to incorporate accountability and trust criteria into different stages of the model creation. This includes being on the lookout for bias in training data to ‘explainable and interpretable’ models and to have a meaningful appeals process. Definitely, thoughtful regulation needs to be introduced in a way not to impede the technological progress, but to push it in the right direction.
In addition to the above, AI is already causing major headaches by amplifying the ability of bad actors – whether it is automating hacking attempts that make corporate security even harder, or causing broader global harm with fake news and propaganda, or making existing weaponry more destructive. As mentioned earlier, cyber risk is the fastest growing risk category and AI will only add more fuel to the fire.
Not everything around AI is increasing risk, though. Apparently, 90% of auto accidents are caused by human errors. So in this case the rise of AI-powered autonomous vehicles may actually bring down overall driving risk as they become more mainstream!
Q8: How do you see AI governance evolving at the enterprise level?
Sastry Durvasula: AI governance is definitely an area that will get a lot of attention over the next 18-24 months and beyond as more and more AI models are implemented by firms across various industries. Operationalizing AI systems is a complex multi-step process that is also complicated by the fact that AI models can drift in performance, especially if they have feedback loops and are training continuously. In addition, AI models can vary in the degree of autonomy – for example, a low autonomy model that supports human augmentation may require less governance as opposed to completely autonomous systems that necessitate a very high degree of governance.
At the very least we see the following issues being very key for AI governance in enterprise systems: explainability, interpretability, and accountability.
The first refers to explainability standards – understanding why an AI system is behaving in a specific way or even if the AI can be explained. This will be critical to improving the overall trust on the accuracy and appropriateness of the predictions. Also, the interpretability of AI algos and models will be a key feature. Finally, accountability tools, such as the ability to audit a model or ways to contest a prediction, will be needed.
Other important issues are the ability to stop biases from creeping into models as well as incorporating appropriate safety controls into the overall system. Safety can be improved with continuous monitoring to check whether the AI system violated any safety constraints, and automatic failover or human override in the case of any suspected safety breach. The quandary about how to limit biases converges with the dilemma around AI ethics – should ethical AI be approached through self-regulation in the development of AI tech, or by creating ‘moral machines’ where ethics and values are built into the machine. In either case, ethics is generally open to interpretation and is not yet in the legal framework.
In addition, as a risk management company, we are always on the lookout for liability issues for our enterprise clients. As the client implements AI, it has to be noted that some person or organization is still ultimately responsible for the actions of the AI systems under their control – no matter how complex or sophisticated the AI model is. On top of it, most enterprise systems will typically rely on AI models that have been developed by a tech company. In many such scenarios, it is not very clear where the liability lies in the case of an incident. For example, if an autonomous vehicle has an accident based on some AI model failure, it is not clear whether the vehicle manufacturer is liable or whether it is the AI software provider or maybe even the AI chip vendor! We are at very early stages of such complex liability frameworks and we may need to have governments stepping in with clear regulatory guidelines in such cases. These are very early days but again we expect to see a flurry of activity in this sector soon.
Q9: How are you attracting top talent in AI, analytics and other emerging tech areas?
Sastry Durvasula: Talent is a big focus area for us. We have been able to attract a number of engineering and product experts, and data science talent, with diverse industry backgrounds. In the US, we hired the head of Labs in Silicon Valley, the head of data science in New York, and built our digital hub in Phoenix. We recently launched global innovation centers in select locations to attract regional talent, and have been forging industry and academia alliances.
It is equally important to keep the team energized and provide cross-functional development opportunities. There are some very interesting and complex data, analytics and digital problems in the risk and insurance space as I discussed earlier. We focus on shedding light on them, building an agile culture, and fostering experimentation.
As an example, we launched a global colleague hackathon called #marshathon that had amazing response and participation. The winning teams get to partner with our Labs to incubate the idea and launch in-market pilots. We also launched the first-ever all-women hackathon in the industry called #ReWRITE, for Women, Risk, Insurance, Tech and Empowerment, in the US and Europe working with Girls in Tech and other industry partners. It was a great opportunity for women technologists from universities, startups and other corporations to network, learn and hack some innovative ideas utilizing AI, IoT, blockchain and other digital technologies.
Q10: Have you seen any significant or notable changes in the risk and insurance industry from when you started?
Sastry Durvasula: Where there is risk, there is opportunity. We are seeing increased momentum and significant investments in digital, data and analytics, and InsurTech is gaining speed. Digital has become a Board level topic in the industry. New collaborations and consortia are forming, especially leveraging the power of Blockchain and other emerging technologies.
There are as many opportunities as there are challenges both on the demand side and the supply side of the value chain. The rapidly changing cyber risk landscape, increased surface area with IoT devices, autonomous vehicles, sharing and gig economy, and other Industry 4.0 advancements are bringing new opportunities while adding new complexities in a tightly regulated environment.
Legacy operational systems are the delimiters for the industry to fully capitalize on these opportunities and address the challenges, and companies need to make digital transformation a strategic and relentless priority. As Yoda would say, “Do. Or do not. There is no try.”
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Sastry Durvasula
Chief Digital and Chief Data & Analytics Officer, Marsh
Sastry is CDO and CDAO of Marsh, the world’s leading insurance broker and risk adviser. He leads the company’s digital, data and analytics strategy and transformation, while building new digital-native businesses and growth opportunities. This includes development of innovative digital platforms and products, data science & modelling, client-facing technology, and digital experiences across global business units. In his previous role at American Express, Sastry led global data and digital transformation across the lifecycle of cardmembers and merchants, driving innovation in digital payments and commerce, big data, machine learning, and customer experience.
Sastry plays a leading role in industry consortia, CDO/CIO forums, FinTech/InsurTech partnerships, and building academia/research affiliations. He is a strong advocate for diversity & inclusion and is on the Board of Directors for Girls in Tech, the global non-profit that works to put an end to gender inequality. Sastry launched an industry-wide initiative called #ReWRITE focused on Women, Risk, Insurance, Technology & Empowerment. He holds a Master’s degree in Engineering, is credited with 20+ patents and has been the recipient of several industry awards for innovation and leadership.
Resources
The Ethics of Artificial Intelligence, Frankfurt Big Data Lab.
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