Transforming Industries with MongoDB and AI: Insurance

This is the fifth in a six-part series focusing on critical AI use cases across several industries. The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries.

With its ability to streamline processes, enhance decision-making, and improve customer experiences in far less time, resources, and staff than traditional IT systems, artificial intelligence offers insurers great promise.

In an inherently information-driven industry, insurance companies ingest, analyze, and process massive amounts of data. Whether it’s agents and brokers selling more policies, underwriters adequately pricing, renewing and steering product portfolios, claim handlers adjudicating claims, or service representatives providing assurance and support, data is at the heart of it all.

Given the volumes of data, and the amount of decision-making that needs to occur based on it, insurance companies have a myriad of technologies and IT support staff within their technology investment portfolios. It’s no surprise that AI is at the top of the list when it comes to current or prospective IT investments. With its ability to streamline processes, enhance decision-making, and improve customer experiences with far less time, resources, and staff than traditional IT systems, AI offers insurers great promise.

Underwriting & risk management

Few roles within insurance are as important as that of the underwriters who strike the right balance between profit and risk, bring real-world variables to the actuarial models at the heart of the insurer, and help steer product portfolios, markets, pricing, and coverages. Achieving equilibrium between exposures and premiums means constantly gathering and analyzing information from a myriad of sources to build a risk profile sufficient and detailed enough to make effective policy decisions.

While many well-established insurers have access to a wealth of their own underwriting and claims experience data, integrating newer and real-time sources of information, keeping up with regulatory changes, and modeling out what-if risk scenarios still involve significant manual effort.

Perhaps the single greatest advantage of AI will be its ability to quickly analyze more information with fewer people and resources. The long-term impact will likely be profound, and there is tremendous promise within underwriting.

Advanced analytics: Traditional IT systems are slow to respond to changing formats and requirements surrounding data retrieval. The burden falls on the underwriter to summarize data and turn that into information and insight. Large Language Models are now being leveraged to help speed up the process of wrangling data sources and summarizing the results, helping underwriting teams make quicker decisions from that data.

Workload and triage assistance: AI models are mitigating seasonal demands, market shifts, and even staff availability that impact the workload and productivity of underwriting teams, saving underwriting time for high-value accounts and customers where their expertise is truly needed. Amid high volumes for new and renewal underwriting, traditional AI models can help classify and triage risk, sending very low-risk policies to ‘touchless’ automated workflows, low to moderate risk to trained service center staff, and high-risk and high-value accounts to dedicated underwriters.

Decision-making support: Determining if a quoted rate needs adjustment before binding and issuing can take significant time and manual effort. So can preparing and issuing renewals of existing policies, another large portion of the underwriters’ day-to-day responsibilities. Automated underwriting workflows leveraging AI are being employed to analyze and classify risk with far less manual effort. This frees up significant time and intellectual capital for the underwriter.

Check out our machine learning solutions page to learn more about automated digital underwriting.

Vast amounts of data analyzed by underwriters are kept on the underwriter's desktop rather than IT-managed databases. MongoDB offers an unparalleled ability to store data from a vast amount of sources and formats and deliver the ability to respond quickly to requests to ingest new data. As data and requirements change, the Document Model allows insurers to simply add more data and fields without the costly change cycle associated with databases that rely on single, fixed structures.

For every major business entity found within the underwriting process, such as a broker, policy, account, and claim, there is a wealth of unstructured data sources, waiting to be leveraged by generative AI. MongoDB offers insurers a platform that consolidates complex data from legacy systems, builds new applications, and extends those same data assets to AI-augmented workflows. By eliminating the need for niche databases for these AI-specific workloads, MongoDB reduces technology evaluation and onboarding time, development time, and developer friction.

Claim processing

Efficient claim processing is critical for an insurer. Timely resolution of a claim and good communication and information transparency throughout the process is key to maintaining positive relationships and customer satisfaction. In addition, insurers are on the hook to pay and process claims according to jurisdictional regulations and requirements, which may include penalties for failing to comply with specific timelines and stipulations.

To process a claim accurately, a wealth of information is needed. A typical automobile accident may include not only verbal and written descriptions from claimants and damage appraisers but also unstructured content from police reports, traffic and vehicle dashboard cameras, photos, and even vehicle telemetry data. Aligning the right technology and the right amount of your workforce in either single or multi-claimant scenarios is crucial to meeting the high demands of claim processing.

  • Taming the flood of data: AI is helping insurers accelerate the process of making sense of a trove of data and allowing insurers to do so in real-time. From Natural Language Processing to image classification and vector embedding, all the pieces of the puzzle are now on the board for insurers to make a generation leap forward when it comes to transforming their IT systems and business workflows for faster information processing.

  • Claims experience: Generating accurate impact assessments for catastrophic events in a timely fashion to inform the market of your exposure can now be done with far less time, and with far more accuracy, by cross-referencing real-time and historical claims experience data, thanks to the power of Generative AI and vector-embedding of unstructured data.

  • Claim expediter: Using vector embeddings from photo, text, and voice sources, insurers are now able to decorate inbound claims with richer and more insightful metadata so that they can more quickly classify, triage, and route work. In addition, real-time insight into workload and staff skills and availability is allowing insurers to be even more prescriptive when it comes to work assignments, driving towards higher output and higher customer satisfaction.

  • Litigation assistance: Claims details are not always black and white, parties do not always act in good faith, and insurers expend significant resources in the pursuit of resolving matters. AI is helping insurers drive to resolution faster and even avoid litigation and subrogation altogether, thanks to its ability to help us analyze more data more effectively and more quickly.

  • Risk prevention: Many insurers provide risk-assessment services to customers using drones, sensors, or cameras, to capture and analyze data. This data offers the promise of preventing losses altogether for customers and lowering exposures, liability, and expenses for the insurer. This is possible thanks to a combination of vector-embedding, traditional, and generative AI models.

Learn more about AI-enhanced claim adjustment for automotive insurance on our solutions page.

Customer experience

Accessing information consistently during a customer service interaction, and expecting the representative to quickly interpret it, are perennial challenges with any customer service desk. Add in the volume, variety, and complexity of information within insurance, and it’s easy to understand why many insurers are investing heavily in the transformation of their customer experience call center systems and processes.

  • 24/7 virtual assistance: As with many AI-based chat agents, the advantage is that it can free up your call center staff to work on more complex and high-touch cases. Handling routine inquiries can now include far more complex scenarios than before, thanks to the power of vector-embedded content and Large Language Models.

  • Claims assistance: Generative AI can deliver specific claim-handling guidelines to claim-handling staff in real time, while traditional ML models can interrogate real-time streams of collected information to alert either the customer or the claim-handler to issues with quality, content, or compliance. AI capabilities allow insurers to process more claims more quickly and significantly reduce errors or incomplete information.

  • Customer profiles: Every interaction is an opportunity to learn more about your customers. Technologies such as voice-to-text streaming, vector embedding, and generative AI help insurers build out a more robust ‘social profile’ of their customers in near real-time.

  • Real-time fraud detection: According to estimates from the Coalition Against Insurance Fraud, the U.S. insurance industry lost over $308 billion to fraud in 2022. With vector-embedding of unstructured data sources, semantic and similarity searches across both vector and structured metadata, and traditional machine learning models, insurers can detect and prevent fraud in ways that were simply not ever before possible.

Other notable use cases

  • Predictive Analytics: AI-powered predictive analytics can anticipate customer needs, preferences, and behaviors based on historical data and trends. By leveraging predictive models, insurers can identify at-risk customers, anticipate churn, and proactively engage with customers to prevent issues and enhance satisfaction.

  • Crop Insurance and Precision Farming: AI is being used in agricultural insurance to assess crop health, predict yields, and mitigate risks associated with weather events and crop diseases, which helps insurers offer more accurate and tailored crop insurance products to farmers.

  • Predictive Maintenance for Property Insurance: AI-powered predictive maintenance solutions, leveraging IoT sensors installed in buildings and infrastructure, are used in property insurance to prevent losses and minimize damage to insured properties.

  • Usage-Based Insurance (UBI) for Commercial Fleets: AI-enabled telematics devices installed in commercial vehicles collect data on driving behavior, including speed, acceleration, braking, and location. Machine learning algorithms analyze this data to assess risk and determine insurance premiums for commercial fleets to help promote safer driving practices, reduce accidents, and lower insurance costs for businesses.

Learn more about AI use cases for top industries in our new ebook, How Leading Industries are Transforming with AI and MongoDB Atlas. Read the full ebook here.

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Embracing Neurodiversity During Autism Awareness Month

April is Autism Awareness Month. According to a 2023 study by The Tavistock Institute of Human Relations, nearly half of neurodivergent employees feel impacted by their conditions in the workplace. On top of this, many neurodivergent employees choose to not disclose their conditions due to fear of stigma or reduced career opportunities. It’s safe to say that organizations can do more to create supportive, inclusive, and empowering environments for neurodivergent employees. Config.MDB is an employee resource group aiming to raise awareness for and build support around neurodiversity in the workplace at MongoDB. Sarah Lin, Senior Information & Content Architect, and Config member, speaks about her experience as a parent to a child with autism and shares her perspective on creating inclusive spaces for neurodivergent employees, customers, and members of our communities. According to the CDC, one in every 36 children in the U.S . is on the autism spectrum. My child is one of those. Parenting a child with autism is, in many ways, just parenting. There’s often awe, wonder, frustration, exhaustion, and fountains of love all mixed up in the same day. Parenting is hard, period. As a parent of one neurodiverse and one neurotypical child, I'm able to see a little bit of both worlds and while there are highs and lows for both, they’re usually different. People with autism often have a particular area of interest, and my child has opened up a world to me I was only vaguely familiar with. Learning, enjoying, and being part of their world is a gift. I’m so grateful to experience it with them every day. Parenting children with different abilities can be lonely and isolating, though. I get support where I can, but many folks we encounter are actively unsupportive, assuring me “they’ll grow out of it” or expecting I can make them behave in a certain way. My child needs lower levels of support to navigate the world, which means that people often just dismiss their diagnosis entirely as misbehavior or even say that their challenges don’t exist. Understanding autism spectrum disorder I learned pretty early in my journey that if you’ve met one person with autism, you’ve met one person with autism. Autism Spectrum Disorder is just that, a spectrum, with individuals needing more or less support, so interacting with one person on this spectrum isn’t generalizable to everyone else. I’ve found that each person presents a unique constellation of abilities and challenges, and the best way forward is to ask and get to know each person I meet. Learning about autism and how my child experiences the world has made me a better person, which I hope has carried over into work as well. The most significant impact is raising awareness of sensory needs in the workplace. We all have sensory needs to varying degrees, and understanding that they are genuine makes me more empathetic, accommodating, and patient. Considering the needs of the neurodivergent community If my child grows up wanting to work in the tech industry like I do, I’d advise them to remember their needs and seek a role at a company that seems like the best fit. For example, does working from home help them avoid auditory overstimulation? Awareness of their legal rights is paramount, though; they should ask for the accommodations they need to succeed. I’m sure it’s the parent in me, but the most essential advice is that they take care of themselves. Having time off, structuring their workday and environment to meet their needs, and having a healthy sensory diet are all foundational for doing their best work. To workplaces and colleagues, I encourage you to consider that creating inclusive spaces can be both physical and metaphorical. I’m reminded of a previous employer that set out a specific neurodivergent space at its customer conferences for attendees. I can only speak for what has helped my family, but having alternative options and authority figures willing to compromise when accommodation is outside the norm is impactful. As I teach my child to advocate for their own needs, I rely on the rest of us to meet them with understanding and flexibility. The impact of increasing awareness When talking about impact, there’s no denying that millions of individuals and families benefit from increased awareness, understanding, and inclusivity in our society. Speaking from my own experience as a parent, even if we don’t take advantage of specifically autism-friendly events or spaces, just knowing they are available takes some stress off of daily “how am I going to make this work?” questions. Taking advantage of sensory-friendly activities, for example, allows my child to have the same experiences as everyone else, making them feel included and accepted. For kids, shared experiences are essential for social belonging, and when you’re already experiencing the world differently, that can be a very challenging area to begin with. As I reflect on Autism Awareness Month, I encourage my friends and colleagues to work towards learning about and supporting neurodiversity in their communities. Some of the best ways to do this are simply through asking and researching. Information abounds, and so does the work of learning rather than expecting to be taught. You can also participate in (or start) a community group or employee resource group — like Config at MongoDB , a global employee resource group focused on disability and neurodiversity at MongoDB. The more we strive to educate ourselves and create inclusive environments for everyone, the better our workplaces and communities will become. Learn more about Diversity & Inclusion at MongoDB.

April 10, 2024
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Building AI with MongoDB: Conversation Intelligence with Observe.AI

What's really happening in your business? The answer to that question lies in the millions of interactions between your customers and your brand. If you could listen in on every one of them, you'd know exactly what was up--and down. You’d also be able to continuously improve customer service by coaching agents when needed. However, the reality is that most companies have visibility in only 2% of their customer interactions. Observe.AI is here to change that. The company is focused on being the fastest way to boost contact center performance with live conversation intelligence. Founded in 2017 and headquartered in California, Observe.AI has raised over $200m in funding. Its team of 250+ members serves more than 300 organizations across various industries. Leading companies like Accolade, Pearson, Public Storage, and 2U partner with Observe.AI to accelerate outcomes from the frontline to the rest of the business. The company has pioneered a 40 billion-parameter contact center large language model (LLM) and one of the industry’s most accurate Generative AI engines. Through these innovations, Observe.AI provides analysis and coaching to maximize the performance of its customers’ front-line support and sales teams. We sat down with Jithendra Vepa, Ph.D, Chief Scientist & India General Manager at Observe.AI to learn more about the AI stack powering the industry-first contact center LLM. Can you start by describing the AI/ML techniques, algorithms, or models you are using? “Our products employ a versatile range of AI and ML techniques, covering various domains. Within natural language processing (NLP), we rely on advanced algorithms and models such as transformers, including the likes of transformer-based in-house LLMs, for text classification, intent and entity recognition tasks, summarization, question-answering, and more. We embrace supervised, semi-supervised, and self-supervised learning approaches to enhance our models' accuracy and adaptability." "Additionally, our application extends its reach into speech processing, where we leverage state-of-the-art methods for tasks like automatic speech recognition and sentiment analysis. To ensure our language capabilities remain at the forefront, we integrate the latest Large Language Models (LLMs), ensuring that our application benefits from cutting-edge natural language understanding and generation capabilities. Our models are trained using contact center data to make them domain-specific and more accurate than generic models out there.” Can you share more on how you train and tune your models? “In the realm of model development and training, we leverage prominent frameworks like TensorFlow and PyTorch. These frameworks empower us to craft, fine-tune, and train intricate models, enabling us to continually improve their accuracy and efficiency." "In our natural language processing (NLP) tasks, prompt engineering and meticulous fine-tuning hold pivotal roles. We utilize advanced techniques like transfer learning and gradient-based optimization to craft specialized NLP models tailored to the nuances of our tasks." How do you operationalize and monitor these models? "To streamline our machine learning operations (MLOps) and ensure seamless scalability, we have incorporated essential tools such as Docker and Kubernetes. These facilitate efficient containerization and orchestration, enabling us to deploy, manage, and scale our models with ease, regardless of the complexity of our workloads." "To maintain a vigilant eye on the performance of our models in real-time, we have implemented robust monitoring and logging to continuously collect and analyze data on model performance, enabling us to detect anomalies, address issues promptly, and make data-driven decisions to enhance our application's overall efficiency and reliability.” The role of MongoDB in Observe.AI technology stack The MongoDB developer data platform gives the company’s developers and data scientists a unified solution to build smarter AI applications. Describing how they use MongoDB, Jithendra says “OBSERVE.AI processes and runs models on millions of support touchpoints daily to generate insights for our customers. Most of this rich, unstructured data is stored in MongoDB. We chose to build on MongoDB because it enables us to quickly innovate, scale to handle large and unpredictable workloads, and meet the security requirements of our largest enterprise customers.” Getting started Thanks so much to Jithendra for sharing details on the technology stack powering Observe.AI’s conversation intelligence and MongoDB’s role. To learn more about how MongoDB can help you build AI-enriched applications, take a look at the MongoDB for Artificial Intelligence page. Here, you will find tutorials, documentation, and whitepapers that will accelerate your journey to intelligent apps.

April 29, 2024