MECBot for Insurance Companies: What It Means for Their Bottom-line, Customers, and Team

The Insurance Data Value Chain is Bigger, Deeper, and More Complex Than Ever.


The insurance data value chain today is larger and more complex than ever. 


The data collected by insurers may be personal data as well as non-personal data. Further, the data may be structured or unstructured. Finally, it may originate from internal sources (e.g. an agent collecting the data directly from the customer) or from external sources (e.g. market reports, public databases, or datasets obtained from private data vendors).


Truth be told, insurance has always been a data-heavy industry. Historically, insurers have been sitting on top of a huge pool of traditional datasets such as demographic data, exposure data, and behavioral data. However, only a fraction of this data has been monetized so far. Furthermore, over and above these traditional datasets, new types of data such as Internet of Things (IoT) data, data from online transactions and other internet-based interfaces, digital communications between customers and agents, as well as data generated from various mobile apps are coming into the picture. 


Interestingly, despite having access to such a massive data pool, even deep pocket insurance firms are able to use a mere 15-20% of the traditional data at their disposal to design a product. If unlocking value from their structured datasets was not challenging enough, now they also have to find ways to mine unstructured data for wholesome, accurate, and clear insights in order to foster better decision-making. Failing to do so leads to the creation of dark data - i.e. a vast expanse of untapped and unexplored data that has not been utilized in making insight-augmented decisions.


The Geneva Association, a leading international insurance think tank for strategically important insurance and risk management issues, classifies insurance data into traditional and new datasets, as shown in the images below.


Let us take a quick look at a granular view of insurance data usage, as shown in the figure below. 

traditional datasets

As is evident from above, while traditional datasets like medical history, demographics, exposure data, behavior data, and population data have relatively lower non-usage rates, modern and complex data, which usually tend to be unstructured and poly-structured, has an alarmingly high rate of non-usage. This includes online media data, hazard data, IoT data, genetics data, geocoding data, bank account and credit card data, and so on.


The problem is further compounded by the fact that insurance data originates from a wide range of disparate sources both within and outside the firm. This makes data hard to be compiled and married to one another to create a single, all-encompassing database. 


The following figure shows the big data sources for insurance firms and compares the same to global big data sources.

Big data insurence

Insurers Are Embracing the Use of Big Data Analytics (BDA).

But, it has challenges of its own.


A study by McKinsey estimates that the potential total value of AI and analytics across the insurance vertical is approximately $1.1 trillion. A 2019 survey conducted by Willis Towers Watson found that life insurers who use predictive analytics reported a 67% reduction in expenses and a 60% increase in sales. Another source indicates that big data analytics in insurance leads to 30% better access to insurance services, 40-70% cost savings, and 60% higher fraud detection rates. However, despite these promising developments, the insurance sector continues to be crippled by its inability to make the most of its data.


The figure below shows the various challenges facing insurers today when it comes to mining structured and unstructured enterprise data located both internally and externally.

EIOPA BDA Thematic

In the above figure, the degree or extent to which a certain data challenge impacts an insurance firm’s ability to derive meaning and insights from all its data has been classified into two categories, i.e. “Exceptionally Important” and “Very Important”. Meeting regulatory requirements, ensuring consumer trust, and circumventing data accuracy issues are the top three reasons insurers are struggling to make sense of their data. Other issues and considerations like data ethics and fairness, cyber risks, reputational risks, lack of access to data, scarcity of data science skills, legacy issues, competition, project risks, lack of data infrastructure, data portability issues, and the fragmented nature of the insurance value chain also play a critical role.


On delving deeper into the challenges, a few root causes emerge as the common underlying factors:

Delayed Realization of Economic Value.


For a dynamic industry like insurance, most of the data science problems are like shape-shifting shadows. Customer data is growing and changing constantly, coupled with an ever-evolving regulatory landscape that essentially leads to analytics goals becoming moving targets. Decision-making problems can change, sometimes completely, midway into a data project. The scaling needs may increase dramatically, or the very nature of the data at hand can alter mid-way into the solution designing process. Further, big data analytics technologies and data storage tools are constantly evolving as well, causing an enormous drain on the insurer’s resources as they struggle to keep up with the new tools, skillsets, and technologies.

data analytics solution

The typical insurance data analytics solution takes anywhere from a few months to a year or more to become fully functional. By the time the full economic value of a data science project is realized, the top-brass loses interest, substantial attrition happens in the data time, and the whole initiate ends up being a sunk cost. Over time, a large number of such failed data initiatives lead to the accumulation of huge technical debt for insurers.

Fragmented Data That Exist in Silos.

Insurance data rarely exists on a single integrated platform. The market is flooded with tools that handle diverse kinds of insurance data separately, but there are hardly any data management platforms that automatically ingest, cleanse and unify it all into a smart data fabric. For any business process in the insurance value chain, the data is inevitably spread across multiple applications, often operating in silos. 


For instance, if a customer is being onboarded by an insurer, then the processes and the data originating from the same would lie across two key modules - the Business Process Management (BPM) and the Policy Administration System (PAS) - and several interfaces in between the two.


Depending on the extent of penetration, insurance data may originate anywhere, including remote, rural interiors where internet bandwidth is a concern. Data may remain in isolated, on-premise applications for weeks before reaching the centralized clouds and servers. Further, insurance contracts are enforceable for a period ranging anywhere from 1 year to 15-20 years, leading to a huge volume of legacy data that may not match with the newer data formats.

Lack of Data Infrastructure to Support High-end Insurance Analytics.

Even if the data quality concerns are alleviated to some extent, the sheer lack of scalable, comprehensive, and flexible data infrastructure to support the immense data processing needs of insurance companies is a major stumbling block. Scattered data storage technologies that don’t reflect real-world data relationships, complexities, and the constant inflow of new data are a bane for deriving actionable intelligence from insurance data. Further, when going beyond traditional analytics to advances in AI, the infrastructure often falls short when it comes to ensuring rapid, compliant, and interpretable out-of-the-box data modeling within the existing data environment of insurance firms. This requires data to be moved around and dumped repeatedly leading to loss of data integrity and quality.


What the insurance industry needs at this hour is a single solution that takes care of data storage, data management, data governance, data analytics, data-ops, AI-powered data modeling, and a powerful, scalable infrastructure to support the same at all times without fail.


At FORMCEPT, we have been working consistently towards paving the way for such a one-stop, all-in-one solution built on top of robust and scalable data science technologies through our flagship product - MECBot.


About MECBot.


Powered by innovations in AI, MECBot by FORMCEPT is a leading data excellence platform that enables insight-driven decision-making in real-time and at scale without relying on the underlying databases or the structure of the data. It is the go-to data analytics platform for several leading Fortune 1000 clients across the globe in Banking, Insurance, Retail, Sports, Healthcare, and more. 


MECBot puts the business first by adopting the Entity Domain Model approach. It comes bundled with a self-service, intuitive interface and takes care of the key data management and analytics requirements in a trusted and centralized manner, including scalable deployment. 

MECBot’s ability to transform large volumes of complex, diverse, and disparate data from an unlimited number of external and internal sources into cleaned, unified, flattened, and trusted datasets puts it in a unique position to bring the magic of augmented analytics to the fingertips of business users.

How MECBot Works.

Data teams typically spend about 80% of their time on the collection and preparation of data, leaving just the remaining 20% of their time for generating insights that fuel business decisions. FORMCEPT’s augmented analytics capabilities effectively reverse this ratio. This means that with FORMCEPT’s augmented data analytics solution, data teams get to spend more time coming up with data-driven recommendations for mission-critical decision-making instead of powering through data preparation challenges. 


Whether it is mapping data sources, auto-configuring data pipelines, making data pipelines repeatable and datasets reusable, or enabling data cleansing, wrangling, and transforming at scale - we have your back.


The diagram below summarizes the step-by-step process that MECBot adopts to convert enterprise data into market-winning decisions.


Use Cases of MECBot in Insurance Analytics.

Data-driven Underwriting and Pricing.


The series of complex decision-making that insurance underwriting entails makes it the prime candidate to benefit from MECBot’s insurance analytics capabilities. Underwriters often work with a wide array of parameters at three distinct levels - the company, the customer, and the market. Even if high-quality, up-to-date data is made available to the underwriting team, it is still a herculean task to manually comb through it all. Relying on the judgment and intuition of underwriters also leads to the underwriting and pricing decisions being prone to biases and human errors. 


According to a PwC report, top insurers are taking into serious consideration the idea of using advanced data models and AI-driven tools to aid underwriters in their decision-making process. The figure below shows that 67% of insurers report a reduction in issue and/or underwriting expense, thanks to predictive analytics. 

For an underwriter too, there are clear incentives to combine expertise and experience with insights from AI-powered data models to make informed and accurate decisions in the shortest possible time. MECBot can easily ingest large amounts of data from an ecosystem of private and public data vendors to foster insight-driven decision-making for underwriters. Instead of the underwriting team having to manually map all the data together to get a big picture, MECBot automatically connects the dots by focusing on data relationships and meaning.


The human mind can sometimes miss out on the details, but with MECBot’s auto detection of patterns using FORMCEPT’s patented datafolding algorithm, that risk is eliminated. Instead of relying on the underwriter’s query, MECBot auto-interprets the data and keeps dropping insight nuggets along the way all on its own. Additionally, whenever underwriters need to query the data for specific insights, they can do so in the natural English language using MECBot’s free-flow search feature without any need for coding.


These features have been designed and built into MECBot keeping in mind that saving money, effort, and time on underwriting and arriving at sound pricing decisions will positively impact the insurer’s bottom line and reduce their time-to-serve by leaps and bounds.


Claims Processing, and Fraud Detection cum Prevention.

According to a McKinsey report, “claims for personal lines and small-business insurance will be fully automated, enabling carriers to achieve straight-through-processing rates of more than 90% and dramatically reducing processing times from days to hours or minutes.” However, nearly 1 out of every 4 insurance claims involve a fraudulent claim. Insurance fraud wipes away at least $80 billion annually from American consumers. Non-health insurance fraud costs the industry over $40 billion in losses each year, while healthcare fraud is estimated to cost $60 billion every year. Furthermore, claim abuse occurs in about 10% of property-casualty insurance losses. In 2020, for example, 8,898 cars were intentionally set on fire in the U.S.


By virtue of MECBot’s inherent data governance and banking-grade data security, detection and prevention of insurance frauds have become one of its superpowers. Fraudulent activities may be carried out by both customers and insurance agents, and MECBot’s unparalleled smart data grid and graph-based storage make fraud detection in both cases a cakewalk. 


In fact, with MECBot, fraud detection and prevention are not limited to analyzing transactions alone. Users can easily launch powerful tools like social media listening and auto-recognition of behavioral and communication patterns that are correlated with the intent of committing fraud among insurance agents and customers.

Augmenting Sales, Marketing, Customer Service, and Cross-sell/Up-sell.

Imagine insurance businesses having the power to customize and tailor every product offering, every marketing communication, and every customer interaction - not just to broad market segments, but to intelligent micro-segments or even to individual customers when required. That’s the kind of granular level intelligence that MECBot’s customer 360o brings to the table. MECBot has the unique ability to resolve multiple customer identities by stitching together first-party, second-party, and third-party customer data. This creates a single source of truth for each customer, each micro-segment, and each broader segment.


The implications of this are enormous. 


Insurers can now stay ahead of the curve by anticipating what customers want and proactively preparing for the same well in advance. Beyond transactional and usage data, MECBot unearths intents and behavior patterns through opinion mining, sentiment analysis, and social media listening. MECBot can also digest consumer behavior reports in minutes and come up with a recommendation engine for next-best-steps for each business goal like sales maximization, marketing ROI optimization, reducing cost-to-serve, reducing customer churn, improving customer satisfaction, and grabbing a larger share of the customer’s wallet by seizing cross-sell/up-sell opportunities.

More Reasons to Choose MECBot

In MECBot, users can build, run, and manage customizable ML models in no time without any scale constraints since MECBot automates the deployment of infrastructure resources based on the needs of the models being trained, deployed, or executed. With its inherently distributed systems that automatically streamline ML workloads at each stage of the model lifecycle, resource management can happen in the background without manual supervision - whether on the cloud, on-premise, or in a hybrid setup.

The moment you enter MECBot, it becomes your trusted partner in identifying the right ML model based on the available data, the specific decision-making requirements, and the use cases at hand. MECBot acts as an ML model marketplace with pre-defined templates and modules with a catalog of anticipated outcomes, competing models, and what-if scenarios to help business users quickly identify the best-fit model.


MECBot makes models and their outcomes explainable, observable, and responsible of its own accord. Furthermore, using plugins and APIs, users can easily add more features, enable integrations with external tools, and perform visualization of insights outside MECBot’s native environment.


MECBot also provides Observability (monitoring) support wherein the user can understand how much memory, disk, CPU, etc. are being consumed by the model. In short, MECBot enables Data Scientists, Citizen Data Scientists, Data Analysts, and Domain Experts to run multiple ML models on their data and choose the best one that fits their data, without worrying about the scalability, the infrastructure, or the configuration of various tools.


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