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Data Integrity For Prescient Claims Decisions

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Data Integrity For Prescient Claims Decisions

März 10, 2021

“Just give me all your data and I’ll fix your problems.”  If I had a nickel for every time someone uttered these very words throughout my professional tenure in Insurance, I wouldn’t need a career in Insurance.  Perhaps you recall my warnings about snake oil in previous posts, but it bears repeating here. Insurers manage significant amounts of complex data every day. But what happens when these data streams come in at a rapid pace, often outdated, often broken, and often from a wide spectrum of incomplete and disjointed software platforms? Well, it’s simple math.  Garbage in, garbage out.

If someone claims they can take all your data, dirty as it may be, and solve all your operational and workflow challenges, you have two options: Politely dismiss their services (because you are NOT a sucker) or hold tight because you are in the presence of the last unicorn; the holy grail of pure and perfectly structured data streams.

Limited access to data

According to insurance respondents from The Five Dimensions of Enterprise AI survey in May 2020, 51% of respondents said they can access some data to start building a POC while only 11% have access to consistent set of core data to build models.  Furthermore, accessibility to clean and consolidated data for AI, 43% of respondents said that some cleansing and consolidation is done for specific use cases.  Roughly translated?  Garbage in, garbage out and that’s the only predictable outcome you can expect. 

Unless you get serious about data integrity.  There isn’t a quick fix; this is a long-term strategy that requires insurance organizations to make solid investments in data engineering.  Data quality not quantity is imperative for machine learning to make AI-powered decisions and recommendations. As they crave more flexibility in their digital capabilities for improved efficiencies and workflow performance, they also want their clients to get all the innovative skin-in-the-sexy-big-tech-game feels by deploying the correct models/algorithms to deliver a decent ROI. Having a reactionary strategy only pulls your organization further down the rabbit hole and allows nimble, agile disruptors the opportunity to present unique and often turnkey solutions for complex challenges.

Insurance data has always suffered from a bit of ring around the collar but there’s no denying that the soil factor continues and is dragging industry reputations out to dry.  Implementing the right steps to make your data squeaky-clean for downstream workflows can be accomplished by simply understanding that data needs to be accurate, protected and organized.

Throughout my career, I was constantly creating manual workarounds to consolidate, clean, and classify claims data before I could analyze it.  Traditionally, insurers input data manually and the systems allowed inconsistent levels of details due to the lack of controls. It limits the ability to identify specific information to identify trends and patterns, effectively manage business or simply make decisions consistently.  What a waste of time, energy, and resources.

Partnerships for the future

Insurtechs are keen to address data quality.  Gone are the days when it took days to make decisions and recommendations. Imagine the freedom you’ll feel when you can focus on your core business objectives and building relationships with your customers instead of extinguishing data dumpster fires all day! AI and machine learning help with data completeness, enrichment and enables automated workflow decisions by leveraging the underlying core systems to streamline your processes.

COVID-19 forced insurance organizations into the digital deep end without a life vest.  Becoming digitally savvy requires expertise that often exists outside of the organization.  Finding the right partnership gives you the insight and education into all the variables that provide positive business outcomes through the strategic process of digital transformation. Digital transformation isn’t as ominous as rumors might suggest. Recent advances in insurance technology like AI and machine learning make solving data infrastructure and workflow issues a thing of the past.  Why slow performance across the entire insurance eco-system when you simplify the process and accelerate growth?

Start by exploring available options when it comes to data integrity. The Insurtechs keen on digital disruption in the insurance industry need the co-operation and vision of insurance organizations to realize efficiencies and performance around data are in sync with scalable growth.  Aligned interests are the path to a more efficient insurance future.