Under Pressure: The Pressure of Working in Underwriting with Incomplete, Old or just Incorrect Information.
Welcome to our third and final SimSight on data use in the insurance sector. This week our attention is on underwriting and how working with low quality data can feel like playing with a broken jigsaw…
In the high-pressure world of underwriting, where decisions can make or break financial outcomes, the reliance on accurate data is paramount. Yet, what happens when the data available is just unusable?
Have you ever found yourself knee-deep in spreadsheets, trying to make sense of the numbers before you, but instead of clarity you're met with a mess of incomplete, inaccurate or downright nonsensical data? It's like trying to solve a puzzle with half the pieces missing. Maybe you find yourself wondering, if you soldier on with the (admittedly poor quality) data you have, what’s the worst that can happen?
Risky business - what risks really come from making decisions using poor quality data?
At the heart of underwriting lies the need to assess risk accurately. When data quality is compromised, so are the decisions made based on that data. It's not just a matter of being unable to find data within a system; it’s the very real possibility of making an already risky decision based on flawed information. Inaccuracies, inconsistencies and missing information, can all lead to flawed assessments, potentially resulting in significant financial losses.
It’s fundamental to ensure your systems are fit for purpose, with the capability to not only store huge amounts of data, but also retrieve it accurately and efficiently wherever it is filed away. If your tools fall short in this regard, underwriters are left scrambling to make decisions based on incomplete or incorrect information.
Effective evaluating can be hard enough, don’t create a data dilemma.
In order to effectively evaluate your data, once you’ve located it, it must be specific and reliable. When your data’s rubbish, so is your evaluation process. You may start second-guessing assessments. When you are unsure how many inaccuracies may lie hidden within that data, you will become uncertain of your ability to assess risk effectively. This uncertainty develops into hesitation, potentially leading to missed opportunities or costly mistakes.
Don’t crumble under pressure, invest in the right data management solutions.
And when the data isn't up to scratch? Underwriters are always walking a tightrope with data, where the line between risk and reward becomes more and more blurred. Working with inferior data only exacerbates the pressure already facing underwriters, placing greater strain on both individuals and organisations alike. As such, the need for reliable data and effective management systems cannot be overstated.
Innovative technologies, AI assisted systems, and smart enterprise search solutions are on hand to help audit and manage your data, in time minimising risk and taking a little bit of that pressure off your hard-working underwriters. At SimSage we help businesses work more efficiently with their data, increasing productivity, success and reducing financial risk. Using our AI based information management platform, we adopt a holistic approach to your entire data asset estate, offering audit, search, categorisation, indexing and data processing capabilities. As an underwriter, this means that you can rest easy, knowing that your evaluations are based on highly reliable data.