Transforming weeks of risk research into seconds, this AI-powered reporting tool empowers underwriters with in-depth risk insights to make faster, smarter decisions.
Role
Sole UX and UI Designer
INDUSTRY
Insurance
Company
MS Amlin
The tides in any industry can turn in the blink of an eye. With such a diverse range of clients and prospective businesses spanning across multiple industries, it's both essential and near impossible to keep a close, continuous eye on insured businesses.
I held user interviews with a team of underwriting assistants, who are usually tasked with creating a report on the risk associated with prospective clients. We identified the following painpoints:
Proper, in-depth research is time consuming and often is deprioritised in favour of other tasks, meaning that corners get cut and the quality of the research suffers.
Research is liable to becoming outdated incredibly quickly. A single event may have a huge effect on the risk level and public opinion of a company in the space of hours. Ongoing research or monitoring of clients is required.
Users may be biased by their own opinions and preferred news sources, potentially leaving important information out of a risk report.
Public and localised opinion isn't widely reported on, if at all. Social media sites, that are blocked in the users' computer environment, may unlock key information pertaining to the risk.
Larger companies may be more widely reported on for more minor events. It can be difficult to cut through the noise and understand which stories have the most impact on risk and public opinion.
There is no framework set for risk reports and users are compiling their research inconsistently. This makes the reports harder to digest and key decisions may be more difficult to make.
A non-biased, thorough report on a company, using AI to scour a huge variety of data sources including international news, financial reporting and social media. The report should include overview of the company, notable events and sentiment to give users an up to date and thorough understanding of the risks associated with any given company.
We kicked off with a solution design workshop between the product team and data science team to decide how we could build data science models to improve the research process. I collaborated with the data science team throughout their discovery sessions, keeping an agile and modular approach to the design features to ensure that the product could adapt to the abilities of the models.
Features:
Working with custom artificial intelligence models is often a treacherous path that requires a high amount of agility. The reliability of the model's outcomes must be frequently tested, challenged and improved, and the approach to the design must stay iterative to account for these changes. It is common to lose key features where the AI outcomes aren't reliable enough to present to the user. Even when an idea appears to be straight-forward and feasible, things can change fast and the engineers often find more or different opportunities for feature development. All the while, I must challenge these new and changing features to ensure that they are suitable for the users' needs, and then prioritise them and adapt the design to suit.
The process of looking through thousands of reliable data sources, producing a coherent, well designed report has saved weeks worth of work for underwriting assistants at a time, allowing them to focus on their higher priority tasks. Management are also able to easily use the tool themselves, taking away the need to wait and rely on other members of staff.
Users reported that the risk appetite is far easier to identify in this scannable report, and users are able to devise patterns over time as well as public sentiment.
Time saved
Weeks of research are optimised, improved and thinned down into a matter of seconds
Easier, more informed decision making
Decisions can be made quickly and efficiently by scanning and interpreting statistics
Better ongoing risk management
Key events that make a huge impact on risk coverage are identified and flagged automatically
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