Point of Quote and Underwriting Optimisation
Help ensure that the correct risk assessment is undertaken at a very early stage of the insurance lifecycle, safeguarding both the customer and insurance provider. To hold onto market share and achieve their growth goals, many process millions of quotes every day via aggregators and their own websites. The cloak of anonymity provided by the internet and this high volume business model have been a gift to fraudsters.
Data enrichment is key.
Data enrichment is a powerful risk selection and fraud detection tool which when used effectively can shave operating ratios and allow insurers to price keenly, gaining competitive advantage over their peers. Conversely, those insurers that don’t embrace data enrichment are left with the crumbs from the table – potentially poor risks which they may unwittingly be priced too low and result in costly claims leaking profit.
It comes as no surprise therefore that as insurers’ appetite for data grows, the supply chain of technology companies and third party data providers servicing their needs has exploded, with new data sources and new entrants to the market witnessed on a regular basis.
A centralised and automated system.
To outperform the competition, insurers need the technical ability to process point of quote and sale data and the vision to prioritise the data they wish to mine and how it should be used. There are a number of potential pitfalls to navigate in adopting data enrichment as part of any underwriting and risk mitigation strategy.
Thanks to CRIF's services, insurance companies can access a wealth of data sources via a centralised and automated decision hub. Data are aggregated and analytic models applied to deliver a risk rating or response to assist in pricing and underwriting processes. CRIF provides bespoke solutions which respond to individual insurer needs at point of quote and sale, with a suite of data products that can be tailored accordingly.
CRIF analyses how an insurer uses data in the underwriting process and applies sophisticated predictive indicators in order to verify the applicant’s identity, confirm prior claims history and validate the information they provide about themselves and their vehicle or property. Incorporating machine learning, these predictive indicators can evolve and adapt to reflect the behaviours of fraudsters and dynamically block them.
CRIF solutions generate responses which help the underwriter make an informed decision with full control.
SPOTLIGHT | SME Risk Management & Assessment
How can insurers and brokers leverage the latest risk evaluation intelligence to quickly assess SME risks and price cover during, but especially after, the pandemic?
Restarting, stabilising, and adapting to a new supply-demand landscape is likely to be a top priority for most insurance providers. This will bring real challenges for many, as they try to adapt to new workflows, staffing capacity, credit flows, remote working and regulatory obligations, while at the same time attempting to balance the books.
When it comes to managing SME risks, pre-crisis data won’t be sufficient for evaluating your clients. However, it’s possible to gather accurate information at point of sale and renewal in order to optimise your business intelligence, and appropriately assess and price risks.