A leading insurer in the residential property sector sought to refine its risk assessment and customer service through advanced data analytics. They faced challenges in accurately categorising property types and resolving data discrepancies.
The insurer needed to improve data accuracy, particularly in distinguishing bungalows from other property types and accurately capturing bedrooms and bathrooms data, to ensure precise risk evaluation and efficient data processing.
Chimnie partnered with the insurer to offer advanced property data processing and matching technologies. A specialised API facilitated accurate property type categorisation and resolved discrepancies in property attributes. This partnership enabled the insurer to refine their risk assessment models and pricing strategies, gaining a competitive edge in the market by enhancing operational efficiency and data precision.
An ambitious startup aimed at disrupting the insurance market with a fully digital, mass-affluent product, focusing on personalised insurance solutions through cutting-edge data analytics.
The startup required detailed property data to enhance underwriting and tailor insurance offerings, needing insights on rebuild values, flood risks, and other critical property characteristics.
Collaborating with Chimnie provided the startup access to unparalleled property data, including lidar and planning application details, enabling them to offer accurate and personalised quotes. This partnership allowed for adjustments in rebuild values and contents insurance, leveraging income and wealth predictions based on detailed census data. Chimnie's flexible data delivery, including API and flat file options, facilitated seamless integration, driving the startup's growth and market impact.
A brokerage specialising in niche high-risk properties sought to revolutionise their approach through data-driven pricing models and underwriting processes.
The broker required more accurate and comprehensive data to enable straight-through processing and accurately price high-risk properties, aiming for digitisation and automation.
By utilising Chimnie's data services, the brokerage gained insights into property risks and values, enabling automation and digitisation of pricing models. This collaboration allowed for improved risk assessment and underwriting efficiency, fostering business scalability in the high-risk property insurance sector.
A prominent mortgage lender sought to improve its risk assessment processes and streamline its underwriting procedures to better serve its clients and maintain a competitive edge in the market.
The lender faced challenges in accurately assessing property values and potential risks associated with mortgage applications. They required more comprehensive and reliable property data to make informed decisions and minimise the risk of defaults.
Chimnie's API solution provided the lender with detailed information on property characteristics, market trends, and potential risks. This partnership enabled the lender to enhance its risk assessment models, streamline its underwriting processes, and make more informed decisions when approving mortgage applications. As a result, the lender experienced improved loan portfolio performance, reduced default rates, and increased customer satisfaction.
A pioneering home renovation company, specialising in sustainable home improvements, aimed to use data analytics to address the challenge of transitioning properties from traditional to more environmentally friendly systems – such as replacing gas heating systems with heat pumps.
To optimise their marketing efforts and service provision, the company needed specific information on property energy performance, existing heating systems, and energy efficiency ratings. Without this data, their marketing strategies lacked direction and precision, leading to ineffective targeting.
Chimnie, with its exhaustive property data, came on board to provide the necessary insights to the company. With access to detailed Energy Performance Certificate (EPC) information, heating system types, and other property-specific data, the company was able to offer their services to properties most likely to benefit from green upgrades. This targeted approach led not only to increased business but also to a broader adoption of sustainable energy solutions in the residential sector, culminating in a win for both the company and the environment.
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