Financial Services & Insurance
More established financial institutions are facing the threat of remaining relevant in the face of new challengers whilst overcoming the constraints of legacy systems. Increasingly competitive markets, encouraged by comparison sites, coupled with the rise of Fintech necessitate reduced development life cycles and intelligent fraud detection.
More agile product and service development rooted in enhanced business intelligence can deliver the necessary speed to market. At the same time ensuring the right metrics are readily available to meet regulatory and governance requirements is an on-going headache.
Advanced insurance data analytics coupled with a robust data strategy provide the opportunity to better manage the risks through more intelligent fraud detection and management.
Read now: Data - The Key to Transformation for UK Insurers
Data Use Cases in Financial Services & Insurance
Customer Personalisation
Insurance Data Analytics can be used to create personalised offerings and policies around groups of loyal customers who share meaningful demographics, purchases, loyalty etc.
Intelligent Fraud Detection
Big data can be used for intelligent fraud detection and is extremely effective. By using data management and predictive models, variables in every claim can be compared against past claims which were fraudulent. If match is found, the claim can be stalled and further investigation can be carried out.
Reduce Risk
Insurance Data Analytics enables firms to conduct the real-time risk analysis, and therefore be extremely responsive in an increasingly volatile risk environment.
Reduce Customer Churn
Help identify, predict and prevent customers with a high propensity to churn.
Simplyhealth adopts data science for greater customer understanding
The health insurer has begun to use data science to improve customer retention following an Adatis-led implementation of a Microsoft Azure data stack and data science skills development programme.
“What we are able to prove with the data is that those customers that engage with the product are much stickier, and that is what our marketing department needed, so they can take proactive action.”
Andrew Bradley
Head of Data and Analytics at Simplyhealth
Read the Case Study
Insurance Whitepaper
Data: The Key to Transformation for UK Insurers
How UK insurers can use data to modernise, innovate and thrive. This paper looks at the challenges and opportunities facing UK-based insurance companies across the sector, from property and casualty (P&C) to life, commercial and health. It explores how insurers can make best use of data to modernise their processes, develop new propositions, and deliver the products and services that today’s customers want and need.
Read our Insurance Whitepaper
Seizing the Data Opportunity in Financial Services
How a modern data strategy powered by data science and AI, can help you
accelerate digitalisation and unlock powerful use-cases to retain your
competitive advantage.
Data is your biggest asset, and how you use it to promote change and drive growth is key to staying competitive. It doesn’t matter if you’re an insurance startup, an emerging fintech, or a high-street bank – how you use data will be the difference between success and failure.
In this paper, we’ll explore the data challenges facing organisations like yours today and explain how you can unlock powerful use cases to shift the tide in your favour and accelerate digital transformation.
Download the White Paper
INSURANCE BROKER CASE STUDY
Insurance broker gains powerful sales and marketing insights with Power BI-based self service reporting from Adatis
A third-party insurance broker couldn’t get the granular insight it needed to target sales and marketing campaigns effectively. They needed detailed geospatial and demographic insights to know where to target campaigns. Adatis showed that Power BI was capable of delivering the required insights.
The company can now get access to powerful, self-service data visualisation and reporting.
Read the full case study
City of London Private Bank creates new Loan to Value Production system with Help of Adatis
This private bank were seeking a new accurate and efficient way to flexibly calculate their key loan to value metrics.
Adatis needed to implement a mathematical model within their ETL process to help predict the risk associated with their lending activities. With the risk of loans falling into negative equity being a key concern for lenders, the ability to automate part of the risk assessment calculation allows those involved to identify risky loans faster, and plan to mitigate the issues presented quickly.
Read the full case study
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