Alexander Goncharuk, Managing Director, UK, and Global Head of BFSI at Intellias
Data is one of the industry’s most powerful and transformative assets – and claims analytics sits right at the heart of it.
Predictive models and automation are accelerating claims resolution, detecting fraud in real time, and ensuring resources are allocated for maximum impact. And it’s forward-thinking companies who apply advanced data science to the claims process that stand to gain the most – benefiting from greater operational efficiency, reduced costs, and improved customer satisfaction.
Beyond operational gains, claims data analytics is also reshaping the strategic foundations of insurance, because it enables more accurate risk assessment, personalised pricing, and proactive customer engagement. With powerful analytics tools, insurers can uncover the types of insights that help to prevent future claims and refine product design.
The ability to hardness and interpret claims data is not an option for serious players in the industry. It’s a complete necessity in a market where customer expectations and risks are constantly evolving
Six key applications of claims analytics in insurance
There are six primary applications today’s leading insurers are using to make smarter, faster, and more informed decisions:
1. Subrogation: Traditionally a slow and resource-heavy process, insurers are accelerating the process by using analytics to spot high-recovery potential early. Examining claim patterns and identifying third-party liabilities means insurers can prioritise cases, estimate recovery values, and negotiate more effectively. All of which cuts costs while keeping premiums competitive.
2. Claims settlement: In claims settlement, analytics are helping insurers to strike the right balance between speed and accuracy. Take this – machine learning models can estimate payouts, flag inconsistencies, and automate verification, which means insurers can handle large claim volumes – such as those that flood in after natural disasters – without compromising fairness or control.
3. Fraud detection: The same data-driven precision powers fraud detection – one of the biggest and most complex challenges of the industry. Predictive modelling, anomaly detection, and natural language processing now expose suspicious claims faster and more accurately than ever before, helping to protect both insurers, and honest customers, from financial loss.
4. Litigation prevention: Analytics plays a quiet, but mighty role in litigation prevention. Having the ability to analyse historical disputes and claims characteristics, means insurers can identify potential flashpoints – such as claims with high payouts or ambiguous evidence – and resolve issues before they escalate into legal action. The result? Fewer court cases, lower costs, and a stronger trust bond between customer and insurer.
5. Benchmarking: Benchmarking is one of the most powerful tools for a business. It helps insurers to see exactly how they perform and where they stand against industry averages. By comparing data on key mechanisms – like processing times, settlement amounts, and loss ratios – insurers can pinpoint inefficiencies, refine their operations, and set more ambitious performance goals.
6. Loss reversing: Analytics delivers a level of accuracy that manual forecasting could never achieve. By continuously updating reserve estimates with fresh data, insurers are able to better predict the long-term cost of claims, allocate capital more effectively, and maintain financial stability – even in the midst of uncertainty.
Data is a strategic asset for insurers
It’s clear that claims data has become a strategic asset for insurers. But beyond improving operations, claims analytics can be rocket-fuel for business growth.
Data gives insurers the ability to speed up underwriting through predictive modelling, allowing premiums to reflect true risk exposure. Plus, it helps insurers to respond proactively to costly “jumper” claims – those that escalate in value around the 90-day mark.
Claims data analytics also provides the information required to create more personalised and affordable policies for customers. It’s no secret that a tailored and more personalised approach leads to happier customers, and, therefore, improved retention. In fact, a McKinsey report revealed that satisfied customers are more than 80% more likely to renew their insurance policies.
Combine this with stronger compliance and better lead generation insights, and you’ve got a recipe for a more agile, customer-centric business – one that has the potential to outperform even your closest rivals.
Alexander Goncharuk, Managing Director, UK, and Global Head of BFSI at Intellias
Alexander Goncharuk is a financial services technology leader and Managing Director, UK, and Global Head of BFSI at Intellias, where he leads large-scale implementation, integration, and transformation programmes for banks, insurers, and fintechs. Previously, he held Front Office and Market Risk Technology roles at JPMorgan Chase, Goldman Sachs, Standard Bank, and Trafigura in the UK and US, specialising in regulatory frameworks. Known for using technology to solve business problems, he partners closely with executives and engineering teams to deliver measurable results.
