Case Study

Claim Propensity

Claim Propensity

Analytical Framework

A large general insurance company operating at scale, managing high volumes of auto insurance claims with a focus on efficiency, accuracy, and loss control.

Type

Industry

Insurance

Categories

AI GovernanceCollections & Recovery

Industry

Model Risk Management

The Challenge

The auto insurance industry faces ongoing challenges in managing both the volume and severity of claims.

While insurers aim to improve customer experience and reduce claim settlement time, the claims journey often remains: – Heavily manual – Dependent on individual experience of claims handlers – Disconnected across reporting, investigation, and settlement stages In addition, insurers must balance faster settlements with sufficient investigation time, while also improving loss ratios and reducing claim costs through more precise and efficient assessments.

The Solution

iTuring.ai enabled insurers to proactively manage claims using claim propensity models powered by machine learning.

With iTuring, insurers were able to: – Predict which policyholders are likely to file a claim – Estimate claim amounts with confidence ranges – Identify drivers and causes of claims at an individual level For a leading general insurance company in North America, iTuring built an Auto Insurance Claim Propensity Model to reduce turnaround time from assessment to settlement. The solution helped: – Claims review agents identify potentially risky claimants – Explain why a policyholder may be likely to claim – Support more accurate treatment and approval decisions To further improve efficiency, iTuring developed policyholder-level risk causation metrics and contact strategies, enabling more informed and faster claim processing.

IMPACT

Claim Settlement Efficiency

32% ↑

Model Accuracy

85%

Time to Deployment

2 weeks

WHY ITURING.AI

iTuring goes beyond basic claim prediction.

The platform:
Predicts who is likely to claim and how much they are likely to claim
Provides confidence levels to support faster, safer decisions
Improves decision accuracy for claim approvals
Reduces reliance on manual judgment alone

FAQs

01

What claims-related problem was the insurer facing?

+

The insurer struggled with manual, experience-driven claim handling that increased settlement time and costs.

02

How did iTuring improve claim management?

+

iTuring predicted claim likelihood, expected claim amount, and claim drivers, enabling early and informed intervention.

03

How were claims teams able to act on these insights?

+

Claims agents used risk scores and causation metrics to prioritize reviews and approve appropriate claim amounts faster.

04

What measurable improvements were achieved?

+

The insurer improved claim assessment and settlement efficiency by 32% with an 85% accurate prediction model.

05

How did this solution impact loss control?

+

By improving risk assessment and investigation precision, the insurer reduced unnecessary payouts and improved loss ratios.

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Tarika Bhutani

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Tarika is a market development leader driving global growth through strategic partnerships and go-to-market initiatives.

 

She focuses on expanding enterprise adoption of AI solutions across international markets, working closely with partners and clients to enable data-driven transformation.

 

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Vipin Johnson

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Rajnish brings over two decades of experience leading data-driven transformation across Fortune 500 organisations.

 

His career spans senior roles at HSBC, Zafin, Cisco, TCS, Nielsen, iQuanti, Symphony, Supervalu, and Harman, delivering measurable cost savings, operational efficiencies, and revenue growth.

 

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Prior to iTuring.ai, he held executive leadership roles at Instant Life, AIG, Nedbank, FNB, and TransUnion. He focuses on enabling enterprises to adopt AI and machine learning within trusted, governed, and risk-managed frameworks.

 

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Mohammed Nawas M P

Co-Founder, VP Product Development

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At iTuring.ai, he oversees product roadmap and customer delivery, applying cloud-first thinking, deep systems expertise, and a focus on building robust, scalable AI solutions that challenge industry norms.

 

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Amit Kumar
Co-Founder, VP Product Engineering

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Prior to iTuring.ai, he held senior technical roles at Radisys and Aricent. Amit leads platform architecture with a focus on governance, lineage, and traceability.

 

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Before founding iTuring.ai in 2018, Suman led analytics at Zafin and Fiserv as CAO and General Manager Analytics, delivering enterprise-scale solutions still running in production.

 

His work includes fraud detection systems saving clients over $19M, patented Customer Relationship Score methodology, and price optimisation recognised by the INFORMS Edelman Award (2014). He has authored multiple research papers and pioneered the data-to-value approach.

 

Suman holds a Master’s in Statistics from CCS HAU and a Bachelor’s in Agricultural Engineering from BHU.