WNS SKENSE - A Data Contextualization Engine Powered by AI

SKENSE Stories of Transformation

Client

Leading Insurer

Solution

Leveraging SKENSE by implementing an award-winning multi-agent system on Azure to transform the underwriting research process.

Business Challenge

The client's team was spending hours on manual, ad-hoc underwriting research using scattered data sources, resulting in inefficiencies, limited risk coverage, and bias. This hampered timely and accurate decision-making, leaving the business unable to keep pace with emerging trends and competitive demands.

Outcomes

85% reduction in research time

99% data relevance and accuracy

92% reduction in research costs

Client

Global Insurance Provider

Solution

SKENSE enabled precise extraction and contextualization of critical information from client claims summaries, delivering enhanced processing efficiency and accuracy.

Business Challenge

The client was facing difficulties with manual claims summary extraction and reconciliation processes that were time-consuming and error-prone. This led to delays in claims processing and customer dissatisfaction.

Outcomes

92 %+ accuracy in claims data extraction

70-90% productivity boost across claims operations

40% reduction in Average Handling Time (AHT) of claims summarization

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Client

Pet Insurance Provider

Solution

This award-winning implementation of SKENSE introduced intelligent claims processing and decision-making workflows, dramatically reducing settlement times.

Business Challenge

Longer claims processing cycles and high manual intervention rates were increasing operational costs and impacting customer satisfaction for the company.

Outcomes

80 %+ reduction in claims settlement cycle

50% straight-through processing achieved

1% leakage in claims processing

Client

Leading Global Bank

Solution

SKENSE implemented ML-driven financial spreading, extracting and contextualizing key financial data points.

Business Challenge

The bank was facing issues with financial spreading, which was resource-intensive and time-consuming, affecting risk assessment timelines and accuracy.