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Machine Learning in Insurance Industry

ML means more Possibilities.

  • Data Analytics
  • Intelligence automation
  • Machine Learning

Better predictions, better choices.

Machine Learning has a game-changing influence on the Insurance Industry. Insurers who initially hesitated and shallowed discomfort around its implementation are eager and quickly adopting ML. Numerous opportunities brought forward by Machine Learning from customer segmentation to product customization helps insurers to grow if leveraged.

Customer/Market Segmentation

Attract the right customers with segmentation. Analyze which customers are contributing to the profitability of the portfolio and are willing to form long-term relationships.

Based on the available data, any such customer segments can be discovered by segmenting the portfolio.

Recommendation engines

Such engines interpret the results and recommend appropriate actions.

Given similar customers, recommendation engines discover where individual insures may have too much, or too little, insurance. Then, proactively help the customers get the right insurance for their current situation.

Process Optimization

Insurance companies expose themselves to financial risks. ML-based risk management has opened the possibility of including complex models in the analysis to assess risk thus helping insurers.

The distinct advantage ML has is it can identify patterns from the data without any rules-based programming.

Churn/Retention

Identifying and targeting your most important customers is essential to reducing churn rates.

By leveraging machine learning algorithms, Identify behavior patterns of potential churners, segment these at-risk customers, and take relevant actions to gain back their trust.

Personalization

Personalized services that are in line with individual customer needs, preferences, and lifestyles improve customer satisfaction.

With machine-learning algorithms applied to data sets, insurers will be able to receive suggestions that fit specific customers through sophisticated selection and matching mechanisms.

Lapse Management

Modeling policyholder's lapse behaviors is important to a life insurer since lapses affect pricing, reserving, profitability, liquidity, risk management.

Adopt machine learning algorithms that Identify any policies that are likely to lapse, and how to approach the insured about maintaining the policy.

Our Success Story

Data Analytics

Transforming Data & Architecture landscape & enabling enterprise users with predictive & trust-worthy Data Insights

0+

Enterprise users enabled with Data-Insights

0%

Reduction in Customer Churn Rate

$0M+

Saved with Modernized Framework

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