Neova Sigorta to transform insurance pricing with AI

Similar collaborations between SAS and SADE demonstrated that the right pricing policy can increase sales up to 15% and decrease the insurer’s combined ratio by 10%.

The deployment, expected to last between six and eight months, will help Neova Sigorta offer more appealing prices to new and existing customers. It is also expected to significantly increase market share in new regions and bolster renewal rates in established regions.

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“In our business, we always prioritize customer comfort and satisfaction above all else, and we know that affordable premiums are one of the main factors that contribute to customer happiness and retention,” said Neslihan Neciboğlu, Neova Sigorta CEO and board member. “We’re thrilled to work with SAS and SADE on this project that will make our customers even more satisfied with the Neova brand.

“We evaluated several platforms to implement this project. SAS Dynamic Actuarial Modeling was the most comprehensive platform, allowing us to train, deploy and automatically monitor the performance of high-accuracy machine learning models. We also appreciated that SAS’ solution capabilities can be extended to other critical functions such as next best offer generation and insurance fraud detection, aiming to improve customer experience and reduce prices even further.”

“Since the establishment of SADE, our work in the insurance sector has been one of our focus areas, and we’ve implemented many projects for fraud detection, data quality and other use cases in the Turkish market,” said Deniz Çelik, SADE co-founder. “The machine learning-based pricing project with Neova Sigorta and SAS is of special importance to us, as it is one of the first and finest examples in the region.

“Neova Sigorta truly understands the importance of AI and ML, and the company’s knowledge and vision put them ahead of many players in the market and in the underwriting field.”

Polishing premium pricing with AI
Auto insurance pricing has historically been based on generalized linear models (GLM). Although these models have good interpretability – performance is predictable and easily explained – the accuracy of estimates tends to be limited, ultimately resulting in higher prices and lower sales.

In addition, limited sample sizes and risk assessment policies significantly deteriorate the quality of the data used to train GLMs, which can cause errors and introduce bias.

Looking to modernize, Neova Sigorta will switch to an alternative approach based on sophisticated ML algorithms to price its auto offerings.

Unlike GLMs, machine learning algorithms do not make any assumptions about the properties of the data the models are using. These algorithms also consider more variables that define customer behavior and are designed to extract more granular patterns in the data. Therefore, ML algorithms produce much more accurate – and realistic – results.

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“Fair, accurate and timely insurance policy pricing is a necessity for insurers in all markets,” said Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions. “Consumers often look for more affordable insurance options amid inflation and other economic turbulence. If insurers can’t offer competitive pricing, they risk losing customers to the competition – and agents often follow when they can’t achieve their goals. This can severely damage an insurer in mere months.

“Switching to a modern, machine learning-based approach with real-time decisioning capabilities will greatly improve an insurer’s bottom line. As important, is built-in model governance. Making these decisions in a trustworthy and transparent way is critical to ensuring satisfied customers.”

“As insurers – and the financial services sector at large – invest in AI, they want to see quantifiable profitability and tangible benefits for customers,” added Rasim Eğri, General Manager of SAS Turkiye & Central Asia. “This use case demonstrates the abundant possibilities that await as industry moves into the AI-powered future. Neova Sigorta is blazing a trail for insurance innovation.”

Learn more about SAS Dynamic Actuarial Modeling at sas.com/actuarial.

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