Data Transparency and Unlocking Insights: Accessible Data for International Insurers and Reinsurers in Turkish Insurance Sector

The Turkish insurance industry sets a prime example with its transparent data practices and robust infrastructure supporting data analysis. Thanks to widely embraced platforms like Tramer and similar standardized data models, accessible across all sectors of society, Turkish insurance sector is now more ripe for leveraging artificial intelligence (AI) and machine learning (ML) tools than ever before. For instance, the existence of a shared data infrastructure, especially in the realm of motor insurance, is a coveted asset for many countries.

However, the effective utilization of data varies across companies and the depth of analysis remains a subject of debate. From my observations, there appears to be a notable variance in data utilization among companies. A significant challenge lies in ensuring that the wealth of detailed data, readily available at a company-specific level and often publicly accessible, is effectively harnessed.

It's remarkable to note that a trove of detailed data, which many industry insiders may not even be aware of, is readily accessible. While it's disheartening to see underutilization of this data, it's encouraging to recognize that the foundational work is largely complete.

Numerous pressing issues persist in our agenda, such as the timely updates on LoBs’ general conditions, ensuring alignment between regulatory bodies and the judiciary, divergent interpretations of insurance terms among stakeholders and customers, and the sector's challenge in effectively communicating its products. A glaring example of this is the prolonged lack of harmony between insurers' general condition interpretation and judges' and high court approach in MTPL  insurance over the past 15-20 years, a situation exacerbated by our own contributions.

However, steering back to the central theme of this article, significant strides have been made in preparing data for analysis. In this regard, stakeholders in the sector, led by SBM (Insurance Information and Monitoring Center) and TSB (Insurance Association of Turkey), are commendably driving progress. While it may be difficult to fully appreciate the magnitude of this achievement, I extend my heartfelt congratulations to the architects of this success and to all colleagues dedicated to enhancing the system day by day.

While only a fraction of industry professionals may currently engage with it, having readily available data marks the crucial starting point, and indeed, the most challenging one.

In my interactions with foreign insurance companies, I've noticed that while their data analysis capabilities are advanced, they often face challenges in accessing data with sufficient granularity. In contrast, in Turkey, the landscape is markedly different; while there are significant variations in the analytical prowess of individual companies, accessing data doesn't require extensive digging. For instance, even within TSB data alone, there exists a wealth of detail accessible to everyone, without exception.

Moreover, during discussions with reinsurance company professionals, who are pivotal stakeholders in the sector, I've found that many of their inquiries revolve around the key aspects encapsulated in this data. With the ability to conduct detailed analyses, they would be empowered to delve into every facet of the industry, from specific companies to specific LoBs and products. This would enable them to scrutinize company performances down to the minutest detail.

Global insurance companies with investments in Turkey or reinsurance companies assuming risks in the country have unrestricted access to detailed data whenever needed. This enables them to conduct thorough analyses, including competition analysis, evaluations of company performance, and monitoring the impact of risk-sharing on profitability.

The list below offers only a glimpse into the vast array of available data. Therefore, it's important not to consider data transparency as confined to these examples. TSB data alone provides access to much more than what is listed here.

• GWP data categorized by LoB and company
• GWP data segmented into main and sub-LoBs (Approximately 100 breakdowns)
• GWP data with LoB  breakdowns presented in separate worksheets
• GWP data detailed by sales channels (Direct, Agency, Bancassurance, Broker, Other)
• GWP main and sub-LoB production data by sales channels
• Rankings of premium production and fluctuations in market share
• Periodic data on nominal and real increases in production
• Rankings, market share, and growth data based on LoBs
• Influence of premium production on policy counts, thus impacting average premiums and pricing policies and UW strategy at the company level, as elaborated above
• Detailed breakdown of premiums based on sales type (Tele-Sales, E-Commerce, Traditional)
• Comprehensive breakdown of policy quantities based on sales type
• Detailed worksheets specific to Kasko (MoD) insurance (company details, vehicle types, Gross Written Premium, policy counts, average premium, market share, pricing strategy, etc.)
• Worksheets specifically detailing MTPL insurance (company details, vehicle types, GWP, policy counts, average premium, market share, pricing strategy, etc.)
• Exhaustive Balance Sheet details presented in both detailed and summarized formats by company
• Comprehensive Profit and Loss (P&L) details provided for companies in both detailed and summarized formats
• P&L details segmented by Line of Business (LoB) with all relevant drill-down figures
• Ratio analysis of financial tables (Paid claims/GWP, Reserves/GWP, Net Paid Claims/Net GWP, Claims Ratio, Operational Expenses/GWP, Combined Ratio, etc.)
• Analysis and comparison of Claims Ratios and Combined Ratios for companies and LoBs, giving a hint of reinsurance arrangements and retention strategies
• Detailed breakdown of Operational Expenses by company and LoB, including expense specifics

As evident from the headings listed above, we're dealing with an extensive array of data encompassing thousands of entries. Another notable deficiency lies in the rich insights gleaned from the mutual utilization of data, providing intricate details about companies. Through competition analysis, we can discern the profitability levels of competitors across various branches and products, offering a comprehensive view of each company akin to a complete X-ray.

In this regard, the Turkish insurance sector sets a commendable example for both domestic and international industries. Visualizing and presenting raw data in a meaningful matrix structure promises to unveil a magnificent mosaic, illuminating the sector's dynamics.

While publicly available claims data may currently lack depth, our focus remains on Underwriting and Sales, mirroring our dedication across all domains. As someone who has worked extensively in Underwriting, Sales, and Claims—including legal and subrogation aspects—I'm eagerly anticipating the holistic view that will emerge once claims data attains greater granularity. With this enhanced data granularity, stakeholders will be empowered to make targeted decisions based on the competencies and expertise of data handlers.

Although we acknowledge there's still a journey ahead, I extend my heartfelt congratulations once again and bowing my hat to all institutions and colleagues who have contributed to shaping this robust infrastructure.

 Best regards,

Fatih Yıldırım

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