AI Meets BiPRO Standards: Why Structured Interfaces Are Essential for Automated Case Processing

The Central Thesis from BiPRO Day 2026

Artificial intelligence was the dominant topic at BiPRO Day 2026 – and precisely in the anniversary year when BiPRO e.V. celebrated its 20th existence. At first glance, this may seem surprising: Why is a two-decade-old interface standard needed when modern AI systems can supposedly process unstructured data? However, the BiPRO community at the event took a clear opposing stance: Artificial intelligence does not replace standards – it makes them more important than ever. This is because powerful AI systems require structured, quality-assured, and semantically unambiguous data to function reliably.

For IT managers in insurance companies and at intermediaries, this is an important classification: In practice, AI projects rarely fail due to model quality but often due to the data basis they rely on.

Why Standardisation is the Prerequisite for Automation

A practical example illustrates the principle. In the automated processing of motor vehicle documents, AI-supported systems today extract manufacturer and type key numbers directly from the vehicle registration document and provide the data required for premium calculation, without manual entry. After transmission via BiPRO Standard 430 – the cross-company digital mailbox – incoming documents and policy data can be automatically categorised, personal and contract data extracted, and converted into a uniform, machine-readable structure.

The crucial point here is that this automation only works reliably because the underlying data is already available via a standardised interface in a defined structure. Without such pre-structuring, the AI would have to individually interpret every document format, every field designation, and every special rule of individual insurers – with a correspondingly higher risk of errors and effort.

In addition, there is an economic aspect: Every interpretation of unstructured raw data by a language model permanently incurs ongoing token costs, which would not arise to this extent with standardised data. Since the prices for model usage are determined by the manufacturers and can change, an architecture that relies heavily on AI interpretation of unstructured data instead of structured interfaces creates an additional economic dependency on the pricing policies of individual providers – with the risk of cost explosion as soon as these prices increase. Standardised data transfer via BiPRO reduces this dependency because AI can be used more targeted and with less interpretation effort.

From Interface to AI Standard

At BiPRO Day 2026, the vision of an extended BiPRO AI standard was therefore discussed. This standard is intended to complement existing norms with aspects such as semantics, ontology, and context, thereby ensuring the interoperability of AI-supported applications between insurers, intermediaries, and service providers. For its 20th anniversary, the BiPRO community thus outlined a path towards a common AI standard designed to build trust in the insurance industry.

This development is also remarkable against the backdrop of broader industry movements: As early as January 2026, Allianz and Anthropic formed a global partnership to promote responsible AI in insurance (Source: Allianz press release), focusing on AI for employees, agent-based automation, and a shared toolkit for AI capabilities such as payload extraction, natural language processing, and the orchestration of AI agents. Here, too, the same basic idea emerges: Without a harmonised data basis, AI cannot be meaningfully scaled on a large scale.

Concrete Automation Areas Along BiPRO Standards

The combination of BiPRO standardisation and AI technologies already opens up several practically relevant application areas today:

• Document Processing and Data Extraction: Personal data, addresses, and relevant key figures can be automatically extracted from scanned IDs, claims documents, and vehicle registration documents – including plausibility checks, such as comparing reported claims with insured maximum amounts.
• Customer Service and Advice: Based on structured tariff and contract data, chatbots can independently answer customer enquiries and relieve the burden on case processing.
• Back Office Automation: Robotic Process Automation (RPA) takes over repetitive, rule-based tasks in data processing, claims handling, and regulatory compliance.
• Analysis of Unstructured Data: Natural Language Processing methods evaluate emails, customer correspondence, and reviews to create sentiment analyses and specifically improve customer service.
• Fraud Detection: AI-supported systems identify anomalies in large data volumes that would be difficult to detect manually – here, too, a consistent data basis is fundamental for reliable results.

A Competitive Advantage for the German Insurance Industry

Furthermore, the European dimension of this development is noteworthy. The BiPRO data model and interface specifications already largely cover the requirements of the EU Financial Data Access (FiDA) initiative, particularly with regard to end-customer processes and customer data. As industry-wide implementation is already well advanced among most BiPRO members, the German insurance industry thus gains an advantage over countries whose standardisation initiatives are still at the beginning of norm-setting.

Practical Implications for IT Managers

This results in several recommendations for action for planning AI projects in insurance IT:

• Data Quality Before Model Selection: Before investing in specific AI applications, it should be checked whether the required data is already structured via BiPRO standards or whether standardisation gaps need to be closed first.
• Utilise BiPRO 430 as an Automation Basis: The broker mail standard offers the most pragmatic entry point for many companies to process document flows automatically and with AI support.
• Monitor the Development of the BiPRO AI Standard: Companies that adopt semantically enriched data models early will be better positioned when the discussed AI standard takes concrete form.
• Consider FiDA Compatibility: Since BiPRO structures are already largely FiDA-compatible, investments in standardisation can be used twice today – for current AI automation and for upcoming regulatory requirements.

Conclusion

The supposed competition between AI and standardisation proves to be complementary upon closer inspection. AI systems only reliably unleash their automation potential in insurance IT when they are based on structured, quality-assured data – and BiPRO standards have provided precisely this foundation for 20 years. Those planning future AI investments should therefore not first ask about the appropriate model, but about the quality and standard conformity of their own data basis.