Allianz
A radical algorithm for the healthcare market.
Predicting the changing costs of health insurance is notoriously difficult for providers the world over. But machine learning can help.
I led an 8-strong design team to successfully develop a new product suite experience which predicts, prices, and evolves health insurance products with respect to inflation, trends, and rising costs. All while adapting to local laws and regulations from Malaysia to Brazil.
Background
Historically, the health insurance industry struggled with accurate product pricing, cost anticipation, and adapting to market changes and consumer needs. Allianz SE's decentralized operating entities (OEs) faced unpredictable markets, inaccurately priced products, ballooning costs, non-unified business approaches, and a lack of transparency from the global headquarters. This spanned across underwriting, pricing, product development, reporting, and claims management. Moreover, the complex landscape of market-specific laws and regulations further complicated health insurance operations.
Integrations
Approach
Under the leadership of a diverse team of researchers, designers, project managers, engineers, and data scientists, we (ADH) embarked on an innovative journey. We conducted extensive contextual inquiries, gaining insights into the real needs of the global business audience in various markets. With this knowledge, our core design team collaborated closely with engineering teams as well as the C-suite to design and build scalable solutions.
Development
Definition of key MVP flows
MVP Design
MVP Product Configurator*
*Note that the visual design is limited by Allianz’s NDBX design libraries.
Solution
The core design team, comprised of talented full-time and freelance professionals, leveraged Sketch, Abstract, and the Allianz NDBX design library to deliver pixel-perfect designs. The team embraced an agile approach, utilizing Jira for remote and asynchronous collaboration during the pandemic. As the product suite and user base expanded, ADH adopted a regular paradigm of A/B testing to ensure their products accurately met evolving demands.
Our solution was a centralized springboard powered by an ever-evolving algorithm, offering a suite of global products which governed and transformed health insurance. This algorithm predicted, priced, shaped, and evolved health insurance products while adapting to local laws, regulations, inflation, and rising disease costs.
The solution provided unprecedented transparency to the headquarters. By embracing this venture, Allianz SE ensured its survival and proactively disrupted itself, preventing potential disruption from international competitors.
Results
As the algorithm matured, we realized the potential to shift the burden from users to the algorithm, reducing the overall user interface footprint. This insight brought forth ethical considerations, envisioning a future where the solution itself could make certain roles redundant.
ADH acknowledged the need to address these implications as we continued to innovate.
The product suite was widely adopted in each market, resulting in continuous growth and increased intelligence of the solution. The success led to the development of an intelligent assistant named ALEX, akin to Alexa or Siri, providing global and localized insights for executives like Günther Thallinger.
ADH's algorithms and smart assistant empowered decision-making, reinforcing their commitment to transforming health insurance.
Conclusions
The case study of Allianz Digital Health showcases how their groundbreaking suite of AI-powered tools which are revolutionizing the global health insurance industry. By centralizing governance and leveraging advanced algorithms, ADH overcame historical challenges and achieved remarkable results.
Contributions
Research
Concept Development
Product Vision Definition
Strategy Direction
Design Direction
Product Roadmap Definition
Project Management
Vision Workshops
Collaboration with Engineering
Collaboration with Data Scientists
Product Development
User Story Definition
Agile Ceremonies
Design CoP (Community of Practice) Lead
User Testing
Team Structure
Role: Head of Design
Supportive Roles:
Senior Product Designers
Product Designers
Junior Product Designers
Multiple Actuarial Teams
Multiple Data Scientist Teams
Multiple Engineering Teams
Multiple Project Mangers
Regional Partners
CPO
Company: Allianz