ART© - Accelerated, Risk-based Transformation

ART© is designed to facilitate and accelerate operational transformation as an iterative process with a risk-based methodology and by adopting established concepts such as underwriting appetite, investment management strategies etcetera. ART© has been initially designed as a response to the digital transformation gap in the (re)insurance sector, also amplified by difficulties to integrate new options from insurtechs in insurance core processes. ART© can easily be adapted to any other area and/or industry.

How to take and actively manage transformation risk?

Iterative steps (standard):

  1. Agree on risk tolerance with management

  2. Define parameters for risk clusters A, B and C

  3. Prepare reactions for detected issues in cluster B

  4. Apply target solution over defined period

  5. Track & manage cluster B; spot-check cluster A & C 

  6. Quantitative and qualitative review for all clusters

  7. Optimize target solution

Goals of iterations:

  • Challenge target solution by monitoring cluster B

  • Optimize risk tolerance and result of clusters

  • Increase number of transactions in target cluster A

  • Develop solution for remaining cluster C (post-transformation)

  • Sensitize ecosystem to ongoing transformation

  • Move from culture of failure to risk culture

  • "Learn to walk before you run"

Examples for ART@ application (industry agnostic)

  • Automated mail response by using Company ChatGPT

  • Optimize distribution and sales by leveraging profiling and matching applications

  • Simplification and standardization of IT infrastructure and applications (incl. security)

  • Cope with volatility, uncertainty, complexity and ambiguity (VUCA) as new reality

  • Enable continuous, forward looking, dynamic workforce and talent management

Examples for ART@ application (insurance specific)

  • Increase straight-through processing in underwriting and claims with predictive analytics

  • Analysis and optimization of products and portfolios thanks to large language models (LLM)

  • Use data analytics to identify proxies and allow clustering for enhanced risk selection