Policies and contracts held by clients can be scanned and instantly sorted by the recognized entities and topics covered. The text of the policies can build the NLP recognition engine for custom AI training, which builds a stronger proprietary knowledge base. All these documents then start into an automated workflow as determined by internal managers.

Capabilities:

We processed 100,000+ images showing damage; then, we created cross-tabulated ML models with the corresponding repair bills.

We increased estimate accuracy by 41% compared to the previous platform, which the automotive brand abandoned altogether. To train the system, Iterate’s AI team processed 20M vehicle repair estimations and 100K images of various damaged vehicles. Many customers had a positive experience with this solution: 75% who started the process scheduled an appointment based on the estimated price.

Results:

Interplay’s Generate can recognize financial data, structure it, and port it directly to existing legacy systems for further processing, while also providing analytics and further training to the Generative AI engine.

Our platform runs as a completely private and secure LLM that can be trained and fine-tuned to the organization's policies, commercial practices, and customer records. These source documents then inform the LLM via RAG methods to provide an advanced AI for the institution.

Customer emails and chat sessions can be routed through a GenAI processor to automatically look up claim information, customer profiles, and any relevant actuary data.

Once a policy is referenced, the GenAI service bot can automatically prepare a summary of relevant policy coverages and even draft a response message for the customer service rep to review and respond back to the customer.

AI/ML has shown an incredible ability to model risk with great accuracy. With advanced pattern matching and neural modeling, multivariate inputs, and dynamic conditions, AI models can be trained to assess and price risk much faster and more accurately than traditional models and older algorithmic approaches. With ever-changing market conditions, ambiguously defined customer groups, and varied asset profiles, traditional risk profiling is rife with misclassifications. When neural network models are applied to customer and asset data sets, patterns are found, trends are recognized, often with insightful results.

Insurance providers have decades of records and petabytes of legacy data. As a result, providers have an enormous investment to existing legacy systems that have functioned for years without major disruption. CTOs are faced with the challenge of protecting this investment while also incorporating new technologies.

Interplay can connect modern AI engines to legacy systems that may not have APIs available: text scraping, RPA, ANSI feeds, etc. can all work inside an integrated low-code application flow.