AI for Insurance Providers
Dynamic document processing, AI/ML-based risk assessment, AI pattern clustering
AI for Insurance Providers
Dynamic document processing, AI/ML-based risk assessment, AI pattern clustering
AI for Insurance Providers
Dynamic document processing, AI/ML-based risk assessment, AI pattern clustering
Goal & Purpose
AI is revolutionizing the insurance sector by digitizing all inbound claims, automating the damage estimation process far more accurately and faster than human assessors, and building large structured data sets for superior risk assessment, pattern matching, and seamless customer experiences.
Risk Assessment
Dynamic Documents
Legacy Integration
Automation
Dynamic Documentation
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:Automatic RPA document sorting based on content
NLP entity recognition of all proper nouns, verbs, terms
Build corporate knowledge base
Unstructured text to structured data
AI Damage Estimators
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:
Increased accuracy of estimates
Better customer satisfaction
Accurate real-time demand projections
Enriched overall data set
AI Text Support
Customer assistant chatbots can be powered by a specific vocabulary built by computer modeling of all existing auction texts with Natural Language Processing (NLP).
Hundreds of “intents” are recognized by the AI-chatbot. Roughly half of all inquiries are immediately handled by AI while the rest are passed on to human agents. This same vocabulary drives search, quizzes, and trend interactions.
System Flow
AI Credit Risk Modeling
AI/ML has shown an incredible ability to model risk with great accuracy. With advanced pattern matching and neural modelling, multi-variate 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.
Integrate with Legacy Systems
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.
Financial Data Extraction
Insurance Providers have decades' worth of unstructured data, legacy documentation on paper records, simple image scans, tape backups, and older data systems. Claims often involve receipts, and 3rd party printed financial statements, or from unstructured data feeds. Interplay’s dynamic data extraction ML 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 AI/ML.