AI | GenAI Enabled Underwriting and Smart Insurance Solution (AUSIS) - Case Study
AUSIS AI/GenAI Platform by Artivatic changing the future of underwriting with focus on digital, personalization, dynamic profile based, instant and in under 60 seconds.
About Artivatic: Artivatic is a no-code/low code full stack solution provider empowering Insurance and Healthcare sector with a product suite built with Artificial Intelligence (AI) and Machine Learning (ML) based technology. A variety of use cases and specific work done by Artivatic using AI / ML based technology include:
Digital customer acquisition platform | Smart Automated Underwriting and Risk Assessment | Customer Financial and Health Profiling and Scoring | Disease Prediction | Non Disclosure and Early Claim Propensity Scoring and Prediction | Fraud Intelligence | Gen AI Models for Interpretation of Medical and Financial documents | Deep learning models to digitise various insurance documents such as KYC, Financial, Medical documents | Image Based analytics | Persistency and Product Recommendation Models
AUSIS Helps In - Insurance and Healthcare Sector
Beneficiary – Life and Health Insurance Customer Onboarding, Risk Underwriting, Profile Based Personalisation, Early Claims, Personalized Pricing, Medical & Health Intelligence Scoring, Location Intelligence, Alternative Data Insights, Fraud Control functions, Medical Validation & Triage and more.
Problems with traditional /existing underwriting process & systems:
The existing underwriting platform and rule engine was not scalable, user friendly, automated and was not an integrated solution.
The underwriting process was manual, dependent on visual inspection of proposal form data and did fully realise the benefit from past trends and experience.
There were limited external sources of data aiding in making underwriting decision
Customer profile based underwriting decision making process needed a lot of objectivity
Most of the data and information used for underwriting decision was not digitised and as a result was not usable for any experience building
As a result of the above steps, the process was manually intensive, the underwriting rules were intuitively built, the decision making was subjective. was not data-backed, could be prone to errors, process did not learn from the positive or adverse experiences from past decisions made
Cost of product purchase is high to customer and leads in wrong benefits purchase too
Customer is unhappy as the entire process takes more time with no personalized need understanding
High cost and inefficient process at the insurance company backend
How AUSIS Platform Plays vital Role in solving underwriting challnages for insurance & healthcare sector:
AI plays a pivotal role in solving the problem stated above by using the power of deep learning models to learn decision patterns and claims experiences from large data sets and by utilizing data from diverse sources which are manually impossible to assess and provide scoring of various aspects affecting the underwriting decision such as Financial Score, Health Score leading to an overall Customer Confidence Score along with a recommendation. Further the recommendation is backed by Explainability by the AI Model with feature importance at a proposal level.
The AI ML underwriting risk scoring model enables faster decision making for proposals that are referred to the underwriter while the low risk cases are recommended to be decided on a Straight Through (STP) basis without the need for being referred to the underwriter.
AI models developed by Artivatic for Underwriting and Risk Assessment significantly contribute to the capability for Insurance companies to assess risk due to:
Enhanced Risk Assessment : AI can analyze vast amounts of data from various sources, including historical claims data, financial records, medical information, and external databases. This analysis helps our clients to evaluate the risk associated with each proposal more accurately, identifying potential red flags or inconsistencies.
Automated Underwriting : AI powered systems automate the underwriting process for insurance applications, reduces manual intervention and speeds up the approval process for low-risk applicants. This automation can significantly minimise routine tasks such as data entry, verification, and risk classification and profiling.
Predictive Modeling : Predictive models using AI Algorithms that use historical data to forecast future claim probabilities have been implemented by Artivatic that enables our clients to progressively develop pricing strategies, redefine underwriting policies and guidelines and develop targeted marketing campaigns to increase share of the wallet from existing low risk customers and continuously optimize risk management practices.
Fraud Intelligence : AI driven models coupled with Computer Vision image analysis models can analyze patterns and anomalies in data, images, photographs, documents to identify potential cases of fraud or misrepresentation. The system can flag suspicious applications for further investigation, helping clients to mitigate and improve their early claim experience arising due to fraudulent activities.:
Customer Profiling : Based on various attributes, the AI models determine customer persona based on their profile, over risk, financial risk and health risk that enables underwriters to assess the associated risks based on historical patterns and forecasted future claim propensities thereby improving the accuracy of the overall underwriting decision eventually leading to building a healthy and profitable insurance portfolio
Impact Metrics post implementation of the solution
Operational Efficiency
Time to process reduced by over 95%. AI ML model recommendation with inferencing within 60-90 seconds
Underwriter Productivity improvement by 35% - 50%
Reduction in errors due to oversight and manual processes
Straight Through Processing (STP) issuance increase by about 35% to 50%
Cost Efficiency
20%-30% cost reduction due to reduction in per unit cost and lower hiring requirements to handle scale
Revenue Optimisation
15% to 20% reduction in business leakage due to reduction in underwriting requirements
Reduction in adverse claims experience which straightaway adds to o the insurer’s bottomline
Customer Experience
Faster issuance turn around times results in enhanced distributor and customer experience
How AUSIS is also focusing on Responsible AI:
Unbiasedness/Fairness:
Utilizing diverse and representative data to train underwriting models, mitigating biases present in the data.
Implementing fairness-aware algorithms and regular audits to detect and address biases in the underwriting process, ensuring equitable outcomes for all individuals.
Data Privacy and Security:
Encryption and Access Controls: Implementing encryption techniques to protect sensitive data and enforcing strict access controls to ensure that only authorized personnel can access
Compliance with Regulations: Adhering to data privacy regulations and implementing security measures to ensure compliance with industry standards and protect customer data from unauthorized access.
Explainability:
Use of model explainability techniques such as SHAP and LIME to identify key factors influencing underwriting decisions.
Incorporation of transparent and interpretable machine learning models, allowing stakeholders to understand the rationale behind underwriting decisions.
If you are an insurance company or reinsurance or MGA, looking for transformation of your existing underwriting process or risk assessment systems, reach out to contact@artivatic.ai for demo.
For more info visit: https://artivatic.ai/