Artificial Intelligence in Insurance
The pressure to drive one's own digitization or digital transformation does not stop at the insurance industry either. Artificial Intelligence (AI), Data Science, and Machine Learning may be key to driving insurance digital transformation. Although various studies show that the insurance industry is currently still concentrating on Robotic Process Automation, the IT infrastructure in insurance companies is not yet fully prepared for Artificial Intelligence, Data Science and Machine Learning Solutions, and Machine Learning Algorithms.
At AI Superior we understand that AI and Data Science pose a challenge, and we recognize that decision-makers don't always trust AI and Data Science. It may seem that machine learning solutions are only accessible for big players like Google or Amazon, but we are working on this challenge, and we can show you that AI and Data Science can also bring value to insurance companies.
What can AI do for the Insurance Industry?
Most common AI Use Cases
Effective Risk Management
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Machine Learning modelling and Data Preparation for Underwriting
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Interpretability of AI Model Decisions
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Behavioral Analysis
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Data Enrichment Services
Efficiency-focused Optimization
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Pricing Policy and optimization of business-relevant KPIs
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Customer Churn Prediction and Retention Strategy assessment
Claim Processing Automation
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Car damage control and examination, repair costs estimation
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Property assessment and evaluation
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OCR-based claims processing automation
Project References
Road Entities Recognition and Traffic Analytics
Category
CV, Core MLClient
System IntegratorPotential industries
RetailIndustry
Government
Social Media Analytics for Marketing Activities
Technology
Core MLClient
BankPotential industries
Retail, Telco, Insurance, EducationIndustry
Government
Road Entities Recognition and Traffic Analytics
Technology
Core MLClient
Real Estate CompanyPotential industries
RetailIndustry
Real Estate
Our Project Approach
The AI project lifecycle has been adopted from an existing standard used in software development. Also, the approach takes into account the scientific challenges inherent in machine learning projects involving software development processes. The approach aims to ensure the quality of development. Each phase has its own goals and quality assurance criteria that must be met before the next stage can be initiated.
Deep Dive in Business Challenges and our AI Expertise
Data enrichment
AI Superior helps to improve the predictive power of your models by providing Data Enrichment Services. It includes data enrichment and data fusion modules allowing to collect, fuse and streamline various heterogeneous data to your AI applications. This enables many use cases such as: Geospatial-based risk indices generation to explore districts and regions on the map and consume demographics, governmental statistics, public information and infrastructure-related insights Satellite imagery-based data for hazard assessment of property e.g. fallen trees or flood prediction, crises prediction and others.
Behavioral analysis
To understand your customers behavioral patterns and risks associated with them, AI Superior offers a behavioral analysis package. Based on sophisticated machine learning models it allows you to get deeper insights into your customers behavior, segment them based on their assignment to a particular risk group and take relevant actions. A typical example of application of such an analysis is a driving style scoring where the behavior of every driver is analyzed in order to obtain driving profiles and risk of an accident associated with them. Such analysis requires telematic data obtained from an installed sensor or a smartphone. Alternatively,…
Interpretability of decisions by AI Models
Due to a high number of variables and complexity behind modern machine learning algorithms, it is hard to interpret the reasoning and decisions made by machine learning models. AI Superior can help to overcome this issue. We can provide a tool that gives an explanation either over the whole population or for an individual customer. We work with a wide variety of methods to name a few: Neural Networks, Gradient Boosting, Random Forest.
Pricing policy and optimization of relevant business KPIs
Are you looking for a higher number of customers and are ready to take more risk or rather to stay risk-averse and optimize the profitability with other means e.g. increasing the premium? All these relevant questions for Underwriting, Finance and Marketing can be answered with the help of Data Science by optimization algorithm to further improve the Unit Economics of your Business.
Machine Learning and Data Preparation for Underwriting
Practical experience and theoretical background allow us to properly represent various types of heterogeneous data into ready to use machine learning data sets. We perfect the art of feature engineering for time-series data, financial transactions, spatiotemporal information, behavioral patterns and many more. A high-quality risk scoring model is one of the key success factors in risk management. Our PhD level data scientists in Machine Learning can train and properly validate a Risk Scoring Model that will have a comprehensive view of the insured.