More effective risk management
Underwriting models, behavioural analysis and enriched data sources give a sharper view of each customer’s risk, with decisions you can explain.
Industries · Insurance
Artificial Intelligence in Insurance from risk scoring to claims
Many insurers still focus on robotic process automation, and their IT is not yet ready for machine learning. We help insurance companies of every size move further: risk models for underwriting, automated claims processing and pricing that reflects each customer’s actual risk. AI is not reserved for the largest players.
- Risk models that outperform statistical baselines
- Personal pricing from real driving behaviour
- Faster claims with automated damage assessment
- 57% of our team hold a PhD
- German company, working under the GDPR
Discuss your project
5.0 18 reviews on Clutch Go Global Awards winner
Trusted by enterprises, scale-ups and non-profits
What you gain
What AI changes for insurers
Three areas where machine learning moves the numbers in insurance.
Claims processing automation
Computer vision examines car damage, estimates repair costs and supports property assessment, so claims move faster with less manual work.
Efficiency-focused optimisation
Pricing policies and business KPIs optimised with data science, plus churn prediction to target retention where it pays off.
What we deliver
AI solutions for insurance companies
The use cases we implement most often for insurers, grouped around risk, claims and efficiency.
- 01
Machine learning for underwriting
We turn heterogeneous data such as time series, financial transactions, spatiotemporal information and behavioural patterns into feature sets for high-quality risk scoring models.
- 02
Interpretable model decisions
Tools that explain what drives a model’s decision, either across the whole portfolio or for a single customer.
- 03
Behavioural analysis
Machine learning models that reveal customer behaviour patterns, assign customers to risk groups and support the right action for each.
- 04
Data enrichment services
Data enrichment and fusion modules that bring heterogeneous sources into your models, for example geospatial risk indices for districts and regions.
- 05
Usage-based insurance
Deep learning on smartphone telematics (accelerometer, gyroscope, GPS) detects driving behaviour, scores each trip and calculates a personal discount.
- 06
Car damage examination and repair cost estimation
Image analysis that examines vehicle damage and estimates repair costs to speed up motor claims.
- 07
Property assessment and evaluation
AI-supported assessment of property claims and valuations to reduce manual review.
- 08
Pricing policy and KPI optimisation
Grow the customer base and accept more risk, or stay risk-averse and protect profitability? Optimisation algorithms answer these questions for underwriting, finance and marketing.
- 09
Churn prediction and retention strategy
Models that identify customers likely to leave and help you assess which retention strategies work.
Not sure which of these you need? Describe the problem and we will recommend the approach.
Discuss your projectCase studies
All case studiesResults we delivered for our clients
Insurance · Machine Learning
Telematics-based driver scoring for usage-based insurance
Per tripdriving score and personal discount
A deep learning model that analyses telematic data from drivers’ phones to detect driving behaviour, score each trip and calculate personalised discounts and safe-driving recommendations.
Read the case study
Risk estimation model for a medical insurance company
Outperformed statistical baseline models
A neural network model, trained on five consecutive years of historical medical data, that estimates the risk of economic loss so a niche health insurer can optimise its pricing policies.
Player churn prediction for an online gaming platform
11.3% churn rate after new retention strategies
A machine learning model that learns player behaviour during the game and predicts the probability of churn over a given time horizon, so the platform can apply the most relevant retention strategy.
Credit scoring model for an SME lending company
800+ features from 14 data sources
A machine learning model that predicts borrower default and fully automates underwriting, improving loan portfolio quality and cutting decision time from hours to a fraction of a minute.
Why AI Superior
Research-grade AI, delivered like a product company
Many vendors can build a demo. Making AI work reliably on real data, inside real business processes, is the hard part, and it needs a different kind of team.
01
A research-led team
AI Superior was founded in 2019 by AI researchers with published papers and patents, and the whole team works to that research standard.
- PhD57%
- MSc28%
- BSc15%
02
Built to reach production
Every project is de-risked in stages, with a go/no-go decision at each milestone, so budget only goes into approaches that have worked on your data.
4× our proof-of-concept success rate compared with the market average
03
A complete team from day one
Strategy, models and software are designed by one team, so nothing gets lost in hand-overs between vendors.
- Product AI product owner, business analyst, project manager
- Data Data scientists, ML engineers, BI analyst
- Development Software engineers, QA, DevOps
04
Your data stays under control
When data must not leave your organisation, we build on private, self-hosted models instead of third-party AI services.
- GDPR, German company
- Self-hosted LLMs
- Transparent methods
Our team
The experts behind your project
Your project is led by senior data scientists and engineers with research backgrounds, supported by our product and development teams.
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Dr. Ivan Tankoyeu CEO PhD in Computer Science. Recognised by the IEEE GRSS Society, winner of the Global AI Hackathon 2017, Kaggle TOP500 data scientist (2014).
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Dr. Sergey Sukhanov Advisor PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
Client reviews
5.0 Rated 5.0 by the people we build for
18 verified reviews on Clutch
“AI Superior showed a strong commitment to delivering high-quality solutions to us.”
Improves and maintains the data infrastructure of a credit solutions company: data pipelines, ML models and custom reports.
“I appreciate their approach, expertise, and the quality of information they provide.”
AI and machine learning consulting to identify the right use cases for a web design and graphics company.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
How we work
A staged AI project life cycle
Every stage ends with a result you can check, and you decide on the next one only after seeing the previous one work. You keep control of scope and budget.
- Estimate before you commitYou see scope and expected results before the build begins.
- Go/no-go after every stageEach stage ends with a result you can check and a decision on the next step.
- Risks reported openlyWe share risks and opportunities as soon as the analysis shows them.
- Go / no-go decision
Discovery
We work through the business problem with your team and define the direction of the solution.
You get: Scope, approach and a high-level estimate of effort and expected results
- Go / no-go decision
Data and feasibility
We get to know your team and data and check whether AI is the right tool for this problem.
You get: A data assessment and a clear feasibility verdict before any build starts
- Go / no-go decision
Proof of concept / MVP
We start small, using the data already available, to test the solution in practice.
You get: Measured results on your own data and a basis for the investment decision
- Go / no-go decision
Integration and scaling
We integrate the solution into your existing systems, fine-tune the models and adjust them where needed.
You get: A solution running inside your processes, compatible with your data and systems
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Evaluation
We review the results with you and make sure they are read correctly.
You get: What the solution delivered and where to improve next
Awards and recognition
Ranked among the top AI companies
Recognised by international business awards and by independent B2B platforms that rank companies on verified client reviews.
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Top Artificial Intelligence Company Germany 2026 · Clutch -
Top Machine Learning Company Germany 2026 · Clutch -
Top Computer Vision Company Germany 2026 · Clutch -
Top Generative AI Company Germany 2026 · Clutch -
Go Global Awards Winner 2021 · International Trade Council -
Best Data Science & AI Service Provider, Europe 2021 · German Business Awards -
Top Artificial Intelligence Company 2023 · Clutch -
Top Machine Learning Company 2023 · Clutch -
Clutch Champion Fall 2023 · Clutch -
Clutch Global Fall 2023 · Clutch -
Top BI & Big Data Company Germany 2023 · Clutch -
Top IT Services Company Germany 2023 · Clutch -
Top Artificial Intelligence Companies 2023 · TrueFirms -
Top Machine Learning Companies 2021 · Techreviewer -
Most Reviewed IT Services Companies Germany · The Manifest
Is AI only realistic for the largest insurers?
No. Machine learning can seem reserved for companies like Google or Amazon, but we build solutions that bring value to insurance companies of every size, starting with the use cases your data can support today.
Can AI improve pricing for health or niche insurance?
Yes. For an insurer in the medical and health domain we built a neural network model on five years of historical medical data to estimate the risk of economic loss. It significantly outperformed statistical approaches, and the client used it to optimise pricing policies.
How does usage-based insurance work with AI?
A deep learning model analyses telematic data from the driver’s phone, detects whether the user is driving or a passenger, and tracks behaviour such as speeding, unnecessary acceleration and hard braking. Each trip gets a score, which feeds a personal discount and safe-driving recommendations.
Our IT infrastructure is not built for machine learning. Where do we start?
Many insurers are in the same position. We start with a use case assessment and data preparation, then build models that integrate with your existing systems, so infrastructure grows with proven value rather than ahead of it.
How do you make sure a model is reliable before it goes live?
We build validation frameworks that compare multiple approaches objectively, and we follow a project lifecycle in which each phase has goals and quality criteria that must be met before the next stage begins.
Start your project
Tell us what you want AI to do for your business
Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.