Better risk decisions
Scoring models predict the probability of default and flag unusual payment behaviour, so you accept the right customers and catch fraud earlier.
Industries · Finance and banking
Artificial Intelligence in Finance: faster decisions, lower risk
Banks, lenders and investment firms use machine learning for personalised customer service, risk management, fraud detection, anti-money laundering and regulatory compliance. We build the models behind these processes, from underwriting data to explainable decisions, across capital markets, commercial banking and personal finance.
- Credit decisions in a fraction of a minute
- Risk and fraud models trained on your own data
- Explanations for every model decision
- 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 in finance
AI helps financial organisations improve customer experience, reduce costs and grow revenue.
Routine work automated
Machine learning takes over routine tasks such as underwriting checks and data entry, raising process productivity and cutting decision times.
Offers that fit each customer
Data on customer preferences and behaviour lets you deliver tailored messages, products and offers that improve loyalty and reduce churn.
What we deliver
AI solutions for financial services
The use cases we see most often in banking, lending and investment, and the expertise we bring to each.
- 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
Fraud detection
Contactless and in-app payments raise transaction volumes and the risk of fraud. Machine learning models detect fraudulent activity across these payment flows.
- 03
Pricing policy and KPI optimisation
Take on more customers and more risk, or stay risk-averse and protect profitability? Optimisation algorithms answer these questions for underwriting, finance and marketing.
- 04
Interpretable AI decisions
Modern models use many variables and are hard to read. We provide tools that explain model decisions across the whole population or for an individual customer.
- 05
Behavioural analysis
Models that segment customers by behaviour and risk group, so you understand their patterns and can take the right action for each segment.
- 06
Data enrichment and fusion
We collect, fuse and streamline heterogeneous data sources into your AI applications to raise predictive power, for example with geospatial risk indices for districts and regions.
- 07
Personalised customer experience
Analytics on customer preferences and behaviour, including social media data, to design tailored offers, service packages and loyalty measures.
- 08
Chatbots and conversational assistants
Banking chatbots, conversational assistants and robo-advisors that answer customers quickly and save time for your service teams.
- 09
NLP for deal sourcing
Semantic search and clustering of company descriptions help private equity and venture capital analysts explore niche markets and find relevant companies.
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
Finance · Machine Learning
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.
Read the case study
Semantic company search for private equity deal sourcing
1 week of analyst work saved per new niche market
An end-to-end NLP solution that lets private equity funds and venture capital firms find and cluster relevant companies by meaning rather than by industry code or keyword.
Social media analytics for a bank’s marketing activities
Lower churn most at-risk clients retained, per client feedback
An interactive tool that analyses social media data to reveal audience interests, social group affiliation and demographics, so a bank can target offers that build loyalty and reduce churn.
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.
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. Sergey Sukhanov Chief Data Scientist PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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Dr. Ivan Tankoyeu Chief Data Officer 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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Sergio Frayle, M.Sc. Lead Data Scientist Master’s in AI, Pattern Recognition and Digital Imaging; 7+ years in image processing and computer vision, with publications.
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Enrique Fernández, M.Sc. Senior ML Expert Master’s in Robotics and Mechatronics; builds and deploys complex AI solutions, particularly in computer vision and NLP.
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Peter Luck, M.Sc. AI Consultant Master’s in Business Engineering and Computer Science; product and project manager for machine-learning products.
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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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
Where does AI deliver value in finance?
Most often in personalised customer service, risk management, fraud detection, anti-money laundering and regulatory compliance. These apply across capital markets, commercial banking and personal finance.
Can a machine learning model make credit decisions?
Yes. For an SME lender we built a model that predicts the probability of borrower default using more than 800 features from 14 data sources. It outperformed expert underwriters, improved loan portfolio quality at the same acceptance rate, and cut decision time from hours to a fraction of a minute.
How do we explain AI decisions to customers and regulators?
We provide interpretability tools that explain a model’s reasoning, either across the whole customer population or for a single customer. This makes complex models usable where decisions must be justified.
Our data is spread across many systems. Is that a problem?
No. Representing heterogeneous data as ready-to-use machine learning data sets is a core part of our work. Our data enrichment and fusion modules combine sources such as transactions, time series and geospatial data to improve model accuracy.
How do you run an AI project in a financial organisation?
We follow an AI project lifecycle adapted from software development standards that also accounts for the scientific uncertainty of machine learning. Each phase has its own 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.
Prefer to pick a time yourself?
Schedule a call