Banking & finance
Credit, fraud and compliance decisions in a bank rest on transactions, documents and customer records. AI can make each of these decisions faster and more consistent. Before it goes into use, a model has to be explainable, pass the bank's validation and run inside the bank.
Challenges
Customer data stays inside the bank
Customer records, transactions and identity documents are covered by banking secrecy and data protection law. Models usually have to be trained and run inside the bank.
Models have to be explainable
Before a model is used, the bank's validation team and auditors need to understand what drives its decisions. Each model comes with the documentation and tests that review requires.
Performance has to be monitored
Customer behavior and fraud methods change over time, and a model left unchecked slowly gets worse. Its scores are compared with actual outcomes as they come in.
Typical problems
Credit risk and scoring
Models that estimate credit risk from customer, transaction and financial statement data, tested side by side with the scorecards already in use.
Fraud detection and transaction monitoring
Spotting unusual transactions and ranking alerts so that investigators see the most important cases first.
Customer onboarding and KYC
Reading identity documents, company papers and forms, and flagging missing or inconsistent information before a person reviews the file.
Customer requests and complaints
Classifying requests that arrive through branches, the call center and digital channels, routing them to the right team and drafting replies for staff to review.
Access to regulation and procedures
Assistants that search regulations, circulars and internal procedures, and give each answer together with the current text it is based on.
Our research areas
See all research areasInterpretability & evaluation
Explaining what drives each credit or fraud score, and tracking performance against real outcomes.
Learning from limited data
Learning from a small number of confirmed fraud cases and from outcomes that become clear only later.
Foundation models
Open-weight language models adapted to Turkish banking terminology and documents.
Working with sensitive data
Training and running models on customer data without the data leaving the bank.
Related services
All services- Anomaly & fraud detectionSystems that flag the unusual transactions, failures and faults worth a closer look, even with few confirmed examples.
- Multimodal AIApplications that combine text, images, audio and documents to complete one defined task.
- AI assistantsAssistants that answer from your own documents and systems, with sources and access control.
- AI agentsAI agents that carry out defined tasks in your systems, within permissions and approval steps you control.
- LLM fine-tuningLanguage models trained on your examples to handle a task, format or writing style they keep getting wrong.
- LLM deploymentLanguage models running on your own servers or private cloud, sized and secured for your workload.
Work with us
Work usually starts with one model or one process, and the system is installed in the bank's own environment with the validation team involved from the start. Once the results are measured, the scope can extend to other products and units.
R&D projects
For problems with no proven solution yet. We test approaches on your data and work toward a prototype in agreed milestones. At each milestone we tell you plainly what looks feasible and what doesn't.
Software development & consulting
For problems with known solutions. We build production software on proven methods, or advise your team on architecture, tooling and model choice.
Funded research
Joint R&D projects backed by national and international research funding, with industry and academic partners.
Corporate training
Training programs tailored to your team. Topics include everyday work with AI assistants, product development, integration and strategy.
Frequently asked questions
Does customer data leave the bank?
No. Models run on the bank's own servers or private cloud, and customer data stays inside the bank. Personal data is processed in line with KVKK and, where relevant, GDPR.
Does AI make the credit decision?
No. The model produces a score or a warning together with what it is based on. The decision is made by the bank's staff under the bank's credit policy.
Can an AI model pass the bank's model validation?
It is built with that review in mind. The documentation and tests the validation team needs are delivered with the model.
Where should a bank start with AI?
Usually with a process that has plenty of data and results that are easy to measure, such as an existing scoring model or a common type of customer request.
Who runs the system after handover?
The bank's own teams. The system is handed over with its documentation and monitoring checks, and we can also provide support after that if needed.
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