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Industries
AI training and consulting for banking and finance
Banking is where AI creates the most value and faces the strictest oversight. From credit risk to enterprise GenAI assistants, we train your teams, validate your models and build solutions that keep data in-house.
Banks and financial institutions have used scoring and risk models for decades. With generative AI, use cases have expanded from contact centres to regulatory analysis, investor relations and internal audit.
But the industry has three particular constraints: customer data must stay in-country and under banking secrecy, models must be independently validated, and decisions affecting people must be explainable. We design our programmes around these constraints from the start.
Key AI use cases
Credit risk and scoring
Application and behavioural scoring, early warning and non-performing loan (NPL) analytics.
Fraud detection
Anomaly detection in card, payment and account activity; moving from rule-based systems to machine learning.
Enterprise GenAI assistants
Employee support assistants, regulation and procedure search, document intelligence, all on premises without data leaving.
Model risk and validation
Independent model validation, performance monitoring, explainability and documentation.
Customer analytics
Segmentation, product recommendation and churn prediction.
Operational efficiency
Document classification, information extraction and automation of repetitive back-office work.
Regulation and risks
- Turkish banking regulator (BDDK): The regulation on banks' information systems and electronic banking services requires primary and secondary systems to be located in Türkiye, which in most scenarios restricts using public cloud LLM services abroad with customer data.
- EU AI Act: Creditworthiness assessment and credit scoring of natural persons, and risk assessment and pricing in life and health insurance, are high-risk; financial fraud detection is excluded. High-risk obligations apply from 2 December 2027.
- Turkish data protection law (KVKK): Cross-border transfer rules and the authority's generative AI guide determine how customer data may be processed in AI tools.
- Türkiye AI Action Plan (2026–2030): It places no direct obligations on companies, but regulators in sectors such as finance are expected to take aligned steps.
Our experience: Credit risk, scoring, NPL analytics, fraud detection and enterprise GenAI assistants. Our founder Çağrı Aksu has led credit risk, scoring, NPL and fraud detection projects in banking and finance, and led the development of enterprise GenAI products such as document intelligence, an investor assistant and an employee support assistant in financial technology.
Recommended training
Programmes we recommend for the roles in your organisation. Every course is tailored with examples from your industry and delivered in person or online.
Responsible AI, Regulation & Data Security
The EU AI Act, data protection, model risk and privacy for risk, compliance, internal control and legal teams.
Learn more →Using Generative AI Safely
Safe-use rules that protect customer data, for every employee in branches and head office.
Learn more →Generative AI at Work
Generative AI with banking examples for credit, operations, treasury and HR teams.
Learn more →Applied Machine Learning
Scoring and classification models and model evaluation for risk modelling and analytics teams.
Learn more →Building LLM Applications
RAG, agents and on-premise LLM deployment for software teams.
Learn more →Consulting and development services
Model Risk & Model Validation
Independent validation of models, especially credit scoring and risk models, with performance monitoring and explainability.
Learn more →Generative AI & LLM Solutions
Enterprise assistants, AI agents, document intelligence and knowledge retrieval systems, in the cloud or on-premise, without your data leaving your walls.
Learn more →On-Premise AI Infrastructure
GPU infrastructure and LLM deployment for organisations that cannot move data off-site.
Learn more →AI Governance & Compliance
AI inventory, risk classification, policies and processes; readiness for the EU AI Act, data protection law and ISO/IEC 42001.
Learn more →Free assessment · 3 min
Start now EU AI Act: Am I in Scope?
Is your AI system in scope of the EU AI Act, which risk category does it fall into, and which obligations apply from when?
Frequently Asked Questions
Can banks use general AI tools such as ChatGPT?
Usually not for scenarios involving customer data. Banking information systems rules, data protection transfer rules and banking secrecy impose serious constraints. Limited use with public or anonymised data, or models running on premises, can be considered.
Are credit scoring models high-risk under the EU AI Act?
Yes. Creditworthiness assessment and credit scoring of natural persons are high-risk, with obligations applying from 2 December 2027. Systems used to detect financial fraud are excluded.
How is AI training tailored for banking?
Courses use examples from banking processes such as credit, operations, risk and customer service, and take banking and data protection constraints into account. Separate content is designed for management, risk and compliance, business units and technical teams.
What does model validation cover?
An independent review of a model's data quality, discriminatory power, calibration, stability and explainability, reporting of findings and setting up monitoring processes.
Related articles
GenAI in Banking: On-Premise or Cloud?
Banks adopting GenAI must balance data localisation, security, model quality and cost. A comparison of on-premise, cloud and hybrid approaches, with a focus on Türkiye.
Read more →How to Validate Credit Scoring Models: A Practical Guide
Validating a credit scoring model is more than checking the Gini coefficient. Conceptual soundness, discriminatory power, calibration, stability, explainability and monitoring.
Read more →Machine Learning for Fraud Detection: From Rules to Models
Rule-based fraud systems can't keep up with new schemes. How machine learning improves fraud detection: approaches, imbalanced data, the right metrics and hybrid architectures.
Read more →Generative AI for Finance and Accounting Teams: Use Cases and Risks
Finance teams can use generative AI for Excel, reports and regulatory summaries, but wrong numbers and confidential data are real risks. Uses and controls.
Read more →Let's build a programme for your industry
Tell us about your needs, use cases and regulatory requirements, and we'll put together the right training and consulting plan.