AI · Deep learning · Financial services
We turn financial data into working AI solutions
ALHARITM is a consulting and engineering team for financial services. We find where AI creates real value — then design, build and run the models, applications and data pipelines that deliver it.
- Risk scoring
- Fraud detection
- Document AI
- Forecasting
- LLM agents
Who we are
Consulting that ends in working software, not slides
Finance means strict rules, sensitive data and high stakes in every decision. We combine an understanding of financial products and processes with hands-on AI engineering, so strategy and implementation are handled by the same team.
Finance first
We start from your business process, regulation and risk appetite — and only then choose the technology.
End-to-end delivery
From data audit and prototype to production deployment, monitoring and training your team.
Responsible AI
Explainable models, data protection and human oversight are built in from day one.
Services
What we do
One partner for the whole path from an idea to an AI system running in production.
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AI strategy & consulting
We assess your processes and data, identify high-value use cases, estimate effort and risk, and build a practical roadmap.
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Machine learning & deep learning
Custom models for scoring, classification, forecasting and anomaly detection — from gradient boosting to neural networks.
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LLM assistants & AI agents
Assistants that answer from your internal documents, and agents that automate multi-step workflows across your systems — with guardrails and audit logs.
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Document AI & OCR
Extract and validate data from contracts, statements, IDs and invoices; classify documents and route them automatically.
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Data engineering & MLOps
Reliable data pipelines, feature stores, model deployment, monitoring and retraining — so models stay accurate in production.
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AI applications & integration
Web apps, dashboards and APIs that put AI into the hands of your staff and customers, integrated with your core systems.
Use cases
Where AI pays off in finance
Typical problems we solve. Every project is tailored to your data, processes and regulatory requirements.
Credit risk scoring
- Challenge
- Manual or rule-based credit decisions are slow, inconsistent and hard to adapt to new customer segments.
- Our approach
- We build scoring models on application, transaction and credit-bureau data, with an explanation for every decision and regular validation.
- What you get
- Faster, more consistent decisions with clear reasons that analysts and regulators can review.
Typical tools: LightGBM / XGBoost SHAP Python
Fraud & anomaly detection
- Challenge
- Fraud patterns change quickly, and static rules either miss new schemes or flag too many honest customers.
- Our approach
- We combine supervised models with anomaly detection and graph features, scoring transactions in real time alongside your existing rules.
- What you get
- Earlier detection of suspicious activity and fewer false alerts for your investigators.
Typical tools: Isolation Forest Graph features Streaming
Document & OCR automation
- Challenge
- Teams spend hours retyping data from passports, contracts, statements and invoices.
- Our approach
- OCR and document-understanding models extract fields, check them against business rules and send uncertain cases to a person.
- What you get
- Less manual data entry, fewer typing errors and a full audit trail for every document.
Typical tools: OCR Layout models Validation rules
Forecasting & planning
- Challenge
- Cash-flow, liquidity, demand and portfolio forecasts live in spreadsheets and quickly go out of date.
- Our approach
- Time-series and machine-learning models that update automatically with fresh data and show the uncertainty of each forecast.
- What you get
- Up-to-date forecasts with confidence ranges to support planning and limit setting.
Typical tools: Time series Gradient boosting Scenario analysis
LLM assistants & agents
- Challenge
- Staff and customers struggle to find answers in policies, product terms and internal knowledge.
- Our approach
- Retrieval-augmented assistants grounded in your own documents, and agents that draft, check and route requests — always with sources and access control.
- What you get
- Quick answers with sources and automated routine tasks, while people stay in control of important decisions.
Typical tools: RAG Vector search Open & commercial LLMs
Data pipelines & analytics
- Challenge
- Data is scattered across core banking, CRM, spreadsheets and external sources, so every report and model starts from scratch.
- Our approach
- We design a reliable data layer: ingestion, cleaning, quality checks and shared features for analytics and machine learning.
- What you get
- One trusted source of data that reports, dashboards and models can reuse.
Typical tools: ETL/ELT Data quality Feature store
Interactive demo
See machine learning in action
Two small models that run entirely in your browser. No data leaves your device.
Explainable credit scoring
Move the sliders to describe a borrower. A logistic-regression model estimates the probability of default and shows how each factor pushed the result up or down.
Logistic regression · 6 features
Why this result
raises risklowers risk
Transaction anomaly detection
Each dot is a card transaction, placed by time of day and amount. Click anywhere on the chart to add a transaction — a k-nearest-neighbours model scores how unusual it is.
k-NN anomaly detection · k = 7 Tip: try a large payment at 3 a.m.
Last transaction
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Illustrative models with simplified, synthetic data — for demonstration only, not for real decisions.
How AI works
Inside an AI pipeline
A production AI system is much more than a model. Select a stage to see what happens there.
Stage 1 / 6
Data
Collect data from core systems, files and APIs. Check quality, completeness and access rights.
Example: loan applications, repayment history and credit-bureau data.
Stage 2 / 6
Features
Turn raw records into signals a model can learn from — ratios, trends, aggregates, text and image embeddings.
Example: debt-to-income ratio, 90-day spending trend.
Stage 3 / 6
Model
Train and compare candidate models on historical data — from interpretable baselines to deep neural networks.
Example: gradient boosting vs. a neural network for default prediction.
Stage 4 / 6
Validation
Before anything goes live, test on unseen data for accuracy, stability, fairness and explainability.
Example: backtesting on last year's loans, bias checks across customer segments.
Stage 5 / 6
Deployment
Package the model as an API or app, integrate it with your systems, and add access control and logging.
Example: a scoring API called by the loan origination system.
Stage 6 / 6
Monitoring
Track data drift and model performance in production, alert on issues and retrain when needed.
Example: an alert when the applicant profile shifts after a new product launch.
How we work
From the first call to a running system
A clear, step-by-step process — you see results at every stage.
Discovery
A short workshop to understand your goals, processes, data and constraints.
Assessment & roadmap
We evaluate data and feasibility, prioritise use cases and agree on success criteria.
Prototype
A working proof of concept on your data, so you can judge the value before investing more.
Production
Secure deployment, integration with your systems, documentation and testing.
Support & growth
Monitoring, retraining and training your team so the solution keeps delivering.
Principles
How we build AI for finance
Security & privacy
Data stays within agreed boundaries; we can work on your infrastructure or in an isolated cloud environment.
Explainability
Every important prediction can be explained to a customer, an auditor or a regulator.
Human in the loop
AI supports decisions; people stay in control of the ones that matter.
Measurable value
We agree on metrics up front and measure results against your current process.
Technology
Modern tools, chosen for the task
We are not tied to any vendor — we pick what fits your environment, budget and requirements.
Machine learning
- Python
- scikit-learn
- XGBoost
- LightGBM
- PyTorch
- TensorFlow
Generative AI
- LLMs (open & commercial)
- RAG
- Vector databases
- AI agents
- Hugging Face
Data & MLOps
- SQL
- Apache Spark
- Airflow
- dbt
- MLflow
- Docker
- Kubernetes
Apps & integration
- FastAPI
- REST / GraphQL
- React
- Dashboards
- Cloud & on-premise
FAQ
Frequently asked questions
We are not sure where to start with AI. Can you help?
Yes. That is exactly what the discovery and assessment stages are for: we look at your processes and data and recommend where to start — or tell you openly when AI is not the right tool.
Do we need a lot of data?
It depends on the task. Some use cases work with modest historical data, others need more. We check what you have before proposing a solution.
Can the solution run on our own servers?
Yes. Depending on your security requirements, we can deploy on your infrastructure, in a private cloud or in a hybrid setup.
Which languages can your AI assistants work in?
We build assistants for Uzbek, Russian, English, Turkish and other languages, and test them on your real documents.
How long does a project take?
A prototype usually takes a few weeks; the production timeline depends on scope and integrations. We agree on the plan after the assessment.
Contact
Let's discuss your project
Tell us about your task — we will reply and suggest the next steps.
Or write to us directly