AI Chatbot Development for Banks and Financial Organizations

Imagine: a bank customer loses their card at 3 AM. They call support—IVR doesn't recognize the issue. An operator picks up after 15 minutes. The customer panics. Our AI bot solves this in 20 seconds. It recognizes intent from the first phrase, initiates multi-factor authentication via phone number, and blocks the card. All actions are logged in an immutable audit trail compliant with 152-FZ and 161-FZ. We have built dozens of such solutions for banks with assets over $1 billion. Guaranteeing compliance from day one.

A typical problem: bank chatbots on rule-based engines handle only 30% of requests without operator intervention. The remaining 70% are repetitive actions with context loss. Our AI bot, based on LLM, understands conversation context, retains history, and does not require re-entry of data. This yields a self-service rate of 75%—2.5 times higher than rule-based solutions—and reduces Customer Effort Score by 30%. On average, banks reduce operational costs by 35% after deployment.

Secure handling of financial transactions

The AI bot operates in a zone of maximum responsibility: financial operations, personal data, regulatory requirements. Architectural decisions are dictated by compliance, not just UX. The bot never requests CVV or the full card number in plain text; all sensitive operations require additional authentication.

Regulatory context: all operations comply with 152-FZ, 161-FZ, AML/CFT, and CBR requirements. Each dialogue is logged in an immutable audit trail with 5-year retention.

Functional blocks

• Informational block (no authentication): Rates, currency exchange, branch addresses, product terms (deposits, loans, cards), system statuses.
• Operational block (after authentication): Balance and statements, transaction history with filtering, card top-up, transfer, card locking/unlocking, product applications (loan, deposit, card).
• Complex scenarios: Transaction dispute (chargeback initiation), changing product parameters, closing an account/card.

Multi-level authentication

Identification via card number/phone (partial) + SMS OTP; operation authorization via push notification in mobile app or SMS with confirmation code; large operations (> X RUB) require voice confirmation or biometrics. Any money action goes through the banking core API with independent authorization. The bot never stores payment data.

Compliance and regulatory reporting

Each dialogue is logged in an immutable audit trail with timestamp and user ID. Data is stored for at least 5 years. Records are accessible to the regulator upon request. AI decisions are explicitly marked in the transaction history.

Anti-fraud integration

Unusual requests through the bot are flagged: balance inquiry from non-standard geolocation, multiple failed authentication attempts, transaction at unusual time/amount. If flagged, additional verification or operation denial. This prevents up to 90% of fraud attempts.

Personalization of financial offers

The bot analyzes client profile: turnover, balances, product history, life events. Offers are relevant (e.g., "You regularly transfer rent—would you like to open a savings account for a mortgage down payment?") and never pushy. Clients can opt out with one command.

Deployment timeline

What is included in the work

• Development of intent model and NLP pipeline
• Integration with banking API
• Configuration of anti-fraud and authentication modules
• Training on historical dialogues (10,000+ records)
• Load testing (10,000 RPS)
• Documentation for the regulator
• Staff training
• 3 months of post-launch support

Why choose us

With 5+ years of experience in fintech and 20+ completed projects for banks with assets over $1 billion, we deliver turnkey AI chatbot solutions. Write to us for a free project assessment. The cost includes development, integration, anti-fraud setup, regulatory documentation, staff training, and 3 months of support. We deliver a turnkey solution in 8–10 months.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.