AI for Fintech and Banking

Classification models trained on your historical applicant data and loan outcomes. Inputs include bureau data (FICO, VantageScore, tradeline history, utilisation), bank transaction features derived from open banking or direct statement ingestion, income verification signals, and alternative data where bureau coverage is thin, utility payment history, rent payment records, mobile money transaction patterns for emerging market portfolios.

The model architecture is typically a gradient boosting classifier (XGBoost or LightGBM) with SHAP (SHapley Additive exPlanations) values computed per prediction so your underwriting team can audit the contributing factors for each application decision. For regulated lenders, we build the explainability layer so adverse action notices can reference the specific model inputs that drove a decline, satisfying ECOA and FCRA requirements. Population Stability Index (PSI) monitoring is configured from day one to detect when the incoming applicant population has shifted away from the training distribution, triggering a model review before accuracy degrades. Scores arrive in seconds, not days. Model accuracy is measured against your current approval and default rates before deployment. Configurable score thresholds by product type and risk appetite.

Transaction-level classification models that evaluate each payment against velocity signals, device fingerprint, merchant category, location, and behavioural history. Outputs a fraud probability score in milliseconds. Transactions above threshold are flagged or declined before settlement. The model is trained on your labelled transaction history and retrained on a schedule as new confirmed fraud cases accumulate. Reduces fraud losses without increasing false decline rates on legitimate transactions.

AI that reads bank statements, pay stubs, tax returns, and ID documents and extracts structured fields automatically. Classifies individual bank transactions by category, salary credits, rent, loan repayments, to give underwriters additional income and liability signals beyond headline balances. Handles scanned paper, PDF, and mobile-camera images. Low-confidence extractions are flagged for manual review. Eliminates manual re-keying and reduces document processing time per application.

Conversational AI for customer-facing support on balance queries, transaction disputes, loan status, and account management. The system is built on a retrieval-augmented generation (RAG) architecture: a vector database (Pinecone, Weaviate, or pgvector) indexes your product documentation, FAQ content, terms and conditions, and historical support transcripts. At query time, the system retrieves the relevant passages and generates a grounded response rather than hallucinating product details from general model knowledge. This makes the output auditable, every response traces back to a source document.

Intent classification handles the routing decision: queries the model can resolve with high confidence are answered automatically; queries involving account security, disputed amounts above a defined threshold, or regulatory matters are escalated to a human agent with the full conversation context passed so the agent does not ask the customer to repeat themselves. Integrates with your existing CRM and ticketing system via webhook or API (Salesforce, Zendesk, Freshdesk, or custom platforms). Sentiment scoring on each conversation flags frustrated customers for priority routing before they abandon or escalate. Reduces cost-per-contact without reducing resolution quality on standard query types.

AI that extracts, classifies, and aggregates the transaction and position data required for regulatory reports, CTRs (Currency Transaction Reports), SARs (Suspicious Activity Reports), capital adequacy reports (Basel III/IV ICAAP), and liquidity reports (LCR, NSFR), from your core banking or ledger system.

The extraction layer uses a combination of rule-based classification for well-structured transaction records and LLM-assisted extraction for unstructured narrative fields and exception notes. Data transformation pipelines are built with Apache Airflow or dbt, producing a documented lineage trail from source system record to report field that satisfies audit requirements. Automated reconciliation checks compare extracted totals against general ledger balances before the report is finalised, catching discrepancies that would otherwise require manual investigation after submission. Reduces the manual data preparation time your compliance team spends before each reporting cycle. Data quality issues in the source data are flagged pre-report rather than discovered during a regulator review. Produces audit-ready outputs with data lineage documentation so your team can trace any reported figure back to the originating transactions. Works with your existing report submission tooling and regulatory filing systems.

Unsupervised and supervised models that learn each customer's normal transaction behaviour and surface deviations for compliance review. Network analysis that maps fund flow between accounts to detect layering and structuring patterns. Produces a prioritised alert queue ranked by anomaly severity, so your compliance team reviews the most likely cases first rather than working through uniform rule-based alert volumes. Reduces false positive rate compared to rule-only monitoring while maintaining detection coverage on genuine suspicious activity.