AI for Fintech and Banking

A credit risk scoring model takes structured inputs about a loan applicant and outputs a probability of default. The inputs can include traditional bureau data -- credit score, payment history, utilisation, derogatory marks -- combined with alternative data you have access to: bank transaction history, income verification documents, employment records, and behavioural signals from your application flow. The model is trained on your historical loan data: applications that were approved, repaid, and defaulted. It learns which combinations of applicant features correlate with repayment behaviour in your specific lending segment and product. Output is a numeric score and the contributing factors, so your underwriting team can understand why a score is high or low. For markets where bureau data is thin, we build models that weight alternative data signals more heavily. We assess what data you have in discovery and tell you what accuracy improvement is realistic before we start building.

Real-time fraud detection for payment processing is a classification model that evaluates each transaction against a set of features and outputs a fraud probability score in milliseconds. Features include transaction amount, merchant category, location, device fingerprint, time of day, velocity signals (how many transactions in the last 10 minutes), and behavioural patterns derived from the cardholder's historical activity. The model scores each transaction as it arrives. Transactions above a threshold trigger a hold or decline. Transactions in a middle band may trigger a step-up authentication request. The model is trained on your historical transaction data labelled with fraud outcomes. It learns the specific fraud patterns on your platform rather than applying generic rules. Because fraud patterns evolve, the model is retrained on a schedule as new labelled fraud data accumulates. We also build the feedback loop: disputes and confirmed fraud cases feed back into training data to keep the model current.

Loan origination document extraction takes unstructured documents -- bank statements, pay stubs, tax returns, ID documents, and utility bills -- and extracts structured data fields from them automatically. The AI reads the document, identifies the relevant fields (account holder name, monthly income, account balance, employer name, employment dates), and outputs structured data into your loan origination system. For bank statements, the model also classifies individual transactions by category -- salary credits, rent payments, loan repayments, gambling transactions -- which gives underwriters additional signal beyond the headline numbers. The model handles a range of document formats and layouts, including scanned paper documents and photos taken on a mobile phone. It flags documents where confidence is low for manual review rather than silently producing incorrect extractions. We assess your document types and origination workflow in discovery to determine the integration point and accuracy requirements.

Standard AML transaction monitoring uses rules: alert when a cash deposit exceeds a threshold, alert when transactions occur in high-risk jurisdictions, alert when structuring patterns appear. Rules catch known patterns but generate large volumes of false positives because they cannot account for customer context. A business that regularly transacts in multiple jurisdictions looks suspicious to a rules engine but is normal behaviour for that customer. AML anomaly detection uses unsupervised and supervised ML models that learn each customer's normal transaction behaviour and flag deviations from that baseline. A transaction that is unusual for this specific customer surfaces, even if it doesn't trigger a rule. Combined with network analysis that maps transaction flows between accounts, the model can surface layering and structuring patterns that are invisible to rule-based systems. Output is a prioritised alert queue with the contributing signals, so your compliance team spends time on the most likely cases rather than reviewing hundreds of low-quality rule alerts.