Industries
AI for Finance & Fintech
AIYatch builds AI for banks, fintechs, lenders and investment platforms: fraud and anomaly detection, forecasting, document extraction for KYC and invoices, and compliant customer assistants. We prioritise explainable models, complete audit trails and controlled deployment, so risk, compliance and engineering teams can each sign off on what goes into production.
Typical challenges
- Fraud and money-laundering patterns change faster than rule-based systems can be updated.
- KYC, onboarding and reconciliation still depend on manual review of documents and spreadsheets.
- Regulators expect explainability, audit trails and model governance that generic AI tools do not provide.
- Customer support volumes spike around statements, payments and outages, and every answer must be accurate.
Typical use cases
Fraud and anomaly detection
Supervised and unsupervised models over transactions and behaviour that score risk in real time and route suspicious activity to analysts with a reason for the flag.
KYC and document extraction
OCR and LLM pipelines that read IDs, bank statements, invoices and contracts, validate fields against rules and push clean data into your core systems.
Cash-flow and market forecasting
Time-series models for liquidity planning, revenue forecasting and portfolio analytics, delivered with dashboards and confidence ranges.
Compliant customer assistants
Chat and voice agents that handle balances, disputes and product questions with retrieval over approved content and strict guardrails on financial advice.
Automated reconciliation and reporting
AI agents that match records across ledgers and statements, explain exceptions and draft period-end reports for finance teams to review.
Credit and churn scoring
Predictive models that estimate default or attrition risk with feature-level explanations that credit and retention teams can act on.
Questions from this sector
We favour models and techniques that expose feature contributions, log every prediction with its inputs, and produce plain-language reasons for flags or scores. Where an LLM is involved, responses are grounded in retrieved, versioned sources. Model cards and monitoring dashboards give risk and compliance teams a clear view of behaviour over time.
Both are possible, but we recommend starting with recommend-and-approve. Agents draft the reconciliation, dispute response or report and a person confirms. Once accuracy is proven on live data, low-risk actions can be automated with thresholds, limits and full audit logging.
Historical transactions with labels for confirmed fraud or chargebacks, plus account and device metadata, are ideal. If labels are sparse we begin with anomaly detection and build labelled data with your analysts as part of the engagement. Data can stay in your environment throughout; we work through secure access rather than exports.
Related work
Outcomes, not demos.
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