Fintech Automation

Automated KYC and AML onboarding pipelines triggered from application submission. Document collection uses configurable document requirement logic per product type and jurisdiction: consumer current account, SME business account, and investment product each have different regulatory document requirements under your applicable AML framework. Identity document verification uses OCR extraction (AWS Textract or Google Document AI) to read passport, driving licence, and national ID data, followed by liveness check and document authenticity verification via Onfido, Jumio, or Veriff depending on the geographic market and acceptable false positive rate.

Sanctions screening checks applicant name and address data against OFAC SDN, UK HMT, EU Consolidated, and UN Security Council consolidated lists via Dow Jones Risk and Compliance, LexisNexis WorldCompliance, or direct list integration. PEP (Politically Exposed Person) screening and adverse media checks run automatically as part of the same workflow. Credit bureau queries use Experian, Equifax, or TransUnion APIs for consumer applications, and Dun and Bradstreet or Experian Business for SME onboarding.

Straight-through approval processes low-risk profiles that meet all automated check thresholds without human review -- typically 60--80% of consumer applications in a well-calibrated system. High-risk indicators, incomplete documentation, match results above a configured threshold, or applicants at score boundaries are routed to a manual review queue with all automated check results pre-populated. Approved applicants trigger automated account setup in the core banking or lending system via API, completing the onboarding without any further manual steps. The FDX (Financial Data Exchange) API standard is used where open banking data enrichment is part of the onboarding affordability or identity verification flow.

Automated transaction processing pipelines that handle the full payment lifecycle from initiation to settlement. For ACH transactions, payment files are generated in NACHA ACH file format with correct Standard Entry Class (SEC) codes -- CCD for corporate credit/debit, PPD for consumer payments, CTX for corporate with addenda -- and submitted to the ODFI (Originating Depository Financial Institution) on the defined processing schedule. Return handling processes NACHA return codes (R01 through R85) automatically: R01 insufficient funds triggers the configurable retry schedule; R08 payment stopped triggers immediate escalation; R29 corporate customer not authorized triggers AML review. Effective settlement date calculations account for banking day calendars and Federal Reserve ACH processing windows.

For SWIFT payments, MT101 and MT103 message formats handle cross-border payment instructions, while MT940 and MT942 messages handle incoming statement and interim reporting for reconciliation. The transition to ISO 20022 MX message formats (pacs.008, pacs.002, camt.053) is handled in systems operating on or targeting the SWIFT network post-2025 migration. Bulk payment processing from file input (CSV, XML, SWIFT) or API validates each record against configurable rules -- account number format, IBAN checksum, payment limit thresholds -- enriches records with BIC lookup and routing information, and routes payments to the correct processing channel. Exception handling for failed, returned, or queried transactions routes to a resolution queue with the failure code, suggested remediation, and affected payment details. Integration with Plaid, Finicity, or MX for open banking account verification and micro-deposit confirmation uses the FDX API standard for standardized financial data access.

Automated reconciliation matching transactions across your general ledger, bank statement feeds, and payment processor settlement records. Bank statement data ingests via open banking APIs (Plaid, Finicity, or MX using FDX API standard for standardized financial data access), SWIFT MT940/MT942 electronic statements, or BAI2 file format for US bank statement feeds. Payment processor settlement data ingests via Stripe, Adyen, Braintree, or Worldpay APIs depending on your processor stack.

Three-way match reconciliation compares the internal ledger entry against the bank statement transaction and the payment processor settlement record, confirming that all three agree on amount, value date, and reference. Configurable matching rules handle the real-world complexity that makes reconciliation hard to do manually: timing differences where the bank posts on a different date than the internal booking, bulk settlement batches where the processor pays a net amount covering multiple individual transactions, partial payments against a multi-instalment receivable, and multi-currency transactions where the booked amount differs from the settlement amount due to FX rate application timing.

Unmatched items are categorized by exception type and presented with suggested resolution: likely duplicate entries, timing differences that will self-resolve at period end, or genuine breaks requiring investigation. Daily automated reconciliation runs complete overnight, generating a reconciliation status report with the matched count, unmatched count, and exception summary. Period-end reconciliation packs are generated automatically with all matched items documented, exceptions explained, and the reconciliation sign-off workflow for finance team approval. The reconciliation that previously consumed 2--5 days of finance team time per month runs overnight with human review focused on the exception list.

Automated assembly of regulatory submissions from your transaction, customer, and risk system data. For UK-regulated firms, FCA Gabriel and RegData submissions -- COREP capital adequacy reports, FINREP financial reporting, FIN-A annual financial returns, and product sales data reports -- are assembled automatically from your source systems against the FCA reporting template specifications. For MAS-regulated firms in Singapore, automated assembly of MAS Form 5 and risk-based returns. For Irish-regulated firms, CBI regulatory returns including balance sheet data, exposures, and product-level reporting.

AML transaction monitoring reports pull flagged transactions from your monitoring system, enrich them with customer risk profile data, and format them for Suspicious Activity Report (SAR) or Suspicious Transaction Report (STR) submission in the format required by your jurisdiction's Financial Intelligence Unit -- FinCEN for US, NCA for UK, FIU-IND for India. Annual AML program reports aggregate the transaction monitoring trigger rates, SAR filing counts, and training completion data required by your AML policy.

Data extraction from source systems uses documented API connections to your core banking platform, transaction ledger, and CRM. Transformation logic maps internal data fields to the regulatory template's required format, applying the calculation methodologies specified in the regulatory guidance. Validation runs against the template's business rules before submission to catch calculation errors and missing data. Submission tracking and deadline management maintains a regulatory calendar with status tracking per return. SOC 2 Type II controls for data integrity and access management are applied to the reporting pipeline to satisfy audit requirements that regulatory data was produced from authoritative sources without unauthorized modification.

AI-powered fraud scoring integrated into your transaction processing and onboarding workflows. Fraud detection models are trained on your historical fraud patterns -- not generic industry models that do not reflect your customer base and transaction profile. Feature engineering extracts behavioral signals from the transaction stream: velocity metrics (number of transactions per hour per account, daily spend velocity against the account's historical baseline), device fingerprinting signals, geographic anomaly indicators (transaction location inconsistent with the account's recent history), and network graph features (shared device IDs or phone numbers across multiple accounts are a strong application fraud signal).

Real-time scoring runs on each transaction at the point of authorization using a low-latency inference endpoint (sub-100ms p99 latency target for payment authorization flows). Batch scoring runs overnight on account-level behavioral patterns for account takeover detection and mule account identification. Anomaly detection uses isolation forest and autoencoder models for unsupervised detection of patterns that deviate from the account's established behavior without requiring labeled fraud examples -- particularly useful for detecting novel fraud patterns before labeled examples accumulate. Risk scores are delivered alongside the transaction data to your operations and fraud team dashboard; automated blocking applies for scores above a high-confidence threshold; human review queues handle borderline cases with the model's feature contribution explanation surfaced alongside the transaction context. PCI DSS SAQ-A controls for card tokenization ensure cardholder data is never stored in plain text in the fraud detection pipeline -- only tokenized references that cannot be reverse-engineered to card numbers if the fraud scoring database were compromised.

Automated credit decisioning pipelines for consumer and SME lending -- from application submission to approved disbursement without manual processing for standard cases. Application data validation checks completeness, format consistency, and cross-field logic before any downstream credit queries are triggered. Credit bureau queries use Experian, Equifax, or TransUnion APIs for consumer applications; credit score, tradeline data, payment history, and public records are ingested and mapped to the scorecard variables. Open banking account data via Plaid, Finicity, or MX (FDX API standard) supplements bureau data for thin-file applicants and provides real-time income verification from account transaction history -- AWS Textract OCR handles bank statement PDF uploads for applicants who cannot provide open banking consent.

Affordability assessment applies your underwriting criteria: debt-to-income ratio against the validated income figure, existing credit obligations from the bureau tradelines, and stress-tested repayment capacity at the offered rate and term. The decision engine applies your scorecard and policy rules to produce an approved, declined, or refer outcome. Approved applications trigger automated disbursement initiation via ACH (NACHA formatted file) or wire transfer. Declined applications generate compliant adverse action notices with the required FCRA reason codes.

Collections workflow automation triggers based on payment status from the servicing system: first missed payment triggers a reminder sequence via SMS and email; second missed payment triggers a cure letter and collections agent assignment; third missed payment triggers the escalation workflow. All collections communications are compliant with FDCPA requirements -- timing restrictions, required disclosures, and opt-out handling are built into the workflow. The result is a lending operations layer that processes standard applications end-to-end without manual handling, scales application volume without proportional headcount growth, and routes the exception cases that genuinely need human credit judgment.