Industries We Serve

Fintech

Smarter Reconciliations & Fraud Prevention

Fintech operations demand real-time accuracy, especially in reconciliation, compliance, and anomaly detection. At Galific, we build custom ML solutions that eliminate manual errors, accelerate reporting, and unlock new efficiencies in financial operations.

Solutions We Offer

Key Outcomes

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  • 01 80% reduction in invoice processing time
  • 02 Instant reconciliation across multiple financial sources
  • 03 Increased fraud detection accuracy
  • 04 Simplified audits and financial compliance

Use Cases

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  • 01 Automating account reconciliations daily across banks
  • 02 Flagging unusual refund spikes for e-commerce platforms
  • 03 Parsing and categorizing financial documents
  • 04 Generating compliance-ready financial summaries for audits

General FAQs

Everything you need to know about the service and how it works. Can’t find an answer? Mail us at info@galific.com

  • Can your models integrate with our existing ERP or accounting software? ⌄
    Yes. Our ML systems are built to integrate with popular ERPs, CRMs, payment gateways, and custom finance tools through APIs and direct connectors. We work inside your existing stack so you do not have to rip out the tools your finance team already relies on.
  • How fast can reconciliation happen with AI? ⌄
    Most clients achieve real-time or near-instant reconciliation, reducing manual effort by over 80 percent. Transactions are matched across banks, ledgers, and gateways automatically, and only genuine exceptions are flagged for your team to review. See our dedicated ML-powered reconciliation service for how this works end to end.
  • Is my data secure? ⌄
    Yes. We follow enterprise-grade encryption and GDPR-compliant practices, keep data in your region where required, and offer on-premise or cloud deployment. You retain ownership of your financial data, and we sign data-protection agreements before any work begins.
  • How does AI improve fraud and anomaly detection in finance? ⌄
    Our models learn the normal patterns in your transactions, refunds, and spending, then flag the outliers that rule-based checks miss, such as unusual refund spikes or out-of-pattern payments. Because the model adapts to your data, detection accuracy improves over time and false alarms drop, so your team spends less time chasing noise.
  • Can you help with regulatory compliance and audit reporting? ⌄
    Yes. We build compliance-ready reporting dashboards and audit trails, so financial summaries and reconciliation records are ready when auditors or regulators ask. Clear confidence scores and logs for every matched transaction make audits and due diligence far less stressful.
  • What data do we need to provide to get started? ⌄
    Typically historical transaction records, bank statements, ledger or ERP exports, and any refund or payout data relevant to the problem. If your data is fragmented across systems or messy, we handle the cleaning, mapping, and feature engineering as part of the build, so you do not need to prepare it perfectly first.
  • How long does a finance AI project take to deploy? ⌄
    A focused solution such as automated reconciliation or anomaly detection typically takes about 4 to 8 weeks from data review to a working deployment. Larger, multi-system builds take longer and are rolled out in phases. We confirm the timeline after an initial data and feasibility assessment.
  • How much does a finance or fintech AI solution cost? ⌄
    Cost depends on scope, data readiness, and how many systems you integrate, so we scope and price after reviewing your setup rather than quote a blind range. We often start with a contained pilot, for example reconciliation for one entity, so you can prove ROI before scaling across the business.
  • What other finance use cases can you build beyond reconciliation? ⌄
    Credit risk modeling, customer churn and lifetime-value prediction, document parsing and intelligent data extraction, and forecasting for revenue and cash flow. These build on our predictive analytics and custom ML capabilities, tuned to financial data and controls.