Purpose-Built for Fintech Reconciliation

Every feature designed to save your finance team hours of manual work

95%+ Match Rate, Out of the Box

Our hybrid matching engine combines exact matching, fuzzy algorithms, and ML embeddings. Most transactions match automatically. Your team only reviews the exceptions.

Handles typos, date mismatches, formatting differences

Learns from your corrections

Processes 1M transactions in minutes

Confidence scoring for every match

See Matching in Action
Dashboard Preview
$1,247.50 → $1,247.50
99%
Subscription renewal - Acme Corp
txn_3h2k9s... Jan 31, 2025 14:23 UTC
$450.00 + $250.00 → $700.00
92%
Split payment - Product bundle
txn_8j4k2p... Jan 30, 2025 11:47 UTC
$1,050.00 → $1,028.55
74%
Professional services - GlobalTech Inc
txn_9m3n7q... Jan 29, 2025 09:12 UTC
Matches transactions with fees, splits, and timing differences
Schema Detection in Action
Raw CSV Headers:
amount, created, customer_email, fee, net
Detected Schema:
"amount" → Transaction Amount
"created" → Transaction Date
"fee" → Processing Fee
Analyzed in 2.3 seconds • 99.8% confidence

Upload Any CSV. We Figure It Out.

Stop manually mapping columns. Our LLM detects transaction IDs, amounts, dates, descriptions—even in messy formats. No configuration needed.

Works with Payment Processors, Bank statements, Custom formats, Raw Data Formats

Detects currencies, date formats automatically

Handles multi-currency transactions

Suggests column mappings with 95%+ accuracy

Try Schema Detection

Reconcile Every Product Line Separately

Running payments, refunds, payouts, and chargebacks? Create separate Spaces for each. Keep data isolated, reports clean, and teams focused.

Unlimited spaces per account

Per-product audit trails

Role-based access per space

Independent reconciliation workflows

Learn About Spaces
Organize by Spaces
Payments Active
1.2M transactions reconciled
Refunds Active
45K transactions reconciled
Payouts Active
892K transactions reconciled
Each space with independent data and workflows
Match Accuracy Over Time
100% 95% 90% 85% 80% 75% 70%
Current
(Manual)
80%
Manual: 80%
Month 1
90%
90% accuracy
Month 2
93%
93% accuracy
Month 3
95%
95% accuracy
+15% improvement from manual to Month 3
Accuracy improves 2-5% over first 90 days

Gets Smarter Every Day

Every correction you make teaches the system. Historical learning means matching accuracy improves continuously without manual retraining.

Learns from your match approvals/rejections

Adapts to your business logic automatically

No ML expertise required

Accuracy improves 2-5% over first 90 days

See How It Works

Compliance by Design

Every match decision logged. Explainable AI shows why transactions matched. Export audit reports in one click. Built for regulated financial institutions.

Immutable audit logs

Match explanations for transparency

User action tracking

One-click audit report export

Learn More
AUDIT LOG ENTRY #4,573
Timestamp: 2025-01-31 14:23:47 UTC
User: sarah.chen@acmecorp.com
Action: APPROVED MATCH
Transaction: $1,500.00 → $1,455.00
Confidence: 73%
Algorithm: Fuzzy + ML
Reviewer Notes:
"Verified $45 fee matches Stripe's 3% rate. Date within settlement window. Approved."
Python SDK Example
import kosha

client = kosha.Client(api_key='...')

# Upload transactions
result = client.upload_csv(
    'transactions.csv'
)

# Get matches
matches = client.get_matches(
    confidence_min=0.90
)

# Export results
client.export_csv(
    workspace_id='payments',
    format='quickbooks'
)

Built for Developers

RESTful API for custom integrations. Webhooks for real-time notifications. SDKs in Python, Node.js, Ruby. Built by developers, for developers.

Complete REST API documentation

Webhook support for events

SDKs in popular languages

Rate limits: 1000 req/min (Enterprise)

Test sandbox environment

Explore API Docs

See Kosha in Action

Book a personalized demo with sample data from your business

Schedule Demo