Alfred: AI Reconciliation
Agent for Fintechs
Stop managing exceptions manually. Alfred connects to your ledgers, banks, and payment processors to automate transaction matching, resolve discrepancies intelligently, and give your team real-time financial clarity.
Based on real-time ledger data, your burn rate has decreased by 12% month-over-month. Here is the breakdown:
Alfred's Core Capabilities
Built to handle the complexity of modern fintech operations at scale.
Instant Answers
Latency under 200ms for most financial queries. No more waiting for data teams.
Learn moreProactive Intelligence
Alfred alerts you to anomalies and opportunities without being asked.
Learn moreAlways Learning
Alfred improves with every interaction, learning your specific business logic.
Learn moreAI Reconciliation vs Rules-Based Reconciliation
Most reconciliation tools match what they're programmed to match — and flag everything else. Alfred goes further.
| Rules-Based Reconciliation | Alfred AI Reconciliation | |
|---|---|---|
| Matching logic | Static rules set by finance team | Learns transaction patterns automatically |
| Exception handling | Every exception requires manual review | Resolves most exceptions autonomously |
| New data sources | Fails on new formats or edge cases | Adapts to new sources and anomalies |
| Maintenance | Requires regular rule updates | Improves from each reconciliation run |
| Scale | High manual workload at volume | Scales with transaction volume |
The result: fewer exceptions in the queue, faster period-end close, and finance teams that spend time on decisions — not discrepancy hunts.
How Alfred Handles Exceptions
AI reconciliation isn't about flagging more — it's about resolving more. Your finance team reviews only what genuinely requires human judgment.
Mismatch detected
A transaction arrives with a discrepancy — amount, counterparty, or timestamp doesn't match the expected record.
Alfred checks context
Alfred queries historical patterns, related transactions, and source metadata to understand the nature of the discrepancy.
High-confidence match
When Alfred's confidence exceeds the threshold, the exception is auto-resolved, logged with full audit trail, and never enters the queue.
Ambiguous case → surfaced with context
For genuinely complex cases, Alfred doesn't just flag — it surfaces the exception with a suggested resolution and the reasoning behind it.
The outcome: Exceptions that would previously require a full manual review cycle get resolved at the infrastructure layer — deterministically, with a full audit trail, before they ever reach your team's queue.
See Alfred in Action
I found 12 failed transactions totaling $18,450. The primary cause was "Insufficient Funds".
Breakdown by Error
Action Items
Programmatic Access to Intelligence
Embed Alfred's capabilities directly into your internal tools, Slack bots, or customer-facing dashboards.
Natural Language to SQL
Send raw text queries, get structured data back. No parsing required.
Role-Based Scope
API keys can be scoped to specific ledgers or data sensitivity levels.
Async Webhooks
Trigger complex analysis jobs and receive results via webhook.
curl -X POST https://api.naya.finance/v1/alfred/query \
-H "Authorization: Bearer sk_live_..." \
-H "Content-Type: application/json" \
-d '{
"query": "What is the total volume of international transactions in Q3?",
"filters": '{
"currency": ["USD", "EUR"],
"status": "settled"
},
"output_format": "json"
}'
# Response
{
"data": '{
"total_volume": 452900.50,
"currency": "USD",
"transaction_count": 1240
},
"confidence": 0.99
}
Try Alfred's Data Processing
Experience how Alfred handles financial operations data in real-time.