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How We Make It Happen
We leverage algorithmic models that extract, verify, and analyze paystub data, even from images, ensuring accurate classification of earnings, deductions, and fraudulent entries. Our Docker-based services are highly scalable, so your system grows as your needs do.
The Results?
Faster paystub processing, reduced fraud, and 1500+ keys identified for accurate creditworthiness checks, repayment capacity evaluation, and proof of income verification.
Success in Action
Success in Action A financial firm automated its document processing, eliminating manual review and speeding up processing by 90%.
How We Make It Happen
Using Spark MLlib, Kafka, and Cassandra, our system analyzes every transaction for fraudulent patterns in real time. We generate fraud scores that highlight suspicious activity, ensuring quick action.
The Results?
A more robust and intelligent financial supervision ecosystem with reduced fraudulent transactions.
Success in Action
A major mobile money provider slashed fraud rates by 75% with Quantrium’s AI-powered fraud detection, protecting millions of transactions.
How We Make It Happen
We extract account details, transactions, and categorize expenses such as EMI payments, withdrawals, and loans, making credit decisions easier and faster.
The Results?
Faster decision-making, reduced operational costs, and enhanced loan approval processes, especially in microfinance.
Success in Action
A national bank processed 200+ pages of bank statements in under 10 minutes, driving faster loan approvals for SMBs.
How We Make It Happen
Our platform evaluates balance sheets, profit & loss statements, and income patterns to provide in-depth financial profiling. This enables financial institutions to conduct due diligence before approving business loans or customising loan services.
The Results?
Enhanced risk assessment, frictionless workflows, and up to 90% reduction in onboarding and assessment time.
Success in Action
A top regional bank adopted our solution, cutting onboarding time by half while improving the accuracy of its credit risk models.
