Risk Engine

Intelligence that makes lendingeasysimpleprofitableeasysimpleprofitable

Some risks may be worth taking, but when it comes to lending, we believe in playing it safe.

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Millions of Datasets

Accurate Decision

Very High Predictability

Making lending precise and profitable
with the power of deep learning

Artificial Intelligence engines

that leverage real-time cash-flow data, identify risky borrowers, and improve collection efficiency by 50%

Machine Learning models

trained on billions of data points to generate highly precise credit assessment

Predictive Analytics

that automate decision making, predict and prevent delinquencies, and reduce drop-offs by up to 60% on a portfolio

Big Data capabilities

that evolve at scale for each contextual use-case and deliver insights that come from deep learning and 16 million new-to-credit (NTC) personas

Lending is all about decisions.
We liberate it from human-error.

MSME Credit

Accurately assess borrowers using alternate data such as payments, cash flows, business accounts to assess creditworthiness

Business loans

Rely on our comprehensively benchmarked proprietary data connectors to evaluate borrowers

Personal loans

Get a borrower’s holistic financial background via a neural AI/ML engine that is trained on 16 million individual borrowers across billions of data point

Consumer durables and sachetized credit

Offset the risk of a collateral-free credit through guardrails-oriented originations with deep intelligence and decision science to back up credit decisions

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to experience the
benefits of…

Alternate Data Underwriting

that assesses a variety of financial transactions to generate a FinBox Inclusion Score (built on 5000+ parameters trained on largest New-To-Credit customer base in India)

Higher approvals rates

specifically for 2X more borrowers in digital lending, and 1.5X more borrowers than traditional lending - while maintaining the same portfolio quality

Clean, high-yield portfolios

thanks to precise lending and an API that lowers delinquency by more than 30%

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