Name Match API | Cashfree Payments

Cashfree’s Name Match is an AI-powered name comparison API designed specifically for India’s complex naming conventions. It helps businesses instantly verify if two names refer to the same person by returning the following:

Name match score: 0 to 1
Name match category: Direct Match, Partial Match, or No Match

Set custom risk thresholds and automate decisions for KYC, payouts, fraud prevention, and other processes. In India, traditional string-matching algorithms often return inaccurate results because of initials, variations in name order, local spellings, and missing components. These issues can lead to rejection rates of up to 18% and increased operational effort. Name Match is designed to handle these scenarios. Key factors behind the Name Match reliability

Handles initials, middle names, and abbreviations.
Understands phonetic and regional spelling variants.
Recognises missing or extra spaces.
Supports subset matching such as Harsh Kishore vs HKishore.
Detects salutation-based name patterns such as Aditya Roy S/O Jatin.
Considers sequence, gender, and regional norms as it is context aware.

Key benefits

The following points highlight the key capabilities of Cashfree’s Name Match feature:

Built for Indian names:Trained on over 100 million Indian name records, the model understands initials, salutation formats, and regional variations.
Accurate and explainable: Returns both a match score and a category, enabling you to build rule-based logic around onboarding or rejection.
Higher conversion, lower friction: Reduce false mismatches, improve user onboarding success rates, and cut down on manual reviews.
Real-time and scalable: Integrates with your existing stack to validate names instantly at scale.

Use cases

The following are key use cases for the Name Match API:

Business type	Benefits
Fintech and payments	Prevent fraud and accelerate onboarding
KYC and lending	Automate compliance checks and reduce manual overhead
E-commerce	Decline suspicious collect requests or wallet withdrawals automatically
Payout and reconciliation	Minimise payout failures and automate reconciliation
Risk and fraud prevention	Flag name mismatches for deeper review

Verifying name match

Follow these steps to verify the Name Match in the Merchant Dashboard:

Log in to the Merchant Dashboard.
In the left navigation menu, select Regulated Digital KYC, and then select Name Match.
In the input fields, enter the two names you want to compare.
Select Verify to start the name match check.
View the match score and match category in the popup.

You can also try the Verify Name Match API for real-time, programmatic validation.

Score categorisation

The following table lists the match categories and their corresponding score ranges returned by Name Match.

Match category	Match score range
Direct Match	1.00
Good Partial Match	0.85–0.99
Moderate Partial Match	0.60–0.84
Poor Partial Match	0.34–0.59
No Match	0.00–0.33

Examples

The following examples show sample name comparisons, their match scores, and the corresponding match categories.

Name 1	Name 2	Match score	Match category
Rahul Verma	Rahul Verma	1.00	Direct Match
S K Mishra	Satish Kumar Mishra	0.92	Good Partial Match
Harsh Kishore	HKishore	0.84	Moderate Partial Match
Jatin Kumar	Jatin Roy	0.52	Poor Partial Match
Rakesh Sharma	Ritu Sharma	0.23	No Match

​Key benefits

​Use cases

​Verifying name match

​Score categorisation

​Examples

Key benefits

Use cases

Verifying name match

Score categorisation

Examples