Case Study

Signature Verification Solution For BFSI Industry

Signature Verification is a task of determining whether a questioned signature matches known signature samples. Day in and day out, customers either drop cheques using drop box, or on cashier counter to pick up cash.

At a Glance

These cheques are picked up periodically and submitted for clearing.

This task involves checking if the cheque belongs to our bank, if yes, then get the account number, input the account number into the system and fetch customer details and signature sample.

Next step involves manually inspecting the signature from the system against that found in the cheque. This procedure repeated over multiple times may cause burnout and can introduce manual error.

Key Highlights

Check Verification

Automation Process

Reduced Employee Burnout

Saves Time

Prevent Human Error

Prevent Fraud

Challenges

Manual account number entry in system to fetch customer record.
Manual verification of signature in cheque against those stored in customer records.
Employee burn-out due to continuous execution of repetitive task.

About Client

The client is a South Africa based company offering a signature based verification solution.

FINTECH

Industry

VERIFICATION

Domain

SOUTH AFRICA

Location

How we helped?

Azilen engaged with the client for blue-printing the concept from idea to implementation.

A robust platform was engineered with a comprehensive technical due-diligence on drone systems, SDKs, high-performance architectures and effective control mechanisms.

The Solution

Signature verification is a technique used to validate the captured image signature to those signature image stored in records. It compares signatures and checks for authenticity. It generates a confidence score, where in a low confidence score suggest a forgery.
Machine learning refers to techniques used wherein intelligence is acquired by referring to examples. In order to solve the signature verification problem, learning Signature Verification Solution can be acquired in two ways:

Person-independent / general learning: it involves learning from a set of genuine and forgeries from a general population and differences between genuine and forged signatures across all individuals are learnt.
Person-dependent / special learning: it involves learning from multiple samples of only that person’s signatures where in within-person similarities are learnt.

It is observed that special learning performs better than general learning and there is an increase of up to 5% in the accuracy. Hence, we have developed our solution using special learning technique.

In order to perform automatic signature verification there are two parts :

- Extract features from a signature
- Determine similarities of two signatures based on these features

Based on the above methods, a confidence score is derived that indicates whether the question signature matched to that particular person or not.

Various scenarios like join accounts where more than one person is allowed to sign on the cheques, can be handled as well with custom logic.

In order to automate this process to an extent, a scanned copy of cheque will be obtained, machine will be able to recognize the account number and signature portion and carry on these steps seamlessly with existing systems.

Accuracy in the range of 80-85% was achieved.

By embracing predictive analytics, personalization, and proactive strategies, they can navigate the challenges of churn with confidence, and stand as exemplars of how innovation can reshape industries and solidify customer loyalty.

The path to sustained success is no longer merely about acquiring customers; it’s about retaining and cherishing them through intelligent insights and action.