Bank Process Automation

For a prominent bank in Indonesia, we developed a computer vision based signature forgery detection system. Using computer vision, the solution is able to learn textual patterns in scanned images or in actual document visuals. The system uses deep learning algorithms to compare it with the original signature to identify even the minutest variations.


Our client is a prominent bank in Indonesia, headquartered in Jakarta. This bank has more than a thousand branches all over the country.


Signature forgery is becoming very commonplace in the banking industry. A fraudster forges a legitimate account holder's signature to withdraw money out of their bank account. If a bank pays out based on a cheque with a forged signature, it is liable for the loss of the account holder. This is why a bank employs several workers to verify the signature in the submitted cheques.

The workers analyze the signature provided in the cheque, compare it with the signature copy stored in the bank database, and verify the authenticity of the signature. Our client had employed 6-12 employees for signature verification in each branch. However, manual signature verification is time-consuming and susceptible to human errors. Moreover, the number of people required for the signature verification process was too high and that meant more costs.


Our client consulted us for a solution to make the cheque clearance process more efficient. The business requirements were to build software that can automate the cheque clearance process and its integration with the existing banking management system should be accomplished with ease.

  • Automate the signature verification process
  • Integrate with existing BMS software


Considering the challenges faced by our client, and based on their business requirement, we developed a computer vision-based signature forgery detection system. Using computer vision, the solution was able to learn textual patterns in scanned images or in actual document visuals. The system uses deep learning algorithms to compare it with the original signature to identify even the minutest variations.

There are situations when one might not be able to sign the exact same way as the sample available with the bank. AI systems are cleverly engineered to identify and measure this mismatch percentage and determine whether it is, in fact, a genuine signature that simply has a minor difference from the original one. This greatly improves the reliability of such systems as it does not cause inconvenience for genuine signatories.

Verification of a  customer’s signature for approval of loans, disbursal of cards, and other related information can be made easy by cross-examining and comparing with originally approved signatures within bank records. This is especially beneficial in preventing fake signatures on cheque leaves and withdrawal documents which would reduce financial fraud to a great extent.


Hybrid AI is a combination of machine intelligence being continuously improved and validated with human supervision. The entire concept of Hybrid AI revolves around the single fact that human and machine intelligence is not meant to be adversaries but complementary forces that join hands to deal with problems more systematically. In this model of AI, we cannot exactly term the final decisions made by algorithms as autonomous. The system is trained with the assistance of a human to arrive at a decision after any processing activity.

After each iteration of the data, the AI system automatically calculates a confidence score for the decision made. This score is the basis of determining how well the system is guaranteeing an accurate result. A low confidence score indicates that the result produced does not guarantee accuracy and thus the system needs human supervision. After imparting the training, the system is again made to determine autonomous decisions and the confidence score is again calculated. Only when the confidence score is fully satisfactory, will the system be allowed to make standalone decisions. In our solution, when analyzing a signature, in case of being unable to decide its authenticity, the system would escalate the case to a human supervisor. Moreover, the system built on hybrid AI is able to learn from the decision made by the human supervisor.


Following are the benefits offered by the computer vision-based signature forgery detection system;

  • Reduces the time required to verify the signature authenticity.
  • Reduced the manpower requirement for signature verification.


The solution was able to deliver great benefits to the organization. Following are the key results of implementing a computer vision-based signature forgery detection system in our client’s bank.

  • Reduced the manpower requirement in one branch from 12 employees to 2 employees.
  • Reduced the cheque signature verification time by 70%.

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Frequently Asked Questions

What are the benefits of signature forgery detection systems?

Signature forgery detection systems offer important benefits to organizations. Some of these benefits are:

  1. Fraud Prevention: These systems help prevent fraud by identifying fake or altered signatures. They protect organizations from financial losses and unauthorized transactions.
  2. Increased Security: Signature forgery detection systems use advanced technology to analyze signature characteristics carefully. They can detect subtle differences that indicate forgery, providing extra security for important documents.
  3. Time & Cost Savings: These systems automate the signature verification process, saving time and effort. They also help avoid the costs of resolving fraud issues or legal disputes.
  4. Compliance & Legal Protection: These systems ensure compliance in industries with strict regulations, like finance or legal sectors. They provide evidence of signature authenticity, offering legal protection in case of disputes or audits.
  5. Reputation & Trust: Organizations demonstrate their commitment to integrity and security using signature forgery detection systems. Moreover, this builds trust with clients, partners, and stakeholders, enhancing their reputation.
  6. Scalability & Flexibility: These systems can be tailored to fit specific organizational needs. They can be integrated into existing systems or used as cloud-based solutions. They handle large volumes of signatures and adapt to changing business requirements.
What are the must-have features of a signature forgery detection system?

A reliable signature forgery detection system should possess certain essential features to fulfill its purpose effectively. 

  • Advanced Signature Analysis: The system should employ sophisticated techniques to analyze signatures in detail, including stroke patterns, pressure variations, and timing. This also allows it to identify any anomalies or irregularities indicating forgery.
  • Machine Learning Capabilities: The system can continuously improve its detection capabilities by utilizing machine learning algorithms. It learns from vast data, enhancing its accuracy over time and adapting to new forgery techniques.
  • Real-time Verification: The system should offer real-time verification capabilities, allowing immediate detection of potential forged signatures. This enables organizations to promptly address fraudulent activities and take necessary actions to mitigate risks.
  • User-Friendly Interface: A user-friendly interface simplifies the process of signature verification. It should be intuitive and easy to navigate, enabling users to interact with the system effortlessly.
  • Integration with Existing Systems: Seamless integration with existing document management or workflow systems is crucial for efficient implementation. This allows organizations to seamlessly incorporate the signature forgery detection system into their existing processes.
  • Scalability & Performance: The system should handle large volumes of signatures without compromising performance. It should scale effectively to accommodate the needs of growing organizations and varying workloads.
  • Customization Options: Flexibility to adapt to specific organizational requirements is vital. The system should allow customization based on different industries, document types, and signature styles, ensuring optimal detection accuracy.
  • Robust Security Measures: As the system deals with sensitive information, it should incorporate robust security measures to protect data privacy and ensure secure signatures and verification results storage.
  • Comprehensive Reporting & Audit Trail: The system should provide comprehensive reports and maintain an audit trail of all verification activities. This aids in compliance, facilitates auditing processes, and offers a transparent record of signature verifications.
  • Ongoing Support & Updates: It offers continuous support, including software updates and technical assistance, to ensure the system remains effective and current.

By encompassing these must-have features, a signature forgery detection system can effectively detect and prevent fraudulent activities, provide a seamless user experience, and contribute to the overall security and integrity of organizations' signature verification processes.

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Signature Forgery Detection

Convolutional Neural Network

97% Accuracy

Hybrid Intelligence

Dockerized Solution

Technologies Used