This study proposes a handwritten signature verification method based on Improved combined features, which combines dynamic features and static features by using the complementarity between classifiers and score fusion. The significance of this study is for the purpose of verifying the authenticity of the signature and protecting the safety of customer property by extracting more comprehensive and representative signature features. The traditional approach for signature verification in the bank uses the human sense organ of eyes, and most of the time human judgment based on what is seen can be queried, especially in cases of impersonation, forgery, identity manipulation to name a few. Today the quest for fast money has driven a lot of frustrated people into various illegal acts and signature counterfeiting is one of the most common of this act. Banks and their customers occasionally fall victim because they lack adequate technology to verify signatures. Recently in most banks there are various cases of banking staff denying services to customers due to signature differences. This has resulted in a lot of misunderstandings, insults, quarrels, and even losses to most financial institutions. This work presents a digital signature verification system to enhance customer services in the banking industry, with the aim of improving the staff customer relationship within the Banking domain. This will be developed using image acquisition tool, image processing tools and machine learning. (Clustering technique). Signature has been globally accepted as a general means of official authentication, for legal documents, cheques, bank drafts, tellers, withdrawal slaps, deposit slops, receipts, and other official documents. This means has been widely accepted and implemented in all banking sectors due to its simplicity, confidentiality, and unique nature, also compared to other biometric verification systems, human signature is one of the few biological modalities that remain the same over time. This authentication means has been abused time and time again through impersonation (identical twins) and forgery, as a result has caused a lot of damage and losses to individuals and financial institutions. This research work addresses this challenge using artificial intelligent technique to present a novel signature verification system that helps authenticate business transactions in all financial institutions. The growing number of online transactions and contracts need stronger protection. Electronic signatures are undoubtedly a huge step forward in efficiency, but electronic signature counterfeiting is extremely real and worrying, especially as large become increasingly dependent on them, especially at a time when the globe is facing a pandemic. Traveling for the sake of conducting business has become a luxury since the world has come to a halt. Today electronic signature is very important in carrying out day-to-day business but at the same time, we need to be increasingly cautious and alert to prevent any e-signature fraud with carefully considered practices and procedures
