Check fraud alludes to any illicit attempts to gain funds via paper or electronic checks. This could involve writing a poor check on one’s account, counterfeiting a check in another person’s name, or creating a wholly fraudulent check (Check Fraud 101 | SQN Banking Systems, 2019). Due to the increase in e-commerce, contactless banking, and computational capability, check fraud has escalated and become more complex today (Button & Cross, 2017). Fraudsters are adopting many of the same technology that firms use to develop and swiftly deliver new banking offerings and services. Using inexpensive, on-demand computing resources or machine learning techniques that are more nuanced and capable of influencing check fraud detection technologies, fraudsters can more effortlessly violate the law.
As discussed herein, the four types of check fraud include paper hanging, check kitting, check floating, and check forgery. Paperhanging means writing faulty checks intentionally, where some account holders set up accounts to write bogus checks (Varmedja et al., 2019). Check-kiting incorporates two wallets owned by the same individual or a partner. Moreover, such people pay from one bank to another, giving the impression of a balance (Varmedja et al., 2019). On the other hand, check floating is written to enjoy the benefits of float time. Account beneficiaries float checks to stall for time before payday. In other circumstances, they operate a more complicated version of the previous hoax.
Falsifying or fudging a signature on a check constitutes check forging. Individuals who carry out such kind of fraud spoof the signature on another person’s check. Then, they can deposit the check in their account or utilize it to acquire products or services. Forgery often occurs in an enterprise when a worker writes a check without appropriate authority (Poddar et al., 2020). Additionally, fraudsters will likely steal a check, approve it, and submit it for settlement at a storefront or bank teller counter with fake credentials.
To detect changed checks, business personnel should remember the following suggestions. First, operators should search for inconsistent handwriting when receiving checks for depositing over the counter. If they notice discrepancies between the value, the payee’s identity, or other written data on the check, they may choose to conduct additional research before processing it. Second, in receiving checks for deposit, cashiers should also search for evident indications of tampering. If they recognize that the balance has been altered or that there are correction markings beneath the payee’s initials, they must adhere to the procedures established. Lastly, organizations may suggest buying check fraud monitoring tools such as SENTRY, Inspect, and SENTRY to prevent losses due to check fraud (Check Fraud 101 | SQN Banking Systems, 2019). These systems examine the stock components electronically and find minor flaws invisible to the human eye. Then, they provide the suspect checks in a workflow program so that staff can physically decide.
Businesses may use the following discussed ways to curb potential check fraud. First, signature verification solutions allow companies to gather and preserve consumer signatures. Then, specialized software examines the signatures on checks and other records for inconsistencies. Second, businesses can use UV fraud identification software by having clients print checks with UV ink. The financial institution next runs the check through a UV scanner to discover any manipulated UV ink. Lastly, banks may utilize an automated image method that uses robust algorithms to detect subtle changes that are not evident to the human eye. These tools examine payee data, signature line positioning, typeface, design, check size, and unique identifiers.
Other information from the website that may benefit business managers is determining how to reduce the expenses of check fraud on their enterprises. Organizations should start checking checks at the presentation stage to lower the enormous cost of fraud. Once a check has been confirmed valid, the operation can be carried out as usual. Additionally, corporate executives might automate the login and evaluation process to reduce human mistakes. Check fraud algorithms compare encoded check data to digitalized data and provide an error notice if there are any inconsistencies, minimizing check fraud costs.
References
Button, M., & Cross, C. (2017). Technology and Fraud: The ‘Fraudogenic’ Consequences of the Internet Revolution. In The Routledge Handbook of Technology, Crime and Justice (pp. 78-95). Routledge.
Check Fraud 101 | SQN Banking Systems. (2019). SQN Banking Systems.
Poddar, J., Parikh, V., & Bharti, S. K. (2020). Offline signature recognition and forgery detection using deep learning. Procedia Computer Science, 170, 610-617.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE.