Evolution of Digital Fraud Prompts Industry Action
As industries continue adopting tech resources to optimize operations, individuals seeking to commit fraud have also embraced and deployed cutting-edge technology, prompting industries to seek skilled software development teams to counter fraudulent efforts that continue to impact revenues and consumers significantly.
Unauthorized transactions are amongst the most common methods of fraud within banking. Statista forecasts unauthorized transactions can cost financial institutions and consumers $38.5 billion by 2027. This chilling report has prompted the finance industry to implement software-based solutions to retain valued banking clients and reduce expenses associated with recovering lost revenue from fraud.
Phishing remains an effective method for committing financial fraud activities. Despite investments made by banking institutions to educate clients, the FBI reported consumers lost $54 million due to phishing scams. Phishing continues confusing consumers because they receive communications directly to personal and business e-mail accounts that closely resemble legitimate companies that advertise products and services by utilizing trademarked logos. E-mail-based scams seek to gather data to steal identities and directly result in unauthorized transactions.
Additionally, resources such as Natural Language Processing have further deceived consumers because fraudulent messaging is more accurately created to mimic industry language and official communications, making legitimate e-mail messages vs. phishing more difficult to decipher.
Financial institutions have adopted Artificial Intelligence resources enhanced by adept software developers to counter the significant increase in digitally-based fraud attacks. Software developers can customize fraud detection programs with unprecedented efficiency and superior performance.
Fraud Mitigation Strategies Powered by AI
Fraud activities follow patterns, but traditional investigation methods often overlook undetected critical nuances. Machine Learning, a subset of AI, sorts through vast amounts of data to quickly identify patterns in transactions, amount, frequency, and purchase category. Banks can assemble profiles, much like authorities, when working to identify suspects and analyze the data compiled by ML to gain a unique insight into fraud by learning what medium, what language, and other methods are used by a scammer to ultimately deploy strategies to proactively prevent scams while alerting clients to threats faster than traditional anti-fraud measures.
Machine Learning’s advanced data analytics enable expedient identification of which banking process is more vulnerable to fraud, allowing a bank to evaluate and upgrade its fraud protection processes quickly. ML enables large volumes of data to be monitored, far exceeding traditional, manual data analysis methods. Compiling and monitoring data in real-time is an invaluable resource because suspicious activity can be immediately identified, and banking personnel can proactively implement safeguards while providing enhanced fraud protection and customer service to clients.
Fraud scores are new resources banks use to determine any associated risks with a transaction. Predicated on ML data analytics, they provide a numerical value to a transaction, enabling a bank to approve or deny the funds. Parameters such as transaction amount, frequency, and even the IP address of a user can be determining factors for assessing data and compiling a fraud score for a bank to review. Dedicated software developers can customize fraud score programs to include any parameter to assist a financial institution with making a more informed decision about suspicious activity.
Enhanced Partnership Provides Critical Resources
The FBI continues alerting financial institutions to global fraud and money laundering campaigns that resulted in a staggering loss of $10.3 billion in 2022. Adhering to the FBI’s call for increased financial cyber security protocols, the Biden Administration signed the Cyber Incident Reporting for Critical Infrastructure Act of 2022. Coupled with a budget of $98 million, we see this as a great leap forward for government officials to strengthen its partnership with law enforcement and financial institutions to mitigate cyber-based fraud.
The Cyber Incident Reporting for Critical Infrastructure Act of 2022 creates a new and increased budget for the creation and deployment of software-based resources dedicated to fraud mitigation. This enables new revenue opportunities for software developers seeking to grow their presence within anti-fraud programming sectors.
New Investments in AI Reinforce Value to Financial Industry
Software specialists continue enhancing AI to optimize industries, demonstrating limitless capabilities as resources can be customized to suit any business challenge. Artificial Intelligence and Machine Learning look perfectly matched to meet and exceed banking’s anti-fraud goals. The industry relies heavily on figures and data sets to meet benchmarks. With ML’s unique ability to expertly monitor large volumes of data while identifying patterns and nuances, we see this as an invaluable resource to boost anti-fraud protocols.
Fintech News has reported banking institutions have collectively invested $217 billion in AI-based resources to safeguard investments and clients from fraudulent activities, reinforcing the value the industry is placing on these cutting-edge resources. Software developers working to enhance AI have demonstrated this resource’s unprecedented versatility and successful deployment within multiple industries. With the recent news of financial institutions significantly investing in AI, software developers customizing this technology to mitigate fraud successfully are poised to capture market share. To explore the opportunity to work with trusted industry and technical experts to develop customized AI-powered fraud prevention solutions, contact us today.