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Digitization and evolving technologies boosting up the FDP markets

Published On: October 2022

To prevent enterprises from suffering financial and reputational harm, FDP solutions use identification and authentication technologies to spot potentially fraudulent actions and assist organizations in looking into suspicious behaviors and inconsistent data pieces.

The fraud detection and prevention sector is rising due to the increasing requirement to reduce fraud losses and preserve revenue from legitimate transactions. Big data, data analytics, predictive modeling, deep learning, cloud computing, machine language, and artificial intelligence (AI) are some of the cutting-edge technologies that have recently emerged and are accelerating market growth. Additionally, the industry is growing because of the rise in real-time transactions and the number of online and mobile banking services. To reduce internal and external fraud, financial and governmental organizations are gradually implementing FDP solutions, boosting local and worldwide economies.

Money laundering, cyber security risks, tax evasion, identity theft, bogus insurance claims, forged bank checks, and financing of terrorism are some examples of fraud-related activities. These fraudulent operations affect the financial services, public sector, healthcare, and insurance industries. As a result, organizations have introduced cutting-edge fraud detection and prevention technology and risk management tactics to counteract the increasing number of chances for fraud. These methods combine real-time monitoring with substantial data sources, adaptive and predictive analytics, machine learning, and artificial intelligence.

The Global System for Mobile Communication (GSMA) anticipates that by the end of 2020, there will be 23 billion connections to the Internet of Things. Because of the simplicity with which they may be accessed, connected devices are one of the factors that make it simpler for hackers to access computer systems. Many different types of sensitive customer data are collected, sent, and stored by connected devices.

A few notable IoT scams that are widespread and treated seriously in business are those that use marketing and automated teller machines. The growth of cybercrime, which targets people with fake job offers, online bookings, free coronavirus tests, and debit and credit card activations, has contributed to the development of fraud detection and prevention systems.

During the epidemic, businesses quickly migrated to digital environments like online banking and eCommerce, which led to an increase in cyberattacks. This has forced enterprises to implement cutting-edge FDP solutions like fraud management and fraud analytics to assure overall security while also giving end users a frictionless experience.

Online fraud risks have risen in tandem with the popularity of business-to-consumer (B2C), business-to-business (B2B), and consumer-to-consumer (C2C) e-commerce transactions that frequently involve the exchange of sensitive information, identity information, and personal data of both individuals and businesses. Future fraudulent behavior is likely because of social media's growing popularity and mobile gaming development. Even as the strategies employed to conduct them become more intricate, the amount of money lost because of fraudulent operations is increasing.

FDP vendors should concentrate on the following to assist business enterprises with their digital transformation journey and assure their safety:

However, the lack of qualified staff who can stop fraudulent activities is one of the biggest problems that firms face today. The companies hiring security specialists lack the requisite expertise in assessing and spotting complex fraud during cyberattacks.


The global fraud detection and prevention market was valued at USD 29.80 billion in 2021 and is expected to reach USD 241.23 billion by 2030, registering a CAGR of 23.5% from 2022 to 2030. The adoption of technology by the banking and financial services sector is still in its infancy. Due to the numerous online data exchanges, financial institutions are highly vulnerable. It is more important than ever for financial institutions to protect transactions from fraud as digital banking processes proliferate. Financial organizations now put equal emphasis on reducing financial risk and detecting deception in real-time. As pattern recognition technology develops, fraud detection techniques are changing. Artificial intelligence has gotten a significant boost from using machine learning technology to guard any system since ML can shield businesses from insider fraud and spot any inconsistencies in people who may have stolen data.

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