MarketsandMarkets forecasts the a new market research report “global insurance fraud detection market size to grow from USD 2.5 billion in 2019 to USD 7.9 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 25.8% during 2019–2024. Rising instances of claims fraud, identity theft, payment fraud; and an increasing sophistication level of cyber-attacks across regions are likely to drive the overall insurance fraud detection market.
Browse 84 market data Tables and 36 Figures spread through 155 Pages and in-depth TOC on “Insurance Fraud Detection Market by Component (Solutions (Fraud Analytics, Authentication, and GRC), Service) Application Area (Claims Fraud, Identity Theft, Payment and Billing Fraud, and Money Laundering), Deployment Mode, Organization Size, and Region – Global Forecast to 2024″
Any criminal activity with an intention to gain something valuable through misrepresentation is called a fraud. Frauds are of several types, such as payment fraud, information theft, identity theft, money laundering, IP theft, and Card Not Present (CNP) fraud. Fraud detection solutions and software enable organizations to detect frauds at an early stage and provide ways to prevent them. Fraud detection solutions help in detecting frauds at an early stage, whereas fraud prevention solutions help in the prevention of fraud from occurring.
The authentication segment is expected to record the highest CAGR in the insurance fraud detection solutions market during the forecast period. The authentication technology refers to the process of verifying the identity of the users, devices, or systems. Authentication plays a crucial role in the insurance fraud detection market. Fraud authentication helps enterprises to protect customer identity from the fraudsters. While fraud analytics helps detect fraudulent activities and the possibilities of fraud incidents happening in the future, fraud authentication is more inclined toward the prevention of such cases. Fraud authentication helps enterprises maintain the authenticity of transactions/information by blocking unauthorized access to the information or identifying false inputs from the users. Based on the use cases and complexity, authentication solutions can be categorized as Single-Factor Authentication (SFA) and Multi-Factor Authentication (MFA).
The cloud deployment mode is expected to grow at a higher CAGR, as SMEs are rapidly adopting this cost-effective deployment. This model helps the SMEs avoid the costs associated with hardware, software, storage, and technical staff. The cloud-based platform offers a unified way in the form of SaaS-based security services to secure business applications. It is also beneficial for organizations with strict budgets for security investments. The cloud deployment model is the most preferred model for securing web and mobile apps and used by most of the SMEs as it is easy to maintain and upgrade.
In terms of geographic coverage, the Insurance Fraud Detection Market has been segmented into five regions, namely, North America, Asia Pacific (APAC), Europe, the Middle East & Africa (MEA), and Latin America. The insurance fraud detection market in APAC is expected to witness substantial growth, as SMEs and large enterprises in the region are rapidly adopting insurance fraud detection solutions to ensure the security of organizational data and customer sensitive data. This region is expected to invest more in deploying insurance fraud detection solutions due to the increasing claims fraud, identity theft, and billing fraud. However, factors, such as increasing need to manage huge volumes of identities by organizations, effectively; improving operational efficiency & enhancing the customer experience; increasing adoption of advanced analytics techniques; and stringent regulatory compliances are driving the adoption of insurance fraud detection solutions.
Artificial Intelligence (AI) and Machin Learning (ML) are revolutionizing various businesses and industries. AI and ML help in drastically reducing workforce costs, discovering new patterns, and creating predictive models from raw data. The technologies enable real-time automated decisions for detecting fraudulent activities. AI and ML help data scientists to determine the transactions, which are most likely to be fraudulent. The technologies automatically discover the patterns across large volumes of streaming transactions. With the increasing use of cloud technology, the trend of managing and securing multiple accounts through the cloud is increasing. It has resulted in increased adoption of authentication solutions.
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