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生物辨別技術

市場調查報告書

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1372603

全球行為生物辨識市場 - 2023-2030

Global Behavioral Biometrics Market - 2023-2030

出版日期: 2023年10月18日 | 出版商:

DataM Intelligence | 英文 205 Pages | 商品交期: 約2個工作天內

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簡介目錄

概述

全球行為生物辨識市場在 2022 年達到 16 億美元，預計到 2030 年將達到 74 億美元，2023-2030 年預測期間年複合成長率為 20.5%。

網路攻擊、詐欺和身分盜竊的發生率日益增加，因此身分驗證技術得到了增強。除了密碼和 PIN 等傳統安全措施之外，行為生物辨識技術還提供額外的保護等級。與傳統方法相比，行為生物辨識技術提供了更流暢、使用者友善的身份驗證體驗。使用者無需記住複雜的密碼，身份驗證可以是被動且持續的，增強了便利性。

機器學習演算法顯著提高了行為生物辨識的可靠性和準確性，這些演算法可以分析大型資料集並檢測使用者行為中的微妙模式。歐洲的 GDPR 和加州的 CCPA 等資料隱私法規促使組織探索更安全和隱私友好的身份驗證方法，從而導致人們對行為生物識別的興趣增加。

亞太地區的網路威脅和詐欺企圖越來越多，行為生物辨識技術提供了連續的安全層來適應不斷變化的威脅。人工智慧和機器學習的進步正在提高行為生物識別系統的準確性和有效性，使其對該地區的組織更具吸引力。

動力學

全球線上交易的成長

全球企業和個人擴大轉向數位平台進行各種活動，包括購物、銀行業務和通訊。線上交易的便利性推動了其成長，使得行為生物識別等安全身份驗證方法變得至關重要。網路攻擊、資料外洩和線上詐騙的數量不斷增加，更加需要更強大的身份驗證方法。行為生物辨識技術增加了額外的安全層來防範這些威脅。

例如，2023 年 9 月 6 日，就業背景篩選服務專家 First Advantage Corporation 以 4,100 萬美元全現金交易收購了總部位於紐約希克斯維爾的生物辨識新創公司 Infinite ID。客製化生物辨識解決方案並擁有子公司 PrintScan，專注於指紋辨識軟體。

兩家公司均表示，Infinite ID 是一家獲利企業，預計年收入將超過 1,000 萬美元。報告顯示，與 ITRC 接觸的受害者中有 16% 表示在成為身分犯罪受害者後有自殺念頭，高於去年的 10%。身分犯罪的經濟影響似乎也在加深，26% 的 ITRC 受害者報告損失超過 10 萬美元。

對多層安全方法的需求不斷成長

網路釣魚、惡意軟體和社會工程等各種網路攻擊是日益成長的威脅格局的一部分。由於傳統的安全措施通常不足以阻止這些攻擊，因此經常需要額外的安全層。由於網路犯罪分子正在開發更複雜的攻擊技術，因此識別和阻止違規行為變得更加困難。多層安全性增加了攻擊者的複雜性，並增加了偵測其活動的機會。

例如，2023 年 10 月 2 日，領先的硬體錢包製造商 CoolWallet 解決了 Web3 領域日益成長的網路釣魚攻擊威脅，特別是針對 Friend.tech 和 Coinbase 的以太坊第 2 層鏈 Base 等平台。 Friend.tech 是一個基於 Base 構建的去中心化社交媒體平台，已經取得了顯著的成長，但也吸引了惡意行為者不必要的關注。

CoolWallet 引入了 Web3 SmartScan 作為防禦網路釣魚攻擊的手段，這種主動交易篩選器可以在用戶成為盜竊受害者之前識別惡意行為和智慧合約漏洞。 CoolWallet Pro 與 Friend.tech 和 Base 無縫整合，提供 EAL6+ 安全元件、生物辨識驗證和防篡改設計等功能，以增強安全性。

行為生物辨識技術的進步

為了研究和解釋使用者行為模式，行為生物辨識主要依賴機器學習和人工智慧技術。隨著這些技術的發展，行為生物辨識系統的精確度和效率不斷提高。高效能運算資源和雲端基礎架構的可用性可以更快、更有效地分析行為資料，從而使即時身份驗證成為可能。

例如，2023 年9 月12 日，倫敦證券交易所集團(LSEG) 旗下企業GIACT 金融犯罪提案開發總監凱特琳·辛克萊(Caitlin Sinclair) 強調了銀行客戶（包括消費者和企業）在整個客戶生命週期中的漏洞，使他們成為詐欺的主要目標。金融機構需要採用超越傳統方法的多方面方法，包括多重身份驗證、拋棄式密碼以及利用替代資料增強驗證的技術。

隱私問題和不準確的數據

使用行為生物辨識技術的系統可能並不總是完全準確。誤報或漏報可能是由使用者變化、環境和所獲得的資料品質等因素造成的。行為有重大變化或殘疾的使用者可能會對這些系統的準確性構成挑戰。儘管行為生物識別通常依賴被動資料收集，但一些用戶參與仍然是必要的。使用者必須採取特定操作（例如打字或滑動）才能收集資料。

一些用戶可能會發現行為生物辨識技術具有侵入性，因為它會持續監控他們的行為和行為。可能會出現隱私問題，特別是當系統在沒有明確同意或控制機制的情況下收集敏感資料時。行為生物識別資料通常以模板的形式存儲，如果保護不當，很容易被盜竊或洩露。保護這些模板對於防止未經授權的存取和濫用至關重要。

Rising cyberattacks, fraud and incidences of identity theft increase day by day so there are enhanced authentication techniques. Beyond conventional security measures like passwords and PINs, behavioral biometrics provides an additional level of protection. In comparison to conventional approaches, behavioral biometrics offers a more smooth and user-friendly authentication experience. Users don't need to remember complex passwords and authentication can be passive and continuous, enhancing convenience.

Machine learning algorithms significantly improve the reliability and accuracy of behavioral biometrics and these algorithms lead to analyze large datasets and detect subtle patterns in user behaviors. Data privacy regulations like GDPR in Europe and CCPA in California have prompted organizations to explore more secure and privacy-friendly authentication methods, leading to increased interest in behavioral biometrics.

A growing number of cyber threats and fraud attempts in Asia-Pacific, where behavioral biometrics provides a continuous layer of security that adapts the evolving threats. Advancements in artificial intelligence and machine learning are improving the accuracy and effectiveness of behavioral biometrics systems, making them more appealing to organizations in the region.

Dynamics

Global Rise in Online Transaction

Businesses and individuals globally are increasingly transitioning to digital platforms for various activities, including shopping, banking and communication. The convenience of online transactions has driven their growth, making secure authentication methods like behavioral biometrics crucial. The escalating number of cyberattacks, data breaches and online fraud has heightened the need for stronger authentication methods. Behavioral biometrics adds an extra layer of security to protect against these threats.

For instance, on 6 September 2023, First Advantage Corporation, a specialist in employment background screening services, acquired Infinite ID, a biometrics startup headquartered in Hicksville, New York, in a US$41 million all-cash deal. Custom biometric solutions and owns the subsidiary PrintScan, focused on fingerprinting software.

Both companies have stated that Infinite ID, a profitable venture, is anticipated to generate annual revenues exceeding US$10 million. The report reveals that 16 percent of victims who engaged with the ITRC reported experiencing thoughts of suicide after falling victim to identity crimes, up from 10 percent the previous year. The financial impact of identity crime also appears to be deepening, with 26 percent of ITRC victims reporting losses exceeding US$100,000.

Rising Need for a Multi-Layered Security Approach

A variety of cyberattacks, including phishing, malware and social engineering are part of the growing threat landscape. Additional layers of security are frequently required because traditional security measures are frequently insufficient to thwart these assaults. Because cybercriminals are developing more complex attack techniques, it is more difficult to identify and stop breaches. Multi-layered security adds complexity for attackers and increases the chances of detecting their activities.

For instance, on 2 October 2023, CoolWallet, a leading hardware wallet manufacturer, addressed the growing threat of phishing attacks in the Web3 sector, particularly targeting platforms like Friend.tech and Coinbase's Ethereum layer-2 chain, Base. Friend.tech, a decentralized social media platform built on Base, has seen significant growth but is also attracting unwanted attention from malicious actors.

CoolWallet introduced the Web3 SmartScan as a defense against phishing attacks and this proactive transaction screener identifies malicious behavior and smart contract vulnerabilities before users become victims of theft. CoolWallet Pro, which integrates seamlessly with Friend.tech and Base, offers features such as an EAL6+ secure element, biometric verification and a tamper-proof design to enhance security.

Advancement in Behavioral Biometrics Technology

In order to study and interpret user behavior patterns, behavioral biometrics mainly relies on machine learning and artificial intelligence technologies. The precision and efficiency of behavioral biometrics systems increase as these technologies develop. The availability of high-performance computing resources and cloud infrastructure enables faster and more efficient analysis of behavioral data, making real-time authentication feasible.

For instance, on 12 September 2023, Caitlin Sinclair, Director of Proposition Development for Financial Crime at GIACT, an LSEG business, highlighted the vulnerabilities across the customer lifecycle for banks' customers, including consumers and enterprises, making them prime targets for fraud. Financial institutions, need to adopt multi-faceted approaches that go beyond traditional methods and this approach includes multi-factor authentication, one-time passwords and embracing technology that leverages alternative data for enhanced verification.

Privacy Concerns and Inaccurate Data

Systems using behavioral biometrics might not always be completely accurate. False positives or negatives may result from elements including user variation, the environment and the quality of the data that was obtained. Users with significant behavioral changes or those with disabilities may pose challenges to the accuracy of these systems. Although behavioral biometrics often rely on passive data collection, some user participation is still necessary. Users must take specific actions (such as typing or swiping) in order for data to be collected.

Some users may find behavioral biometrics intrusive, as it continuously monitors their actions and behaviors. Privacy concerns can arise, particularly when the system collects sensitive data without clear consent or control mechanisms. Behavioral biometric data is typically stored in the form of templates, which can be vulnerable to theft or compromise if not properly secured. Protecting these templates is crucial to prevent unauthorized access and misuse.

Segment Analysis

The global behavioral biometrics market is segmented based on type, deployment, application, end-user and region.

Significant Advancement in Signature Analysis Boosts the Market

Machine learning algorithms have made a significant advancement in recent years, allowing for more accurate and reliable analysis of behavioral biometric data and this has contributed to the feasibility and effectiveness of integrating behavioral biometrics into signature analysis. Security is paramount organizations also strive to provide a seamless user experience. Behavioral biometrics can enhance user convenience by enabling frictionless authentication based on natural behaviors, such as how a person signs their name.

According to the paper published in Transactions on Engineering and Computer Science, in September 2021, the significance of handwritten signatures as a widely accepted behavioral trait in biometric security systems. Signatures contain various dynamic and innate behavioral traits that can provide insights into a person's soft characteristics, including age, gender, personality and handedness. The paper presents a personality prediction system that determines different characteristics of a person's personality based on offline handwritten signature images.

Geographical Penetration

Digital Transformation in North America

North America has seen the implementation of stringent data privacy regulations, such as the California Consumer Privacy Act and the General Data Protection Regulation for businesses dealing with European customers. Behavioral biometrics aligns with these regulations as it often doesn't require the storage of sensitive biometric data. Organizations in North America are undergoing digital transformation initiatives, with a focus on providing digital services to customers.

For instance, on 7 August 2023, BioCatch Ltd. unveiled "BioCatch Ltd. Connect," a revamped anti-fraud platform powered by behavioral biometrics technology and this platform utilizes artificial intelligence (AI) to analyze data from various sources, including applications, devices and networks, enabling it to assess user behavior within specific contexts. foundational element continuously collects thousands of data signals from various sources through a lightweight mobile and web software development kit (SDK).

Competitive Landscape

The major global players in the market include BioCatch Ltd., Nuance Communications, Inc., LexisNexis Risk Solutions, Ping Identity, Zighra Inc., IKS TN S.r.l., Fair Isaac Corporation, Mastercard International Incorporated, ThreatMark and Plurilock Security Inc.

COVID-19 Impact Analysis

With lockdowns and social distancing measures in place, people have turned to digital channels for work, education, shopping and entertainment and this increased digital activity has generated more behavioral data, providing a plenty of information for behavioral biometrics systems to analyze. The pandemic has led to significant changes in user behavior. Remote work and online learning have altered typing patterns, mouse movements and other digital interactions. Behavioral biometrics systems have needed to adapt to these new patterns and recognize them as legitimate.

The need for secure remote access to systems and services has surged. Behavioral biometrics has played a crucial role in providing frictionless authentication for remote workers, reducing the reliance on traditional authentication methods like passwords. The pandemic has brought about an increase in cyberattacks and fraud attempts. Behavioral biometrics has been leveraged to detect fraudulent activities, such as account takeovers and phishing attacks, by analyzing user behavior for anomalies or suspicious patterns.

Some organizations have explored the use of behavioral biometrics for health monitoring during the pandemic. For example, monitoring typing patterns or voice characteristics to detect signs of stress or fatigue in remote workers. The collection and analysis of behavioral data for authentication and monitoring have raised privacy concerns. Users may be more sensitive to the handling of their personal data, leading to increased scrutiny of behavioral biometrics practices.

AI Impact

AI algorithms can analyze and interpret behavioral biometric data with high accuracy. Machine learning and deep learning techniques enable systems to recognize subtle patterns and variations in user behavior, reducing false positives and false negatives. AI enables real-time analysis of behavioral biometric data and this means that user authentication and fraud detection can occur instantaneously, providing immediate security responses when anomalies or suspicious activities are detected.

AI-powered behavioral biometrics systems can continuously learn and adapt to evolving user behavior and they can identify changes or deviations from established patterns, making them effective in detecting fraudulent activities that may change over time. AI algorithms excel at detecting anomalies in user behavior, they can identify unusual or unexpected actions that may indicate fraudulent access or compromised accounts, providing an additional layer of security.

For instance, on 26 September 2023, Amazon introduced new AI capabilities for its Alexa products, powered by a large language model called AlexaLLM and this technology aims to make Alexa more personalized and capable of retaining context during conversations. However, it was revealed that Amazon plans to use some user voice interactions with Alexa to train its AI model.

Amazon reassured users that they will maintain control over their Alexa experience through privacy controls and indicators, such as a glowing blue light and optional audible tones when Alexa is listening. However, the introduction of features like "Alexa, let's chat" with Visual ID, which allows activation without cue words, raises questions about privacy.

Russia-Ukraine War Impact

During times of geopolitical conflict, there is often an increase in cyberattacks and cyber threats. Adversarial nations or cybercriminal groups may target critical infrastructure organizations or individuals. By examining user behavior for indications of harmful activity, behavioral biometrics can be extremely useful in identifying and reducing such risks. Conflict-affected areas typically have more awareness of security issues and the value of safeguarding confidential information.

The disruption caused by conflict and security concerns may result in more people working remotely and conducting digital transactions. Behavioral biometrics can facilitate secure remote access and online transactions by providing continuous authentication without the need for physical tokens or passwords. In regions directly affected by conflict or political instability, there may be concerns about government surveillance and the privacy of individuals' digital activities.

By Type

Signature Analysis
Keystroke Dynamics
Voice Recognition
Gait Analysis

By Deployment

On-Premise
Cloud

By Application

Identity Proofing
Continuous Authentication
Risk and Compliance
Fraud Detection and Prevention

By End-User

BFSI
Retail and Commerce
Healthcare
Government and Public Sector
Others

By Region

North America
- U.S.
- Canada
- Mexico

Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe

South America
- Brazil
- Argentina
- Rest of South America

Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific

Middle East and Africa

Key Developments

In April 2023, Onbe, a leading financial technology company specializing in disbursements, introduced OnbeGuard, an enhancement to its suite of fraud prevention tools. OnbeGuard now incorporates behavioral biometrics from BioCatch Ltd., a renowned fraud detection leader and this advanced solution combines historical spending patterns, BioCatch Ltd.'s behavioral biometrics and channel data to predict and combat payment fraud while reducing false positives at checkout, account login and ATMs.
In May 2022, the Commonwealth Bank of Australia (CBA) is enhancing its fraud detection capabilities by incorporating additional behavioral biometrics into its security features. The bank will utilize behavioral biometrics to analyze customer computer configurations and individual behavior patterns, strengthening its real-time fraud detection capabilities across digital channels.
In May 2022, LexisNexis Risk Solutions (LNRS) acquired LexisNexis Risk Solutions, a behavioral biometric technology provider, to enhance its anti-fraud solutions and this integration will enable merchants to strengthen identity verification and prevent fraud by utilizing a layered defense approach. Behavioral biometrics analyze how trusted users interact with their mobile devices and use this information for authentication during subsequent transactions.

Why Purchase the Report?

To visualize the global behavioral biometrics market segmentation based on type, deployment, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of behavioral biometrics market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global behavioral biometrics market report would provide approximately 69 tables, 70 figures and 205 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Type
3.2. Snippet by Deployment
3.3. Snippet by Application
3.4. Snippet by End-User
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Global Rise in Online Transaction
  - 4.1.1.2. Rising Need for a Multi-Layered Security Approach
  - 4.1.1.3. Advancement in Behavioral Biometrics Technology
- 4.1.2. Restraints
  - 4.1.2.1. Privacy Concerns and Inaccurate Data
- 4.1.3. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Type

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 7.1.2. Market Attractiveness Index, By Type
7.2. Signature Analysis*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Keystroke Dynamics
7.4. Voice Recognition
7.5. Gait Analysis

8. By Deployment

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 8.1.2. Market Attractiveness Index, By Deployment
8.2. On-Premise*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Cloud

9. By Application

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.1.2. Market Attractiveness Index, By Application
9.2. Identity Proofing*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Continuous Authentication
9.4. Risk and Compliance
9.5. Fraud Detection and Prevention

10. By End-User

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.1.2. Market Attractiveness Index, By End-User
10.2. BFSI*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Retail and Commerce
10.4. Healthcare
10.5. Government and Public Sector
10.6. Others

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. U.S.
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. UK
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Russia
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. BioCatch Ltd.*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Nuance Communications, Inc.
13.3. LexisNexis Risk Solutions
13.4. Ping Identity
13.5. Zighra Inc.
13.6. IKS TN S.r.l.
13.7. Fair Isaac Corporation
13.8. Mastercard International Incorporated
13.9. ThreatMark
13.10. Plurilock Security Inc.

LIST NOT EXHAUSTIVE