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市場調查報告書
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1445949

金融科技中的人工智慧 - 市場佔有率分析、產業趨勢與統計、成長預測（2024 - 2029）

AI in Fintech - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3個工作天內

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簡介目錄

人工智慧金融科技市場規模預計到 2024 年為 440.8 億美元，預計到 2029 年將達到 508.7 億美元，預測期內（2024-2029 年）CAGR為 2.91%。

金融科技中的人工智慧 - 市場

COVID-19 大流行的爆發加速了人們與金融服務互動方式的改變。以支付和財富為重點的金融科技公司一直致力於透過投資新資源或擴大能力來加強現有基礎設施，以承受更高交易量對其系統造成的壓力。儘管這對金融科技公司來說似乎具有課題性，但此類行動對人工智慧解決方案提出了巨大的需求，因為這些公司的收入依賴交易量。這些因素預計將帶動金融科技市場對人工智慧解決方案的需求。

主要亮點

  • 金融公司是大型電腦和關聯式資料庫的早期採用者。他們熱切地等待著更高水準的運算能力。人工智慧 (AI) 透過在更廣泛的範圍內應用源自人類智慧方面的方法來改善結果。過去幾年的計算軍備競賽徹底改變了金融科技公司。機器學習、人工智慧、神經網路、巨量資料分析、演化演算法等技術使電腦能夠處理比以往任何時候都龐大、多樣、多樣且深入的資料集。
  • 此外，人工智慧和機器學習使銀行和金融科技受益，因為它們可以處理大量有關客戶的資訊。然後對這些資料和資訊進行比較，以獲得有關客戶想要的及時服務/產品的結果，這從本質上有助於發展客戶關係。
  • 此外，機器學習正在以前所未有的速度被採用，特別是用於創建傾向模型。銀行和保險公司正在為網路和行動應用程式引入基於機器學習的解決方案。透過根據即時行為資料預測客戶的產品傾向，進一步增強了即時目標行銷。
  • 一些市場現有企業正在透過明確提供解決方案來建立利基市場，例如銀行業的人工智慧聊天機器人。例如，2021 年 6 月，Talisma 和 Active.Ai 合作，使用支援對話 AI 的聊天機器人來改善 BFSI 的客戶體驗。
  • 此外，一些信用卡公司在其現有的詐欺偵測工作流程中實施了預測分析，以減少誤報。研究市場進一步吸引了一些為信用卡公司和其他金融機構提供基於人工智慧的反洗錢 (AML) 和詐欺檢測解決方案的參與者。
  • 例如，2022 年 6 月，人工智慧驅動的反洗錢 (AML) 軟體開發商 Lucinity 與詐欺管理公司 SEON 合作，在 AML 合規軟體中包含即時詐欺預防功能。 SEON的詐欺預防解決方案將透過Lucinity的平台提供，為客戶提供從交易監控到即時詐欺偵測和預防的合規風險服務。
  • 此外，人工智慧就緒的基礎設施應該能夠進行高效的資料管理，具有足夠的處理能力，敏捷、靈活和可擴展，並且能夠容納不同數量的資料。因此，對於金融科技小型企業來說，組裝必要的硬體和軟體元素來支援人工智慧將更具課題性。此外，隨著人工智慧和深度學習應用程式民主化的擴大，不僅對科技巨頭而言，而且對中小型企業來說也是可行的。對人工智慧專業人員完成這項工作的需求也在激增，而訓練有素的資源的稀缺是人工智慧在金融科技領域面臨的主要課題。

人工智慧在金融科技市場趨勢的應用

詐欺檢測預計將顯著成長

  • 人工智慧可以幫助確定快速有效的方法來檢測金融詐欺和不當行為。它們允許機器準確地處理龐大的資料集，而人們有時會遇到困難。使用人工智慧進行詐欺檢測具有多種優勢。快速運算能力是人工智慧和機器學習的眾所周知的優勢。它可以掌握用戶的應用程式使用習慣，例如交易方式、付款方式等，從而能夠即時發現異常情況。它減少了誤報，並允許專家專注於更複雜的問題，因為它比手動技術更有效。
  • 根據認證詐欺審查員 (ACFE) 和分析先驅 SAS 進行的一項新民意調查，去年國際上使用人工智慧 (AI) 和機器學習 (ML) 進行詐欺檢測的情況有所增加。調查顯示，13% 的組織採用人工智慧 (AI) 和機器學習來偵測和阻止欺詐，另有 25% 的組織計劃在未來一兩年內這樣做，增幅約為 200%。根據這項民意調查，詐欺審查人員發現這項跨產業的反詐欺技術和其他反詐欺技術正在廣泛傳播。
  • 此外，印度儲備銀行 (RBI) 報告稱，2022 會計年度印度各地發生了約 9,103 起銀行詐欺事件。這一數字比前一年有所增加，扭轉了過去十年的趨勢。銀行詐騙總價值從 1.38 兆印度盧比下降至 6,040 億印度盧比。銀行詐欺案件的如此大量增加將使人工智慧市場參與者能夠開發新的解決方案或工具來滿足客戶的廣泛需求。
  • 市場上的參與者正在合作，為客戶提供更好的服務。例如，2023 年 2 月，萬事達卡與中東和非洲首屈一指的數位商務供應商 Network International 合作，解決詐欺、拒絕付款和退款問題，以最大限度地降低收單機構的成本和風險。透過此次合作，Network 將在整個地區推出萬事達卡的 Brighterion 人工智慧 (AI) 技術，為收單機構和企業提供交易詐欺篩選和商家監控。
  • 此外，2022 年3 月，為全球保險業提供人工智慧驅動的決策自動化和最佳化解決方案的提供者Shift Technology 與為財產和意外傷害保險行業提供技術解決方案的全球提供商Duck Creek Technologies 宣佈建立解決方案合作夥伴關係，以支援人工智慧的詐欺偵測功能將於 2022 年上市。完全整合後，Duck Creek Claims 用戶將直接在其索賠管理軟體系統中收到即時詐欺警報。

北美佔據最大的市場佔有率

  • 由於著名的人工智慧軟體和系統供應商、金融機構對人工智慧專案的聯合投資以及大多數人工智慧在金融科技解決方案中的採用，北美預計將主導金融科技市場的人工智慧。預計該地區在未來幾年將在這一領域實現顯著成長。此外，北美是許多人工智慧金融科技公司的業務中心，Sidetrade 等公司選擇將其北美業務設在卡加利。
  • 政府對人工智慧的措施和投資。例如，會推動市場。史丹佛大學最近的一項研究資料顯示，2022 會計年度，美國政府在人工智慧 (AI) 合約上花費了 33 億美元。聯邦政府機構的技術支出從 2021 年的 27 億美元每年成長超過 6 億美元，其中決策科學、電腦視覺和自主領域獲得了大部分投資。自 2017 年美國政府在人工智慧技術上花費 13 億美元以來，人工智慧合約總支出已成長超過 2.5 倍。
  • 市場參與者正在合作，為該地區的客戶提供更好的服務。例如，2022 年 8 月，NACUSO CUSO 年度獎得主、透過更好的評分改善信貸准入的參與者 Zest AI 宣布與全球資料、分析和技術公司 Equifax, Inc. 建立合作夥伴關係。此次合作將使信用社能夠使用 Zest AI 的核保技術來分析更多 Equifax 來源的資料，從而以更快的速度接受更多申請，特別是那些傳統上銀行服務不足的申請。這是 Zest AI 與國家消費者報告機構的第一個大型分銷關係。
  • 一些公司的解決方案可以幫助企業透過下一代最佳行動軟體發展零售銀行業務，檢測和打擊金融欺詐，並透過多通路客戶體驗解決方案改善客戶關係。例如，2022 年 4 月，協作應收帳款領域的參與者 Versapay 今天表示，已完成對美國金融科技新創公司 DadeSystems 的收購。此次收購後，Versapay 的一系列應收帳款 (AR) 自動化解決方案及其人工智慧和機器學習功能得到了擴展。它還擴大了 Versapay 的企業和中端市場足跡，同時為其不斷成長的員工增加了關鍵技能。
  • 該地區的銀行已開始使用區塊鏈技術來記錄資料並打擊詐欺。區塊鏈記錄每筆交易的詳細資訊，從而更容易檢測駭客企圖。該技術允許在全球範圍內進行支付，並允許以較低的佣金進行快速交易。區塊鏈的分散式帳本技術（DLT）有助於跨不同商店和分散式網路記錄和共享資料。此外，加密和演算法方法可以跨金融網路同步資料。這是重要的一步，因為交易資料可以儲存在不同的位置。它為區塊鏈互通性和跨行業資料交換鋪平了道路。

人工智慧在金融科技產業概述

由於全球參與者眾多，金融科技市場的人工智慧正走向分散化。大公司的各種收購和合作預計很快就會發生，重點是創新。市場上一些主要的參與者包括 IBM 公司、英特爾公司、Narrative Science 和微軟公司。

2023 年 2 月，汶萊的 Baiuri 銀行選擇了新加坡軟體即服務 (SaaS) 金融科技公司 Finbots.ai，透過人工智慧 (AI) 實現信用風險管理現代化。據Finbots.ai稱，其人工智慧信用建模解決方案creditX將使Baiduri Bank能夠以極少的時間和成本設計和部署高品質的信用記分卡。這將最大限度地降低信貸風險，提高零售和中小型組織（SME）的效率和敏捷性，並加快銀行針對服務不足的信貸市場的普惠金融活動。

2023 年 2 月，豐業銀行推出了新工具 Scotia Smart Investor，為客戶提供更好的資產控制。這家加拿大銀行透過 Assistance+ 推出了這款新設備，將人工智慧驅動的建議與即時個人化幫助相結合。 Scotia Smart Investor 由豐業銀行關聯的共同基金交易商豐業證券創建。該工具包括人工智慧驅動的建議引擎，將幫助用戶設計、規劃、監控和更新財務目標。

額外的好處：

  • Excel 格式的市場估算 (ME) 表
  • 3 個月的分析師支持

目錄

第 1 章：簡介

  • 研究假設和市場定義
  • 研究範圍

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 供應商的議價能力
    • 買家的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭激烈程度
  • 人工智慧在金融科技的新興應用
  • 技術簡介
  • COVID-19 對市場的影響

第 5 章：市場動態

  • 市場促進因素
    • 金融組織對流程自動化的需求不斷增加
    • 提高資料來源的可用性
  • 市場限制
    • 需要熟練的勞動力

第 6 章：市場區隔

  • 依類型
    • 解決方案
    • 服務
  • 依部署
    • 雲
    • 本地部署
  • 依應用
    • 聊天機器人
    • 信用評分
    • 量化與資產管理
    • 詐欺識別
    • 其他應用
  • 依地理
    • 北美洲
    • 歐洲
    • 亞太地區
    • 世界其他地區

第 7 章：競爭格局

  • 公司簡介
    • IBM Corporation
    • Intel Corporation
    • ComplyAdvantage.com
    • Narrative Science
    • Amazon Web Services Inc.
    • IPsoft Inc.
    • Next IT Corporation
    • Microsoft Corporation
    • Onfido
    • Ripple Labs Inc.
    • Active.Ai
    • TIBCO Software (Alpine Data Labs)
    • Trifacta Software Inc.
    • Data Minr Inc.
    • Zeitgold
    • Sift Science Inc.
    • Pefin Holdings LLC
    • Betterment Holdings
    • WealthFront Inc.

第 8 章：投資分析

第 9 章：市場的未來

簡介目錄
Product Code: 61424

The AI in Fintech Market size is estimated at USD 44.08 billion in 2024, and is expected to reach USD 50.87 billion by 2029, growing at a CAGR of 2.91% during the forecast period (2024-2029).

AI in Fintech - Market

The COVID-19 pandemic outbreak has been accelerating the change in the way how people interact with financial services. Payment- and wealth-focused fintech companies have focused on bolstering their existing infrastructure by investing in new resources or expanding capacity to withstand the stress to their systems from higher transaction volumes. Though it seemed challenging for fintech companies, such actions have provided a significant need for AI solutions as these companies depend on transaction volumes for revenue. Such factors are expected to spearhead the demand for AI solutions in the fintech market.

Key Highlights

  • Financial firms have been the early adopters of the mainframe computer and relational database. They eagerly waited for the next level of computational power. Artificial Intelligence (AI) improves results by applying methods derived from the aspects of human intelligence at a broader scale. The computational arms race for past years has revolutionized fintech companies. Technologies, such as machine learning, AI, neural networks, Big Data Analytics, evolutionary algorithms, and much more, have allowed computers to crunch huge, varied, diverse, and deep datasets than ever before.
  • Moreover, AI and machine learning have benefited banks and fintech as they can process vast amounts of information about customers. This data and information are then compared to obtain results about timely services/products that customers want, which has aided, essentially, in developing customer relations.
  • Additionally, machine learning is being adopted at unprecedented rates, specifically to create propensity models. Banks and insurance companies are introducing machine learning-based solutions for web and mobile applications. This has further enhanced the real-time target marketing by predicting the product propensity of the customers based on behavioral data in real-time.
  • Several market incumbents are establishing a niche by explicitly offering solutions, like AI Chatbots for banking. For instance, in June 2021, Talisma and Active.Ai has partnered to enable improved customer experience in BFSI using conversation AI enabled Chatbot.
  • Moreover, several credit card companies implement predictive analytics into their existing fraud detection workflows to reduce false positives. The studied market further gains traction with several players offering AI-based Anti-money Laundering (AML) and Fraud detection solutions for credit card companies and other financial institutions.
  • For instance, in June 2022, Lucinity, a developer of AI-driven anti-money laundering (AML) software has partnered with fraud management company SEON to include real time fraud prevention capabilities in AML compliance software. SEON's fraud prevention solution will be available through Lucinity's platform, providing customers with compliance risk services from transaction monitoring to real-time fraud detection and prevention.
  • Further, AI-ready infrastructure should be capable of efficient data management, have enough processing power, be agile, flexible, and scalable, and have the capacity to accommodate different volumes of data. Therefore, it would be more challenging for fintech small businesses to assemble the necessary hardware and software elements to support AI. Moreover, as the democratization of AI and deep learning applications expands, not only for tech giants but is now viable for small and medium-sized businesses. The demand for AI professionals to do the work has ballooned as well, and the scarcity of trained resources is the major challenge for AI in fintech.

AI in Fintech Market Trends

Fraud Detection is Expected to Witness Significant Growth

  • Artificial intelligence can assist in identifying rapid and effective ways to detect financial fraud and malpractice. They allow machines to process enormous datasets accurately, which people sometimes struggle with. Using artificial intelligence for fraud detection has various advantages. The ability to compute quickly is a well-known benefit of AI and machine learning. It creates a grasp of a user's app usage habits, such as transaction methods, payments, and so on, allowing it to spot anomalies in real-time. It reduces false positives and allows specialists to focus on more complex issues because it is more efficient than manual techniques.
  • According to a new poll conducted by Certified Fraud Examiners (ACFE) and analytics pioneer SAS, the use of Artificial Intelligence (AI) and Machine Learning (ML) for fraud detection increased internationally last year. According to the poll, 13% of organizations employ artificial intelligence (AI) and machine learning to detect and deter fraud, with another 25% planning to do so in the next year or two, representing roughly 200% growth. According to the poll, fraud examiners identified this and other anti-fraud tech developments in a cross-industry that are extensively spreading.
  • Further, the Reserve Bank of India (RBI) reported around 9,103 bank fraud incidents across India in fiscal year 2022. This increased over the previous year, reversing the last decade's trend. The total value of bank scams fell from INR 1.38 trillion to INR 604 billion. Such high rise in the bank fraud cases would allow the AI market players to develop new solutions or tools to cater wide range of needs of the customer.
  • The players in the market are collobarting to provide better service to its customer. For instance, in february 2023, Mastercard partnered with Network International, the Middle East and Africa's premier provider of digital commerce, to address fraud, declines, and chargebacks to minimise costs and risk for acquirers. Through the collaboration, Network will roll out Mastercard's Brighterion Artificial Intelligence (AI) technology across the region, providing acquirers and businesses with transaction fraud screening and merchant monitoring.
  • Further, in March 2022, Shift Technology, a provider of AI-driven decision automation and optimisation solutions for the global insurance industry, and Duck Creek Technologies, a global provider of technology solutions to the P&C insurance industry, have announced a solution partnership to bring AI-enabled fraud detection capabilities to market in 2022. Once fully integrated, Duck Creek Claims users will receive real-time fraud alerts directly into their claims management software system.

North America Accounts For the Largest Market Share

  • North America is expected to dominate the AI in Fintech market due to prominent AI software and systems suppliers, combined investment by financial institutions into AI projects, and the adoption of most AI in Fintech solutions. The region is expected to experience significant growth in this area in the coming years. Additionally, North America serves as the business hub for many AI Fintech firms, with companies like Sidetrade choosing to locate their North American operations in Calgary.
  • Government initiatives and investments towards AI. would drive the market for instance. In fiscal year 2022, the U.S. government spent USD 3.3 billion on artificial intelligence (A.I.) contracts, according to data from a recent Stanford University study. Spending by federal government agencies on technology climbed by over USD 600 million annually, from USD 2.7 billion in 2021, with the decision science, computer vision, and autonomous segments receiving the majority of investment. Since 2017, when the U.S. government spent USD 1.3 billion on artificial technology, total spending on A.I. contracts has climbed by over 2.5 times.
  • The players in the market are collobarting to provide better service to the customer in the region. For instance, in august 2022, Zest AI, the recipient of NACUSO's CUSO of the Year Award and a player in improving credit access through better scoring announced a partnership with Equifax, Inc., a worldwide data, analytics, and technology firm. The collaboration will allow credit unions that use Zest AI's underwriting technology to analyze more of the data sourced by Equifax to accept more applications with better speed, particularly those who have traditionally been underbanked. This is Zest AI's first big distribution relationship with a National Consumer Reporting Agency.
  • Some companies' solutions help businesses grow retail banking with next-best-action software, detect and combat financial fraud, and improve client relationships with multichannel customer experience solutions. For insatnce, in April 2022, Versapay, a player in Collaborative Accounts Receivable, said today that it has finalised its acquisition of DadeSystems, a fintech startup based in the United States. Versapay's array of accounts receivable (AR) automation solutions has been expanded, as have its AI and machine learning capabilities, as a result of the acquisition. It also broadens Versapay's enterprise and mid-market footprint while adding critical skills to its growing staff.
  • Banks in the region have started using blockchain technology to record data and combat fraud. Blockchain records the details of each transaction, making it easier to detect hacker attempts This technology permits worldwide payments and allows for speedy transactions with low commissions. The Distributed Ledger Technology (DLT) of Blockchain assists in the recording and sharing data across different stores and a distributed network. Furthermore, cryptographic and algorithmic methods synchronize data across the financial network. This is a significant step since transaction data can be stored in different locations. It paves the way for blockchain interoperability and cross-industry data exchange.

AI in Fintech Industry Overview

AI in the Fintech market is moving towards fragmented due to many global players. Various acquisitions and collaborations of large companies are expected to occur shortly, focusing on innovation. Some major players in the market include IBM Corporation, Intel Corporation, Narrative Science, and Microsoft Corporation.

In February 2023, Baiduri Bank in Brunei chose Singapore-based Software-as-a-Service (SaaS) fintech Finbots.ai to modernize its credit risk management with artificial intelligence (AI). According to Finbots.ai, its AI credit modeling solution, creditX, will allow Baiduri Bank to design and deploy high-quality credit scorecards in a fraction of the time and cost. This will minimize credit risk, increase efficiency and agility for retail and small and medium-sized organizations (SMEs), as well as expedite the bank's financial inclusion campaign for the underserved credit market.

In February 2023, Scotiabank introduced a new tool, Scotia Smart Investor, to give customers greater asset control. The Canadian lender introduced the new device via assistance+, combining AI-powered recommendations with real-time personalized assistance. Scotia Smart Investor was created by Scotia Securities, Scotiabank's linked mutual fund dealer. The tool, which includes an AI-powered advice engine, will assist users in designing, planning, monitoring, and updating financial goals.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Buyers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitutes
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Emerging Uses of AI in Financial Technology
  • 4.4 Technology Snapshot
  • 4.5 Impact of COVID-19 on the market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Demand For Process Automation Among Financial Organizations
    • 5.1.2 Increasing Availability of Data Sources
  • 5.2 Market Restraints
    • 5.2.1 Need for Skilled Workforce

6 MARKET SEGMENTATION

  • 6.1 By Type
    • 6.1.1 Solutions
    • 6.1.2 Services
  • 6.2 By Deployment
    • 6.2.1 Cloud
    • 6.2.2 On-premise
  • 6.3 By Application
    • 6.3.1 Chatbots
    • 6.3.2 Credit Scoring
    • 6.3.3 Quantitative & Asset Management
    • 6.3.4 Fraud Detection
    • 6.3.5 Other Applications
  • 6.4 By Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia Pacific
    • 6.4.4 Rest of the World

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 IBM Corporation
    • 7.1.2 Intel Corporation
    • 7.1.3 ComplyAdvantage.com
    • 7.1.4 Narrative Science
    • 7.1.5 Amazon Web Services Inc.
    • 7.1.6 IPsoft Inc.
    • 7.1.7 Next IT Corporation
    • 7.1.8 Microsoft Corporation
    • 7.1.9 Onfido
    • 7.1.10 Ripple Labs Inc.
    • 7.1.11 Active.Ai
    • 7.1.12 TIBCO Software (Alpine Data Labs)
    • 7.1.13 Trifacta Software Inc.
    • 7.1.14 Data Minr Inc.
    • 7.1.15 Zeitgold
    • 7.1.16 Sift Science Inc.
    • 7.1.17 Pefin Holdings LLC
    • 7.1.18 Betterment Holdings
    • 7.1.19 WealthFront Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET