銀行業務向け予測分析市場レポート：動向、予測、競合分析 (2031年まで)

世界の銀行業務向け予測分析市場の将来性は有望で、中小企業市場と大企業市場に機会がありそうです。世界の銀行業務向け予測分析市場は、2025年から2031年にかけてCAGR 20.6%で成長すると予測されます。この市場の主な促進要因は、AIを活用したアナリティクスの採用が増加していることと、不正検知ソリューションのニーズが高まっていることです。

• Lucintelの予測では、種類別では、顧客分析が予測期間中に最も高い成長を遂げる見込みです。
• 用途別では、中小企業が高い成長を遂げる見込みです。
• 地域別では、アジア太平洋が予測期間中に最も高い成長が見込まれます。

銀行業務向け予測分析市場の新たな動向

銀行業務向け予測分析業界は今日、銀行の顧客理解、リスク処理、業務推進方法を再定義する様々な主要動向の影響を受けています。これらの動向は、最新のテクノロジーとますます膨大になるデータを活用しています。

• リアルタイム予測分析：即座の融資実行、取引中の詐欺警告、顧客とのエンゲージメントの瞬間にターゲットを絞った提案など、即座の意思決定を行い、顧客体験を向上させ、リスクを低減するために、銀行はリアルタイム予測分析を急速に取り入れています。このような瞬間的な性質は、レスポンスと顧客の喜びを高めます。
• 信頼と透明性を育む説明可能なAI：より洗練されたAIモデルの応用が進むにつれ、予測がどのように導かれたかを洞察できる説明可能なAIに対する需要が高まっています。これは、規制上のニーズ、顧客の信頼、銀行業務における自動化された意思決定を人間が監視する能力にとって不可欠です。
• 共同データ分析のための統合学習：銀行は、データ・プライバシーの問題や規制上の障壁を克服するために、統合学習を検討しています。統合学習は、複数の金融機関が機密性の高い顧客データを交換することなくAIモデルを共同で学習することを可能にし、より包括的で強固な予測洞察を促進します。共同学習方式はデータのプライバシーを保護します。
• 自然言語処理の組み込み：顧客サービスコール、ソーシャルメディア、ニュースフィードなど、従来とは異なるソースからの非構造化データを分析し、顧客の態度、顕在化しつつあるリスク、市場開拓動向をより深く理解し、予測力を高めるために、銀行が自然言語処理（NLP）を利用するケースが増えています。これにより、従来とは異なるソースから豊富な情報が得られるようになります。
• パーソナライズされたファイナンシャル・ウェルネスのための予測分析：レガシー・バンキング商品とは別に、パーソナライズされたファイナンシャル・ウェルネス・ガイダンス、予算管理機能、積極的な提案を提供するために予測分析を活用する新しいトレンドがあります。これはトランザクショナル・バンキングの域を超えています。

これらの動向は総体的に、銀行業務向け予測分析市場を、より良い意思決定を促進し、全体的なバンキング体験を向上させる、よりリアルタイムで透明性の高い、協調的で顧客中心のソリューションへと変化させつつあります。

銀行業務向け予測分析市場の最近の動向

今日の銀行業務向け予測分析業界は、精度や効率を最大化し、データ利用の倫理的要素を考慮することを目的とした重要な進化を遂げています。この進歩は、銀行が競争力を獲得し、消費者から信頼を得るのに役立っています。その推進力は、責任と大きな影響力を持つAIに向けられています。

• モデルの迅速な展開を促進するAutoMLプラットフォームにおける新たな革新：AutoMLプラットフォームは飛躍的に開発が進んでおり、銀行がより少ない人的労力でより迅速に予測モデルを開発できるようになり、多くの銀行機能でアナリティクスの迅速な導入が促進されています。
• フィーチャーエンジニアリングと選択の重視：銀行は、データから有用なシグナルを引き出すための高度なフィーチャーエンジニアリング手法や、予測モデルの精度と解釈可能性を高めるための高度なフィーチャー選択手法に、より多くの資金を投入しています。
• 強力なモデルモニタリングとガバナンスモデルの開発：変化し続ける顧客データと行動の性質を理解し、銀行は予測モデルのパフォーマンスを常時監視するための強力なモデルを開発し、バイアスを制御して長期にわたって精度を維持するためのガバナンスを構築しています。
• グラフデータベースの統合によるリレーションシップ分析の向上：銀行は、不正検知や信用リスク分析においてより正確な予測を行うため、顧客ネットワークや取引パターンなど、データ内の複雑な関係をより的確に分析するためのグラフデータベースの利用を増やしています。
• プライバシーを保護するAI手法への注目：データプライバシーに関する法律が強化される中、銀行は顧客データを損なうことなく予測分析にデータを利用するため、ディファレンシャル・プライバシーやホモモーフィック暗号化など、プライバシーを保護するAI手法を採用・統合しています。

これらの動向は、より正確で信頼できるモデルの迅速な展開、複雑なデータ関係のより良い理解、倫理とプライバシーを重視したデータ利用を促進することで、市場における銀行の予測分析に影響を与えています。

目次

第1章 エグゼクティブサマリー

第2章 市場概要

• 背景と分類
• サプライチェーン

第3章 市場動向と予測分析

• マクロ経済の動向と予測
• 業界の促進要因と課題
• PESTLE分析
• 特許分析
• 規制環境

第4章 世界の銀行業務向け予測分析市場：種類別

• 概要
• 魅力分析：種類別
• 顧客分析：動向と予測（2019～2031年）
• ホワイトカラー自動化：動向と予測（2019～2031年）
• 信用スコアリング：動向と予測（2019～2031年）
• 取引分析：動向と予測（2019～2031年）
• その他：動向と予測（2019～2031年）

第5章 世界の銀行業務向け予測分析市場：用途別

• 概要
• 魅力分析：用途別
• 中小企業：動向と予測（2019～2031年）
• 大企業：動向と予測（2019～2031年）

第6章 地域分析

• 概要
• 世界の銀行業務向け予測分析市場：地域別

第7章 北米の銀行業務向け予測分析市場

• 概要
• 北米の銀行業務向け予測分析市場：種類別
• 北米の銀行業務向け予測分析市場：用途別
• 米国の銀行業務向け予測分析市場
• メキシコの銀行業務向け予測分析市場
• カナダの銀行業務向け予測分析市場

第8章 欧州の銀行業務向け予測分析市場

• 概要
• 欧州の銀行業務向け予測分析市場：種類別
• 欧州の銀行業務向け予測分析市場：用途別
• ドイツの銀行業務向け予測分析市場
• フランスの銀行業務向け予測分析市場
• スペインの銀行業務向け予測分析市場
• イタリアの銀行業務向け予測分析市場
• 英国の銀行業務向け予測分析市場

第9章 アジア太平洋の銀行業務向け予測分析市場

• 概要
• アジア太平洋の銀行業務向け予測分析市場：種類別
• アジア太平洋の銀行業務向け予測分析市場：用途別
• 日本の銀行業務向け予測分析市場
• インドの銀行業務向け予測分析市場
• 中国の銀行業務向け予測分析市場
• 韓国の銀行業務向け予測分析市場
• インドネシアの銀行業務向け予測分析市場

第10章 その他の地域 (ROW) の銀行業務向け予測分析市場

• 概要
• その他の地域 (ROW) の銀行業務向け予測分析市場：種類別
• その他の地域 (ROW) の銀行業務向け予測分析市場：用途別
• 中東の銀行業務向け予測分析市場
• 南米の銀行業務向け予測分析市場
• アフリカの銀行業務向け予測分析市場

第11章 競合分析

• 製品ポートフォリオ分析
• 運用統合
• ポーターのファイブフォース分析
  • 競争企業間の敵対関係
  • バイヤーの交渉力
  • サプライヤーの交渉力
  • 代替品の脅威
  • 新規参入業者の脅威
• 市場シェア分析

第12章 機会と戦略分析

• バリューチェーン分析
• 成長機会分析
  • 成長機会：種類別
  • 成長機会：用途別
• 世界の銀行業務向け予測分析市場の新たな動向
• 戦略分析
  • 新製品開発
  • 認証・ライセンシング
  • 企業合併・買収 (M&A) 、契約、提携、合弁事業

第13章 バリューチェーン上の主要企業のプロファイル

• Competitive Analysis
• Accretive Technologies
• Angoss Software Corporation
• FICO
• HP
• IBM
• Information Builders
• KXEN
• Microsoft
• Oracle
• Salford Systems

第14章 付録

• 図の一覧
• 表の一覧
• 分析手法
• 免責事項
• 著作権
• 略語と技術単位
• Lucintelについて
• お問い合わせ

List of Figures

• Figure 1.1: Trends and Forecast for the Global Predictive Analytics in Banking Market
• Figure 2.1: Usage of Predictive Analytics in Banking Market
• Figure 2.2: Classification of the Global Predictive Analytics in Banking Market
• Figure 2.3: Supply Chain of the Global Predictive Analytics in Banking Market
• Figure 2.4: Driver and Challenges of the Predictive Analytics in Banking Market
• Figure 3.1: Trends of the Global GDP Growth Rate
• Figure 3.2: Trends of the Global Population Growth Rate
• Figure 3.3: Trends of the Global Inflation Rate
• Figure 3.4: Trends of the Global Unemployment Rate
• Figure 3.5: Trends of the Regional GDP Growth Rate
• Figure 3.6: Trends of the Regional Population Growth Rate
• Figure 3.7: Trends of the Regional Inflation Rate
• Figure 3.8: Trends of the Regional Unemployment Rate
• Figure 3.9: Trends of Regional Per Capita Income
• Figure 3.10: Forecast for the Global GDP Growth Rate
• Figure 3.11: Forecast for the Global Population Growth Rate
• Figure 3.12: Forecast for the Global Inflation Rate
• Figure 3.13: Forecast for the Global Unemployment Rate
• Figure 3.14: Forecast for the Regional GDP Growth Rate
• Figure 3.15: Forecast for the Regional Population Growth Rate
• Figure 3.16: Forecast for the Regional Inflation Rate
• Figure 3.17: Forecast for the Regional Unemployment Rate
• Figure 3.18: Forecast for Regional Per Capita Income
• Figure 4.1: Global Predictive Analytics in Banking Market by Type in 2019, 2024, and 2031
• Figure 4.2: Trends of the Global Predictive Analytics in Banking Market ($B) by Type
• Figure 4.3: Forecast for the Global Predictive Analytics in Banking Market ($B) by Type
• Figure 4.4: Trends and Forecast for Customer Analytics in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 4.5: Trends and Forecast for White-Collar Automation in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 4.6: Trends and Forecast for Credit Scoring in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 4.7: Trends and Forecast for Trading Insight in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 4.8: Trends and Forecast for Others in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 5.1: Global Predictive Analytics in Banking Market by Application in 2019, 2024, and 2031
• Figure 5.2: Trends of the Global Predictive Analytics in Banking Market ($B) by Application
• Figure 5.3: Forecast for the Global Predictive Analytics in Banking Market ($B) by Application
• Figure 5.4: Trends and Forecast for Small & Medium Enterprises in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 5.5: Trends and Forecast for Large Enterprises in the Global Predictive Analytics in Banking Market (2019-2031)
• Figure 6.1: Trends of the Global Predictive Analytics in Banking Market ($B) by Region (2019-2024)
• Figure 6.2: Forecast for the Global Predictive Analytics in Banking Market ($B) by Region (2025-2031)
• Figure 7.1: Trends and Forecast for the North American Predictive Analytics in Banking Market (2019-2031)
• Figure 7.2: North American Predictive Analytics in Banking Market by Type in 2019, 2024, and 2031
• Figure 7.3: Trends of the North American Predictive Analytics in Banking Market ($B) by Type (2019-2024)
• Figure 7.4: Forecast for the North American Predictive Analytics in Banking Market ($B) by Type (2025-2031)
• Figure 7.5: North American Predictive Analytics in Banking Market by Application in 2019, 2024, and 2031
• Figure 7.6: Trends of the North American Predictive Analytics in Banking Market ($B) by Application (2019-2024)
• Figure 7.7: Forecast for the North American Predictive Analytics in Banking Market ($B) by Application (2025-2031)
• Figure 7.8: Trends and Forecast for the United States Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 7.9: Trends and Forecast for the Mexican Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 7.10: Trends and Forecast for the Canadian Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 8.1: Trends and Forecast for the European Predictive Analytics in Banking Market (2019-2031)
• Figure 8.2: European Predictive Analytics in Banking Market by Type in 2019, 2024, and 2031
• Figure 8.3: Trends of the European Predictive Analytics in Banking Market ($B) by Type (2019-2024)
• Figure 8.4: Forecast for the European Predictive Analytics in Banking Market ($B) by Type (2025-2031)
• Figure 8.5: European Predictive Analytics in Banking Market by Application in 2019, 2024, and 2031
• Figure 8.6: Trends of the European Predictive Analytics in Banking Market ($B) by Application (2019-2024)
• Figure 8.7: Forecast for the European Predictive Analytics in Banking Market ($B) by Application (2025-2031)
• Figure 8.8: Trends and Forecast for the German Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 8.9: Trends and Forecast for the French Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 8.10: Trends and Forecast for the Spanish Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 8.11: Trends and Forecast for the Italian Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 8.12: Trends and Forecast for the United Kingdom Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 9.1: Trends and Forecast for the APAC Predictive Analytics in Banking Market (2019-2031)
• Figure 9.2: APAC Predictive Analytics in Banking Market by Type in 2019, 2024, and 2031
• Figure 9.3: Trends of the APAC Predictive Analytics in Banking Market ($B) by Type (2019-2024)
• Figure 9.4: Forecast for the APAC Predictive Analytics in Banking Market ($B) by Type (2025-2031)
• Figure 9.5: APAC Predictive Analytics in Banking Market by Application in 2019, 2024, and 2031
• Figure 9.6: Trends of the APAC Predictive Analytics in Banking Market ($B) by Application (2019-2024)
• Figure 9.7: Forecast for the APAC Predictive Analytics in Banking Market ($B) by Application (2025-2031)
• Figure 9.8: Trends and Forecast for the Japanese Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 9.9: Trends and Forecast for the Indian Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 9.10: Trends and Forecast for the Chinese Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 9.11: Trends and Forecast for the South Korean Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 9.12: Trends and Forecast for the Indonesian Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 10.1: Trends and Forecast for the ROW Predictive Analytics in Banking Market (2019-2031)
• Figure 10.2: ROW Predictive Analytics in Banking Market by Type in 2019, 2024, and 2031
• Figure 10.3: Trends of the ROW Predictive Analytics in Banking Market ($B) by Type (2019-2024)
• Figure 10.4: Forecast for the ROW Predictive Analytics in Banking Market ($B) by Type (2025-2031)
• Figure 10.5: ROW Predictive Analytics in Banking Market by Application in 2019, 2024, and 2031
• Figure 10.6: Trends of the ROW Predictive Analytics in Banking Market ($B) by Application (2019-2024)
• Figure 10.7: Forecast for the ROW Predictive Analytics in Banking Market ($B) by Application (2025-2031)
• Figure 10.8: Trends and Forecast for the Middle Eastern Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 10.9: Trends and Forecast for the South American Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 10.10: Trends and Forecast for the African Predictive Analytics in Banking Market ($B) (2019-2031)
• Figure 11.1: Porter's Five Forces Analysis of the Global Predictive Analytics in Banking Market
• Figure 11.2: Market Share (%) of Top Players in the Global Predictive Analytics in Banking Market (2024)
• Figure 12.1: Growth Opportunities for the Global Predictive Analytics in Banking Market by Type
• Figure 12.2: Growth Opportunities for the Global Predictive Analytics in Banking Market by Application
• Figure 12.3: Growth Opportunities for the Global Predictive Analytics in Banking Market by Region
• Figure 12.4: Emerging Trends in the Global Predictive Analytics in Banking Market

List of Tables

• Table 1.1: Growth Rate (%, 2023-2024) and CAGR (%, 2025-2031) of the Predictive Analytics in Banking Market by Type and Application
• Table 1.2: Attractiveness Analysis for the Predictive Analytics in Banking Market by Region
• Table 1.3: Global Predictive Analytics in Banking Market Parameters and Attributes
• Table 3.1: Trends of the Global Predictive Analytics in Banking Market (2019-2024)
• Table 3.2: Forecast for the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.1: Attractiveness Analysis for the Global Predictive Analytics in Banking Market by Type
• Table 4.2: Market Size and CAGR of Various Type in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.3: Market Size and CAGR of Various Type in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.4: Trends of Customer Analytics in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.5: Forecast for Customer Analytics in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.6: Trends of White-Collar Automation in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.7: Forecast for White-Collar Automation in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.8: Trends of Credit Scoring in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.9: Forecast for Credit Scoring in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.10: Trends of Trading Insight in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.11: Forecast for Trading Insight in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 4.12: Trends of Others in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 4.13: Forecast for Others in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 5.1: Attractiveness Analysis for the Global Predictive Analytics in Banking Market by Application
• Table 5.2: Market Size and CAGR of Various Application in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 5.3: Market Size and CAGR of Various Application in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 5.4: Trends of Small & Medium Enterprises in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 5.5: Forecast for Small & Medium Enterprises in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 5.6: Trends of Large Enterprises in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 5.7: Forecast for Large Enterprises in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 6.1: Market Size and CAGR of Various Regions in the Global Predictive Analytics in Banking Market (2019-2024)
• Table 6.2: Market Size and CAGR of Various Regions in the Global Predictive Analytics in Banking Market (2025-2031)
• Table 7.1: Trends of the North American Predictive Analytics in Banking Market (2019-2024)
• Table 7.2: Forecast for the North American Predictive Analytics in Banking Market (2025-2031)
• Table 7.3: Market Size and CAGR of Various Type in the North American Predictive Analytics in Banking Market (2019-2024)
• Table 7.4: Market Size and CAGR of Various Type in the North American Predictive Analytics in Banking Market (2025-2031)
• Table 7.5: Market Size and CAGR of Various Application in the North American Predictive Analytics in Banking Market (2019-2024)
• Table 7.6: Market Size and CAGR of Various Application in the North American Predictive Analytics in Banking Market (2025-2031)
• Table 7.7: Trends and Forecast for the United States Predictive Analytics in Banking Market (2019-2031)
• Table 7.8: Trends and Forecast for the Mexican Predictive Analytics in Banking Market (2019-2031)
• Table 7.9: Trends and Forecast for the Canadian Predictive Analytics in Banking Market (2019-2031)
• Table 8.1: Trends of the European Predictive Analytics in Banking Market (2019-2024)
• Table 8.2: Forecast for the European Predictive Analytics in Banking Market (2025-2031)
• Table 8.3: Market Size and CAGR of Various Type in the European Predictive Analytics in Banking Market (2019-2024)
• Table 8.4: Market Size and CAGR of Various Type in the European Predictive Analytics in Banking Market (2025-2031)
• Table 8.5: Market Size and CAGR of Various Application in the European Predictive Analytics in Banking Market (2019-2024)
• Table 8.6: Market Size and CAGR of Various Application in the European Predictive Analytics in Banking Market (2025-2031)
• Table 8.7: Trends and Forecast for the German Predictive Analytics in Banking Market (2019-2031)
• Table 8.8: Trends and Forecast for the French Predictive Analytics in Banking Market (2019-2031)
• Table 8.9: Trends and Forecast for the Spanish Predictive Analytics in Banking Market (2019-2031)
• Table 8.10: Trends and Forecast for the Italian Predictive Analytics in Banking Market (2019-2031)
• Table 8.11: Trends and Forecast for the United Kingdom Predictive Analytics in Banking Market (2019-2031)
• Table 9.1: Trends of the APAC Predictive Analytics in Banking Market (2019-2024)
• Table 9.2: Forecast for the APAC Predictive Analytics in Banking Market (2025-2031)
• Table 9.3: Market Size and CAGR of Various Type in the APAC Predictive Analytics in Banking Market (2019-2024)
• Table 9.4: Market Size and CAGR of Various Type in the APAC Predictive Analytics in Banking Market (2025-2031)
• Table 9.5: Market Size and CAGR of Various Application in the APAC Predictive Analytics in Banking Market (2019-2024)
• Table 9.6: Market Size and CAGR of Various Application in the APAC Predictive Analytics in Banking Market (2025-2031)
• Table 9.7: Trends and Forecast for the Japanese Predictive Analytics in Banking Market (2019-2031)
• Table 9.8: Trends and Forecast for the Indian Predictive Analytics in Banking Market (2019-2031)
• Table 9.9: Trends and Forecast for the Chinese Predictive Analytics in Banking Market (2019-2031)
• Table 9.10: Trends and Forecast for the South Korean Predictive Analytics in Banking Market (2019-2031)
• Table 9.11: Trends and Forecast for the Indonesian Predictive Analytics in Banking Market (2019-2031)
• Table 10.1: Trends of the ROW Predictive Analytics in Banking Market (2019-2024)
• Table 10.2: Forecast for the ROW Predictive Analytics in Banking Market (2025-2031)
• Table 10.3: Market Size and CAGR of Various Type in the ROW Predictive Analytics in Banking Market (2019-2024)
• Table 10.4: Market Size and CAGR of Various Type in the ROW Predictive Analytics in Banking Market (2025-2031)
• Table 10.5: Market Size and CAGR of Various Application in the ROW Predictive Analytics in Banking Market (2019-2024)
• Table 10.6: Market Size and CAGR of Various Application in the ROW Predictive Analytics in Banking Market (2025-2031)
• Table 10.7: Trends and Forecast for the Middle Eastern Predictive Analytics in Banking Market (2019-2031)
• Table 10.8: Trends and Forecast for the South American Predictive Analytics in Banking Market (2019-2031)
• Table 10.9: Trends and Forecast for the African Predictive Analytics in Banking Market (2019-2031)
• Table 11.1: Product Mapping of Predictive Analytics in Banking Suppliers Based on Segments
• Table 11.2: Operational Integration of Predictive Analytics in Banking Manufacturers
• Table 11.3: Rankings of Suppliers Based on Predictive Analytics in Banking Revenue
• Table 12.1: New Product Launches by Major Predictive Analytics in Banking Producers (2019-2024)
• Table 12.2: Certification Acquired by Major Competitor in the Global Predictive Analytics in Banking Market

The future of the global predictive analytics in banking market looks promising with opportunities in the small & medium enterprise and large enterprise markets. The global predictive analytics in banking market is expected to grow with a CAGR of 20.6% from 2025 to 2031. The major drivers for this market are the rising adoption of AI-driven analytics, and the growing need for fraud detection solutions.

• Lucintel forecasts that, within the type category, customer analytics is expected to witness the highest growth over the forecast period.
• Within the application category, small & medium enterprise is expected to witness higher growth.
• In terms of region, APAC is expected to witness the highest growth over the forecast period.

Emerging Trends in the Predictive Analytics in Banking Market

The predictive analytics banking industry is today influenced by a range of key trends that are redefining how banks understand customers, handle risk, and drive their operations. These trends tap into the latest technologies and increasingly large pools of data.

• Real-Time Predictive Analytics: Banks are fast embracing real-time predictive analytics in order to take instant decisions like instant loan disbursements, fraud warnings in the middle of a transaction, and targeted offerings at the moment of engagement, improving customer experience and lowering risk. This in-the-moment nature enhances response and customer delight.
• Explainable AI for Fostering Trust and Transparency: As more sophisticated AI models find increased application, there is increasing demand for explainable AI that gives insight into how predictions were arrived at. This is imperative for regulatory needs, customer trust, and the ability to exercise human oversight of automated decisions within banking.
• Federated Learning for Collaborative Data Analysis: Banks are considering federated learning to overcome data privacy issues and regulatory barriers. Federated learning enables multiple institutions to train AI models jointly without exchanging sensitive customer data, facilitating more comprehensive and robust predictive insights. The collaborative method preserves data privacy.
• Incorporation of Natural Language Processing: NLP is more and more used by banks to analyze unstructured data from non-traditional sources such as customer service calls, social media, and news feeds to develop a better understanding of customer attitudes, emerging risk, and market trends, boosting predictive power. This opens up rich information from non-traditional sources.
• Predictive Analytics for Personalized Financial Wellness: Aside from legacy banking products, there's a new trend of utilizing predictive analytics to provide personalized financial wellness guidance, budgeting capabilities, and proactive suggestions to empower customers to better manage their finances, creating deeper customer relationships and loyalty. This is beyond transactional banking.

These trends collectively are transforming the predictive analytics in banking market into more real-time, transparent, collaborative, and customer-centric solutions that facilitate better decision-making and improve the overall banking experience.

Recent Developments in the Predictive Analytics in Banking Market

The predictive analytics in banking industry today is undergoing key advancements aimed at maximizing accuracy, efficiency, as well as considering ethical factors of using data. The advancements help the banks achieve competitiveness and obtain trust from consumers. The push is towards AI with responsible as well as significant impact.

• Emerging Innovations in AutoML Platforms Facilitating Quick Deployment of Models: AutoML platforms are advancing by leaps and bounds, making it possible for banks to develop predictive models faster using less human effort, driving quick adoption of analytics across many bank functions.
• Greater Emphasis on Feature Engineering and Selection: Banks are putting more money into sophisticated feature engineering methods to draw useful signals out of their data and using advanced feature selection techniques to enhance the accuracy and interpretability of their predictive models.
• Development of Strong Model Monitoring and Governance Models: Understanding the ever-changing nature of customer data and behavior, banks are developing strong models for constant monitoring of their predictive models' performance and governance to control bias and sustain accuracy over time.
• Graph Database Integration for Improved Relationship Analysis: Banks are increasingly using graph databases to better analyze intricate relationships in their data, including customer networks and patterns of transactions, to make more precise predictions in fraud detection and credit risk analysis.
• Focus on Privacy-Preserving AI Methods: As increasing data privacy laws, banks are adopting and integrating privacy-preserving AI methods, including differential privacy and homomorphic encryption, to use data for predictive analytics without compromising customer data.

These trends are influencing the banking predictive analytics in market by facilitating quicker deployment of more accurate and trustworthy models, better understanding of intricate data relationships, and focus on ethics and privacy-driven use of data.

Strategic Growth Opportunities in the Predictive Analytics in Banking Market

The predictive analytics in banking market has significant strategic growth opportunities across different applications based on the prospect of optimizing revenues, lowering costs, and improving customer relationships. Data-driven insights can revolutionize different aspects of banking operations.

• Improved Customer Acquisition and Retention: Predictive analytics can detect potential high-value customers and forecast churn risk, allowing banks to execute targeted marketing campaigns and proactive retention initiatives, resulting in higher market share and customer loyalty.
• Better Credit Risk Evaluation and Loan Origination: Using advanced predictive models to evaluate creditworthiness, predict default probabilities, and automate loan origination processes can result in better lending decisions and lower credit losses.
• Proactive Fraud Detection and Prevention: Predictive analytics in real-time can recognize unusual patterns in transactions and foresee fraudulent activities more accurately, keeping financial losses by the bank as well as customers to a bare minimum.
• Personalized Product Recommendations and Cross-Selling: Using predictive models, banks can comprehend individual customers' needs and likes and recommend very relevant products as well as opportunities for cross-selling, thus maximizing revenue and satisfaction.
• Optimized Branch Operations and Resource Planning: Predictive analytics can predict customer traffic, transaction levels, and branch staffing requirements, allowing for optimized resource planning, lower operational expenses, and enhanced customer service efficiency.

These strategic growth prospects demonstrate the value creation potential of predictive analytics throughout the banking value chain, from customer acquisition and retention to risk management and operation optimization, ultimately leading to profitability and competitiveness enhancement.

Predictive Analytics in Banking Market Driver and Challenges

Banking predictive analytics market is driven by a strong synergy of forces highlighting the growing prominence of data-informed decision-making in finance as well as having major challenges capable of limiting widespread and efficient usage. To tackle this dynamic developing landscape, appreciating these drivers is imperative.

The factors responsible for driving the predictive analytics in banking market include:

1. Exponential Growth in Volume and Variety of Data: The huge volumes of data created through banking transactions and customer interactions present a fertile ground for leveraging predictive analytics to extract valuable insights.

2. Improvements in Artificial Intelligence and Machine Learning: Ongoing improvements in AI and ML algorithms make it possible to create more complex and accurate predictive models for numerous banking applications.

3. Growing Regulatory Attention to Risk Management and Compliance: Regulatory demands for strengthening risk management, fraud prevention, and meeting anti-money laundering requirements propel predictive analytics adoption in the interest of better oversight.

4. Rising Customer Expectations of Personalized Services: Customers now increasingly demand personal and relevant financial products and services, which can be effectively offered by banks using predictive analytics.

5. Competitive Pressure from FinTech's and Digital-Native Banks: The emergence of nimble fintech firms and neobanks that use data analytics adds to the pressure on traditional banks to gain similar capabilities in order to be competitive.

Challenges in the predictive analytics in banking market are:

1. Data Privacy and Security Concerns: The confidential nature of financial information calls for severe data privacy and security protocols that make data access and use more challenging for predictive analytics.

2. Legacy IT Infrastructure and Data Silos: Most conventional banks are plagued by legacy IT systems and isolated data silos, which prevent smooth integration and analysis of data to support effective predictive modeling.

3. Lack of Qualified Data Scientists and Analysts: Insufficient experts with the right skills in data science, machine learning, and banking domain knowledge can slow the creation and deployment of sophisticated analytics solutions.

Strong forces of data growth, technology breakthroughs, and regulatory requirements are driving predictive analytics adoption in the banking sector. But to benefit fully from predictive analytics' disruptive power, it is essential that banks overcome barriers to data privacy, legacy, and talent onboarding.

List of Predictive Analytics in Banking Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. With these strategies predictive analytics in banking companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the predictive analytics in banking companies profiled in this report include-

• Accretive Technologies
• Angoss Software Corporation
• FICO
• HP
• IBM
• Information Builders
• KXEN
• Microsoft
• Oracle
• Salford Systems

Predictive Analytics in Banking Market by Segment

The study includes a forecast for the global predictive analytics in banking market by type, application, and region.

Predictive Analytics in Banking Market by Type [Value from 2019 to 2031]:

• Customer Analytics
• White-Collar Automation
• Credit Scoring
• Trading Insight
• Others

Predictive Analytics in Banking Market by Application [Value from 2019 to 2031]:

• Small & Medium Enterprises
• Large Enterprises

Predictive Analytics in Banking Market by Region [Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Predictive Analytics in Banking Market

The global predictive analytics in banking industry is increasingly using predictive analytics to better understand customer behavior, streamline operations, and manage risks. Advances in artificial intelligence, machine learning, and big data technologies over the past few years are powering major trends in how banks in leading economies are applying predictive analytics to improve their competitive advantage and respond to changing market conditions.

• United States: Emphasis on fraud detection and custom individual experiences. The latest innovations involve advanced AI-driven systems for real-time fraud detection and the application of prediction models in providing highly customized products and services to enhance customer retention and acquisition in a competitive marketplace.
• China: Accelerating adoption in digital banking and credit scoring. China's banks are fast embracing predictive analytics, specifically digital banking platforms for risk assessment, credit scoring for an extensive unbanked population, and targeted marketing in their expansive digital ecosystems.
• Germany: Regulatory compliance and risk management focus. Current developments in Germany center on using predictive analytics for more effective risk management, such as credit risk measurement and anti-money laundering initiatives, while meeting strict data privacy rules and compliance measures.
• India: Expansion of digital lending and financial inclusion programs. India is experiencing greater application of predictive analytics to the growing space of digital lending to determine creditworthiness and extend financial inclusion to underpenetrated markets, frequently relying on alternative sources of data.
• Japan: Customer retention and operational effectiveness in a saturated market. New trends in Japan highlight the deployment of predictive analytics to enhance customer retention in an established banking industry and operational efficiency through forecasting and resource management.

Features of the Global Predictive Analytics in Banking Market

• Market Size Estimates: Predictive analytics in banking market size estimation in terms of value ($B).
• Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.
• Segmentation Analysis: Predictive analytics in banking market size by type, application, and region in terms of value ($B).
• Regional Analysis: Predictive analytics in banking market breakdown by North America, Europe, Asia Pacific, and Rest of the World.
• Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the predictive analytics in banking market.
• Strategic Analysis: This includes M&A, new product development, and competitive landscape of the predictive analytics in banking market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the predictive analytics in banking market by type (customer analytics, white-collar automation, credit scoring, trading insight, and others), application (small & medium enterprises and large enterprises), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Market Overview

• 2.1 Background and Classifications
• 2.2 Supply Chain

3. Market Trends & Forecast Analysis

• 3.1 Macroeconomic Trends and Forecasts
• 3.2 Industry Drivers and Challenges
• 3.3 PESTLE Analysis
• 3.4 Patent Analysis
• 3.5 Regulatory Environment

4. Global Predictive Analytics in Banking Market by Type

• 4.1 Overview
• 4.2 Attractiveness Analysis by Type
• 4.3 Customer Analytics: Trends and Forecast (2019-2031)
• 4.4 White-Collar Automation: Trends and Forecast (2019-2031)
• 4.5 Credit Scoring: Trends and Forecast (2019-2031)
• 4.6 Trading Insight: Trends and Forecast (2019-2031)
• 4.7 Others: Trends and Forecast (2019-2031)

5. Global Predictive Analytics in Banking Market by Application

• 5.1 Overview
• 5.2 Attractiveness Analysis by Application
• 5.3 Small & Medium Enterprises: Trends and Forecast (2019-2031)
• 5.4 Large Enterprises: Trends and Forecast (2019-2031)

6. Regional Analysis

• 6.1 Overview
• 6.2 Global Predictive Analytics in Banking Market by Region

7. North American Predictive Analytics in Banking Market

• 7.1 Overview
• 7.2 North American Predictive Analytics in Banking Market by Type
• 7.3 North American Predictive Analytics in Banking Market by Application
• 7.4 United States Predictive Analytics in Banking Market
• 7.5 Mexican Predictive Analytics in Banking Market
• 7.6 Canadian Predictive Analytics in Banking Market

8. European Predictive Analytics in Banking Market

• 8.1 Overview
• 8.2 European Predictive Analytics in Banking Market by Type
• 8.3 European Predictive Analytics in Banking Market by Application
• 8.4 German Predictive Analytics in Banking Market
• 8.5 French Predictive Analytics in Banking Market
• 8.6 Spanish Predictive Analytics in Banking Market
• 8.7 Italian Predictive Analytics in Banking Market
• 8.8 United Kingdom Predictive Analytics in Banking Market

9. APAC Predictive Analytics in Banking Market

• 9.1 Overview
• 9.2 APAC Predictive Analytics in Banking Market by Type
• 9.3 APAC Predictive Analytics in Banking Market by Application
• 9.4 Japanese Predictive Analytics in Banking Market
• 9.5 Indian Predictive Analytics in Banking Market
• 9.6 Chinese Predictive Analytics in Banking Market
• 9.7 South Korean Predictive Analytics in Banking Market
• 9.8 Indonesian Predictive Analytics in Banking Market

10. ROW Predictive Analytics in Banking Market

• 10.1 Overview
• 10.2 ROW Predictive Analytics in Banking Market by Type
• 10.3 ROW Predictive Analytics in Banking Market by Application
• 10.4 Middle Eastern Predictive Analytics in Banking Market
• 10.5 South American Predictive Analytics in Banking Market
• 10.6 African Predictive Analytics in Banking Market

11. Competitor Analysis

• 11.1 Product Portfolio Analysis
• 11.2 Operational Integration
• 11.3 Porter's Five Forces Analysis
  • Competitive Rivalry
  • Bargaining Power of Buyers
  • Bargaining Power of Suppliers
  • Threat of Substitutes
  • Threat of New Entrants
• 11.4 Market Share Analysis

12. Opportunities & Strategic Analysis

• 12.1 Value Chain Analysis
• 12.2 Growth Opportunity Analysis
  • 12.2.1 Growth Opportunities by Type
  • 12.2.2 Growth Opportunities by Application
• 12.3 Emerging Trends in the Global Predictive Analytics in Banking Market
• 12.4 Strategic Analysis
  • 12.4.1 New Product Development
  • 12.4.2 Certification and Licensing
  • 12.4.3 Mergers, Acquisitions, Agreements, Collaborations, and Joint Ventures

13. Company Profiles of the Leading Players Across the Value Chain

• 13.1 Competitive Analysis
• 13.2 Accretive Technologies
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.3 Angoss Software Corporation
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.4 FICO
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.5 HP
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.6 IBM
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.7 Information Builders
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.8 KXEN
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.9 Microsoft
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.10 Oracle
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing
• 13.11 Salford Systems
  • Company Overview
  • Predictive Analytics in Banking Business Overview
  • New Product Development
  • Merger, Acquisition, and Collaboration
  • Certification and Licensing

14. Appendix

• 14.1 List of Figures
• 14.2 List of Tables
• 14.3 Research Methodology
• 14.4 Disclaimer
• 14.5 Copyright
• 14.6 Abbreviations and Technical Units
• 14.7 About Us
• 14.8 Contact Us