世界中の 500 人以上の金融サービスの専門家を対象に調査したデータを分析しましょう。2022 年の金融サービスにおける AI の状況を定義するトレンド、課題、機会をご覧ください。
膨大なデータセット。たえまなく変動する市場。リモートの労働力。インテリジェントなテクノロジは、現代の金融サービス業界の重要な課題に対処することができます。ディープラーニング、機械学習、自然言語処理 (NLP) など、NVIDIA の AI により、金融機関はリスク管理を強化し、データに基づいた意志決定やセキュリティを改善し、顧客体験を向上させることができます。
世界中の 500 人以上の金融サービスの専門家を対象に調査したデータを分析しましょう。2022 年の金融サービスにおける AI の状況を定義するトレンド、課題、機会をご覧ください。
この新しい E-book では、American Express、BNY Mellon、PayPal など、金融サービス業界のリーダーが不正防止に NLP を使用してコストを削減しています。その仕組みをご覧ください。
AI を利用してプロセスの最適化、リスクの軽減、コストの削減
を実現している金融機関や企業から学ぶ
イベントと最新情報
Conference & Training September 19 - 22 | Keynote September 20
Discover how financial institutions are using AI to enhance customer experiences, including personalizing interactions with recommendation engines, improving self-service with conversational AI, and making transactions more secure with fraud-detection models.
The world of finance is undergoing seismic change, with every process and touchpoint being transformed by technology. Banks face fundamental choices that will determine the role they play in the future. Deutsche Bank is overhauling its approach through focused bets on technologies such as cloud, AI, and machine learning, laying the foundation to seamlessly integrate its offerings into customer journeys. Hear from Bernd Leukert, Deutsche Bank’s management board member for technology, data and innovation, on how the bank is making moves to increase the value and convenience for customers by applying data-driven insights and transitioning the financial services ecosystem into virtual environments.
The enterprise analytics group at U.S. Bank embarked on a journey to create a center of excellence (COE) for application of artificial intelligence to the bank’s problems across various business units. The artificial intelligence developments, specifically in natural language processing (NLP), computer vision, and graph analysis, are experiencing tremendous and rapid advancements in the technology and application areas they impact. To keep up with these advancements, U.S. Bank’s strategy office created the COE, embracing an open innovation model where homegrown solutions for the bank’s problems are juxtaposed and contrasted with commercial offerings. In this presentation, we’ll walk through the development of the center and highlight a specific use case—information extraction from collateralized loan obligations (CLOs)—that showcases the need for internal development, the sensitive nature of the data, ensemble development, and scaling up with GPUs.
The quantitative model is the core of quantitative investment and the key carrier to realize the investment concept. With the continuous development of technology, AI models are becoming more and more widely used in the field of quantitative investment. With its powerful learning, cognition, and reasoning ability, an AI model can develop trading models for different markets and asset types more quickly and has faster self-iteration ability for rapidly changing markets. Through visualization, data mining, logical reasoning, knowledge graphs, and other technologies, the model construction and reasoning process is made transparent, and a certain understandability of the AI model can be presented to satisfy the attribution of returns and the control of investment risks in the process of quantitative investment.
Practitioners of automated, personalized online advertising are often challenged with the attribution problem—an inability to accurately credit a visitor’s conversion to appropriate experiences along their journey. The problem is commonly discussed in the context of ad performance reporting, but misattribution can limit the predictive performance of the ad recommendation engine itself. We present a state-of-the-art personalized recommendation architecture for Capital One advertising, powered by the ALBERT Transformer algorithm commonly employed in NLP tasks (Vaswani et al. 2017, Lan et al. 2019). We recruited the NVIDIA Merlin Transformers4Rec package (de Souza Pereira Moreira et al. 2021) to demonstrate that, for repeat visitors coming to the Capital One homepage, the Transformer outperforms an ensemble-based alternative model currently in production by a wide margin.
金融機関向けの AI および HPC ハードウェア、ソフトウェア、ネットワーキング ソリューションについてご紹介します。
NVIDIA のテクノロジを活用することで、銀行は巧妙化した取引詐欺や ID 詐欺に対抗することができます。詐欺検出の精度が向上し、誤検出が減少し、口座やエンティティ全体で未知のパターンが特定され、AML や KYC の規制への準拠が改善されます。顧客と組織の金融の健全性を守るための最善の方法をご覧ください。
Banks that leverage NVIDIA in their HPC environments are able to run more simulations that incorporate a greater variety and volume of data, yielding the insights needed to successfully manage the business’s risk profile, address regulatory and compliance requirements, and employ resources more efficiently.
デジタルを優先したチャネルにおける AI の成功は、AI アプリケーション、仮想アシスタント、コール センターのチャットボットへの投資を促進しています。NVIDIA AI ソリューションは、通話後の時間を最大 80% 短縮し、通話中の感情やその変化を測定し、コール センター エージェントに次の最適な手順を薦めたりするのに役立ちます。コストを削減しながら、エージェントと顧客の成果と体験を改善します。
NVIDIA のエキスパートがビジネスの可能性を引き出し、イノベーションを解き放つお手伝いをします。