Today’s AI opportunities in financial services

The rise of artificial intelligence (AI) and machine learning (ML) in financial institutions is spurring the growth of applications, with cloud infrastructure easily scaling to handle peak loads and future growth. This accessibility minimizes upfront investments, which is revolutionizing the industry. But challenges such as data privacy and ethical algorithms must be addressed.

As technology advances, AI and ML are poised to play an even greater role in shaping the future of financial services. Current industry deployments — and benefits — are evidence.

5 ways financial institutions can deploy and benefit from AI and ML

1. Cost savings
AI offers significant cost savings and operational efficiency across various domains. For example, AI-driven product recommendations, personalized marketing campaigns, supply chain optimization, customer service automation, fraud detection and demand forecasting. These initiatives result in substantial human capital savings that can be redirected to customer-centric product innovation. AI can optimize on-premises data center cooling and energy usage, reducing operational costs. For businesses that use cloud infrastructure, managed services like AWS Compute Optimizer can reduce operating costs 25–30% through AI- and ML-based workload analytics.

Real-world example: JPMorgan Chase uses ML to improve trading strategies and reduce operational costs. The bank’s advanced AI systems analyze vast amounts of data for market insights, optimize trading decisions and manage risk more effectively. This technological integration enhances efficiency and reduces human error and costs in trading operations.

2. Risk and compliance
To meet stringent regulatory requirements, banks must review and document interactions and correspondence between transaction parties. AI-powered computer vision techniques eliminate human error and automate responses, ensuring compliance with consent orders and matters requiring attention/immediate attention. By analyzing historical data and using ML algorithms, banks can identify and mitigate potential risks proactively. The cloud's scalability and computing power enable real-time processing and analysis of vast data volumes. Organizations can make informed decisions, maintain regulatory compliance, save time and resources, and reduce the risk of fines, business disruption and revenue loss due to non-compliance. Automating compliance helps meet regulatory obligations more effectively and efficiently.

Real-world example: Goldman Sachs developed ML models that can quickly analyze the creditworthiness of potential borrowers, greatly accelerating the decision-making process and reducing risk assessment costs.

3. Data analysis and data quality
ML techniques identify deep insights from big data. Classification, clustering and regression help in the discovery of good versus bad data and can deploy automatic data correction techniques. These emerging use cases stand to be tremendously valuable to financial analysts who depend on high-quality data to derive insights.

Real-world example: Citadel uses advanced ML algorithms in its quantitative strategies to analyze massive volumes of market data. Sophisticated models can detect subtle patterns and correlations that humans might miss, which enhances the accuracy and quality of its trading strategies. This leads to better risk-adjusted returns and a competitive advantage in high-frequency trading environments.

4. Personalization and customer experience
AI revolutionizes personalization and customer experience in the financial services industry. It helps design tailored financial products and advice based on individual customer needs, such as customized investment strategies and personalized recommendations. Robo-advisors use AI to provide automated, data-driven advice on investing and wealth management, predicting customer preferences. By optimizing marketing campaigns using AI, leaders can analyze market conditions and historical data to offer personalized investment strategies. Chatbots and virtual assistants provide real-time, personalized recommendations and financial advice. These AI-powered assistants analyze customer data, such as spending habits and financial goals, to streamline customer service and enhance satisfaction and loyalty. Minimizing human intervention improves efficiency.

Real-world example: Morgan Stanley’s AI-driven Next Best Action System analyzes individual client data to provide personalized investment advice. It uses ML algorithms to understand client preferences, financial goals and risk tolerance, so financial advisors can offer tailor-made suggestions that resonate with clients’ unique situations. It enhances customer satisfaction while improving client engagement and retention.

5. Process automation
Process automation helps banks and financial institutions enhance productivity and reduce operational costs across various business domains. Automating time-consuming tasks like input data validation, document processing and workflow orchestration streamlines operations.

Real-world example: Goldman Sachs automated its trading processes, particularly in its foreign exchange and fixed-income markets. By implementing ML algorithms, the bank automates trades that previously required manual intervention. It speeds up transaction times, increases trade accuracy and efficiency, and reduces slippage and operational costs.

Setting a new standard in banking

These examples demonstrate how major financial institutions are using AI and automation technologies to transform traditional processes. It enhances efficiency, accuracy and the ability to scale operations in capital markets. As this industry continues to evolve under the influence of AI, ML and generative AI, the benefits will manifest across multiple dimensions. These technologies will boost growth and cost efficiencies while significantly enhancing the quality of data analysis, personalizing customer experiences and automating routine processes. JPMorgan Chase, Goldman Sachs and Morgan Stanley are leading examples of how using AI can result in superior risk management, more informed decision-making and increased operational efficiency.

Adopting AI technologies in capital markets is setting a new standard for conducting transactions, managing risks and serving customers. Ongoing advancements promise to unlock further potential, so industry players must continuously innovate and adapt to maintain competitive advantage and meet evolving market demands. In the future, AI will be integral to every facet of capital markets. This transformation will be both profound and essential to the next era of financial services.

Read NTT DATA’s Advances in AI Cloud-Powered Banking, presented by American Banker, to discover how artificial intelligence and machine learning are reshaping banking and capital markets.