AI in financial services: transforming investment, risk and customer experience

September 2025 | SPOTLIGHT | BANKING & FINANCE

Financier Worldwide Magazine

September 2025 Issue

Artificial intelligence (AI) is reshaping the financial services (FS) landscape, touching every aspect of FS business, enhancing efficiency and profitability. Banks and financial institutions (FIs) are increasingly leveraging AI technologies to streamline operations, enhance decision making and deliver personalised services to their customers.

The impact of the introduction of AI to these businesses is substantial. According to Citigroup’s June 2024 report on AI in Finance, AI could inject an additional $170bn into the global banking sector’s profit pool annually through 2028, representing a 9 percent year over year increase.

This boost is expected to help propel the industry’s total profits to an impressive $2 trillion by 2028. FIs are already yielding positive benefits from implementing AI technologies into their business. A July 2024 survey by Bain & Company of 109 US FS firms revealed that companies are experiencing average productivity improvements of 20 percent across various functions, including software development and customer service.

Looking to the future, AI will likely become an integral part of an FI. The industry is expected to witness a significant shift toward automation and data-driven insights, with AI becoming essential to operational strategies.

As FIs continue to innovate, the focus will likely shift toward ethical considerations and compliance with AI regulation, ensuring that AI applications enhance both profitability and customer trust in this rapidly changing landscape.

This article will discuss current use cases and applications of AI in finance, highlighting new innovations which are reshaping the sector.

Customer service

AI is transforming customer service within the FS industry, with AI-powered chatbots and virtual assistants able to provide 24/7 support, handle routine customer queries and assist employees at offering personalised advice, improving customer experience.

Morgan Stanley’s AI @ Morgan Stanley Debrief tool allows financial advisers to capture insights from client conversations, generating tailored recommendations and outlining next steps for clients, thereby improving the advisory process.

Bank of America’s Erica, an AI-powered virtual assistant, has served over 32 million customers and facilitated more than 1 billion interactions. Erica assists with various tasks, including transaction searches, bill payments and providing personalised financial guidance based on user behaviour. Its capabilities have evolved to include intelligent call routing, ensuring clients receive specialised support when needed.

Capital One’s Eno serves as a multichannel solution that helps customers manage their accounts by checking balances, tracking purchases and detecting potential fraud in real time. Eno proactively alerts users about suspicious activities and can lock cards if necessary.

Fraud detection and prevention

AI in fraud detection and prevention within the FS industry is enhancing security and helping companies reduce losses. The industry has been implementing advanced AI systems to monitor transactions 24/7 to detect unusual behaviours instantly and subsequently mitigate any risk.

American Express uses an enhanced early warning system that processes approximately $1.2 trillion in annual transactions. This AI system employs machine learning (ML) to detect patterns and anomalies in real time, adjusting to emerging fraud tactics effectively.

Visa leverages hundreds of AI models across its services, including Visa advanced authorisation, which evaluates over 500 risk attributes in milliseconds to generate real time risk scores for transactions. This proactive approach has enabled Visa to prevent more than $700m in fraud within Australian businesses.

Mastercard’s Decision Intelligence Pro, a generative AI (genAI) consumer protection tool, assesses transaction risks by analysing relationships between entities. It scans 1 trillion data points to predict the likelihood of transactions being genuine or fraudulent, ensuring swift identification of suspicious activities.

Investment management

AI is enabling faster, more informed investment decisions, improving operational efficiency and enhancing client engagement in the investment management sector.

Blackstone uses AI in its investment management to enhance decision making, accelerate due diligence and optimise portfolio performance. The investment teams apply AI-driven models to analyse data, enabling faster and more precise financial modelling.

AI tools help Blackstone identify attractive investments and also avoid risks, such as rejecting targets in sectors vulnerable to AI disruption. In operational terms, Blackstone uses AI for predictive analytics, risk assessment and automation to improve efficiency and drive portfolio company growth.

For example, in its real estate division, AI analyses billions of data points to forecast market demand and optimise property acquisitions, significantly reducing error rates and improving investment outcomes.

Goldman Sachs has introduced a genAI tool that enhances developer efficiency by approximately 20 percent, allowing employees to build custom applications for investment banking, such as a copilot assistant that analyses extensive public and proprietary documents for insights.

Regulatory compliance and reporting

AI can help to transform regulatory compliance and reporting in the FS industry by automating monitoring processes and identifying potential compliance issues.

Deutsche Bank employs named entity recognition to extract critical information from client documents, such as invoices and transport documents. This data is essential for conducting sanctions and embargo checks, enabling Deutsche Bank to be compliant with its regulatory reporting requirements.

HSBC has partnered with Google to develop a dynamic risk assessment system, which screens vast amounts of customer data against publicly available information to detect suspicious activities. This AI-driven approach has increased the detection of financial crimes by two to four times while reducing false positives by 60 percent, streamlining investigations.

Predictive analytics for customer insights

Companies are implementing AI-driven predictive analytics to analyse historical data to forecast customer behaviour, enabling FIs to tailor products and services effectively. By leveraging these insights, companies can enhance customer satisfaction, improve retention rates and drive overall business growth in a competitive market.

JPMorgan has invested in genAI technologies, revealing that an AI solution designed to nudge customers who abandon product applications has resulted in a 10 to 20 percent increase in completion rates. This application highlights AI’s role in improving customer engagement and retention.

Similarly, ING employs predictive analytics and ML to enhance customer interactions by anticipating their needs and offering relevant banking products, thereby improving cross-selling effectiveness. The bank also optimises cash logistics by predicting ATM cash requirements, ensuring sufficient availability without excess.

Operational efficiency

Like many industries, FS companies are looking to AI to significantly enhance operational efficiency. By streamlining back-office operations such as data entry, document processing and account management, companies can achieve operational efficiencies.

BNP Paribas has entered a multi-year partnership with Mistral AI, enabling access to current and future AI commercial models across all business lines. This collaboration focuses on developing applications in customer support, sales and IT, enhancing service delivery and operational processes.

The integration of Mistral AI’s large language models will facilitate the creation of high-quality virtual assistants and streamline end to end processes, ultimately improving client support and operational efficiency.

Loan processing automation

The FS industry has been using AI in attempts to automate and streamline any elements of the loan application process to achieve further efficiencies.

ANZ Bank employs AI-driven systems that analyse extensive datasets to assess creditworthiness more effectively, improving the accuracy of credit assessments. The bank has introduced the HLQ platform, which automates tasks such as document ingestion, image processing and data extraction, significantly reducing manual handling and enabling faster loan approvals.

Perpetual is leveraging Lakeba’s DoxAI platform (powered by Microsoft’s AI technology) to revolutionise its loan document verification process. The AI system reduces document processing time and assists in verifying critical loan documentation such as land title registries, certificates of title and mortgage documents.

The technology ensures that all loan contracts are accurate and enforceable by catching errors such as spelling mistakes that could invalidate documents. This AI is specifically focused on document verification rather than credit quality assessment.

As FIs continue to invest in and develop AI capabilities, the technology’s impact on efficiency, customer service and profitability will only grow. As the industry evolves, AI will become not just a competitive advantage but a fundamental requirement for delivering the efficient, personalised and secure FS that customers increasingly demand.

Nick Abrahams is the global co-leader of the digital transformation practice and Maxwell Crawford is an associate at Norton Rose Fulbright. Mr Abrahams can be contacted on +61 408 673 648 or by email: nick.abrahams@nortonrosefulbright.com.

Nick Abrahams and Maxwell Crawford

Norton Rose Fulbright