The future of AI in financial services

The financial services industry continues to make notable progress thanks to Generation AI. In the last few months alone, we have seen the launch of several AI-powered solutions, including Morgan StanleyThe tool that summarizes video meetings and generates follow-up emails, as well as JPMorganChase AI Assistant LLM Suite. Others, like BNP Paribas And TD Bankannounced significant partnerships with gen AI model creators.

Industry spending on AI is should increase from $35 billion in 2023 to $97 billion by 2027, representing a compound annual growth rate of 29%. The largest players are investing aggressively in developing their AI infrastructure and scaling use cases to drive more value. Daniel Pinto, President and Chief Operating Officer of JPMC, recently estimated that Generation AI use cases in banking could bring in up to $2 billion.

The question now is what will financial services do next and how soon will they apply AI across their organizations and more broadly to customers.

Based on my conversations with senior leaders and through Accenture’s work leading the FinTech Innovation Lab, I predict that we will see the AI ​​generation evolve over two time horizons: which is happening now – which will see adoption rapid availability of AI-assisted tools and technologies for unstructured data processing and data collection – and one further into the future, with more sophisticated applications as infrastructure, modeling and regulatory considerations are progressing.

The most immediate time horizon sees financial services firms focusing on four areas:

1. AI co-pilots – Co-pilots working alongside employees will streamline workflows and provide new insights, leading to significant productivity improvements. Citizens Bank for example, expects to see up to 20% efficiency gains from AI generation as it automates activities such as coding, customer service and fraud detection. In the future, these co-pilots could adapt investment strategies in real time or predict market trends, helping to strengthen the competitive advantage of financial services companies and deliver differentiated results to clients.

2. AIways-on AI Web Crawlers – These web crawlers continuously collect and analyze data from various web sources and public records. They can track financial news and market movements in real time while detecting subtle changes in consumer confidence across social media platforms, alerting banks to potential risks and opportunities while enabling proactive management.

3. Automating unstructured data tasks – Gen AI systems will process and analyze unstructured data (emails, documents and multimedia content), transforming it into structured, actionable information and reducing the time traditionally required for data management. This change will allow employees to focus on higher value-added tasks, such as strategic decision-making and creative problem solving. We have already seen financial services clients deploy a large number of highly targeted solutions generative AI agents which can automate processes with little or no ongoing human intervention.

4. Hyper-personalization – Banks and others are leveraging AI and non-financial data to better create and target highly personalized offers. This shifts the FS paradigm from a responsive service to a truly intuitive and responsive service. Take the example of Klarna’s AI assistant. It now handles two-thirds of customer service interactions and has reduced marketing spend by 25%. Rather than responding reactively when customers have a request or problem, the company could potentially think ahead and contact customers proactively before they even realize something is wrong.

Further into the future – risk management and new propositions via synthetic data

As technology and infrastructure advances to enable more sophisticated models with larger data sets at a lower cost than today – and as regulatory policy takes shape – we expect to see Financial services are deepening the use of AI generation to address risk management and provide a richer customer experience.

Generation AI could play a vital role in risk management through the creation and use of synthetic data, which will become essential to improve the accuracy of predictive models, particularly for fraud detection, and help financial institutions to proactively guard against threats and make more informed decisions. A European neobank, bunqis already using generative AI to improve the training speed of its automated transaction monitoring system that detects fraud and money laundering.

More broadly, generation AI could transform compliance and security measures, enabling businesses to more effectively meet regulatory requirements while reducing the costs and efforts required to combat financial fraud and manage risk.

Synthetic data could also lead to better customer experiences through the design and testing of new propositions, such as loans or investments. Banks can use the data to simulate how customers might react to these new products or other scenarios, such as a financial recession. Some financial services companies are already testing tools in this area, but it may take some time before they are truly enterprise-ready.

Fintechs will help democratize the AI ​​generation

Fintechs remain at the forefront of harnessing the AI ​​generation and many of their use cases and solutions are impacting financial services. For example, Synthesia uses an AI platform to create high-quality video and voiceover content tailored to financial services, while Deriskly provides AI software aimed at optimizing financial promotions and communications compliance. Others include Reality Defender, whose deepfake detection platform helps banks, insurers and governments detect AI-generated content at scale, and Hyperplane, a data intelligence platform that enables institutions financial to develop personalized experiences and predictive models using large proprietary language models (it was recently acquired). by Nubank).

Many fintechs will play an enabling role in helping to democratize gen AI capabilities for mid-sized and smaller financial institutions, enabling these companies to leverage gen AI in ways that are currently only available for the biggest players in the financial sector in the world.

Financial services have made significant progress in adopting Generation AI over the past two years. Although the focus to date has been on efficiency and cost optimization, many FS CIOs are keen to drive revenue growth. To do this, they will need to work closely with the business to think about how Generation AI can lead to new ways of working, new products and new capabilities that can help accelerate revenue. The future of AI in financial services looks bright and it will be interesting to see where companies head next.